On the Agenda: Extending LabVIEW with NATS

I had the opportunity to present Extending LabVIEW with NATS, where I discussed how their pub/sub messaging model can extend LabVIEW test systems beyond a single-language, single-executable architecture. The core idea is that monolithic test applications can be bulky and hard to manage, while tools like NATS allow for more modular software design. Check out the code in the LabVIEW Toolkit.

Sessions That Stood Out

There were many presenters and panels that stood out this year. Here are a few that stuck with me. You can also see these sessions as they’re uploaded to GDevCon’s YouTube channel.

• Open-source Wrapping: The Gift That Keeps on Giving (Chris Davis, Amentum): A look at wrapping open-source C libraries for LabVIEW for Redis and Kafka. These are great complementary tools to NATS.
• Shift-Left: Building Secure LabVIEW Systems from Day One (Sarah Zalusky, JKI): A practical look at security in LabVIEW architectures.
• MALpocalypse (Michael Klessens, Intel): A candid account of when measurement abstraction layers stop working at scale with validation constraints.
• Shaping the Future: AI and the Evolution of LabVIEW (Elijah Kerry, NI): An early look at Nigel, NI’s AI coding assistant for LabVIEW. Plus, lots of CI/CD tooling with LabVIEW containers.
• Our Build and Deployment Process: Using CI/CD and SystemLink (Sarah Morales, SRAM): A walkthrough of SRAM’s g-cli-based GitLab pipelines. SRAM’s approach validated the work DMC has done to automate our own pipelines with similar tooling.

Catching Up with Partners

Beyond the sessions, GDevCon is a great chance to connect with DMC’s partners. We participated in hardware and software demos from Pickering, Beckhoff, and NI. Most importantly, our partners give us insight into their project roadmaps. This allows us to be trusted advisors to our clients, knowing what to avoid for obsolescence and where to leverage new tools.

Final Takeaway

GDevCon N.A. is always a great place to learn what the test and measurement community is building, breaking, and fixing in production. The conference also gives us the opportunity to catch up with our hardware partners, and this year’s event did not disappoint!

We cannot wait to see what next year will bring.

The post What We Learned at GDevCon N.A. 2026: AI, CI/CD, and the Future of LabVIEW appeared first on DMC, Inc..

The practical answer is not to rewrite everything. Instead, a split-runtime architecture allows teams to keep LabVIEW where it shines and move high-change computation into Python services connected over TCP.

This Python integration approach supports a wide range of industrial use cases, including:

• Vision inference and image analysis.
• Statistical quality calculations.
• Optimization and scheduling support.
• Report generation and data enrichment.
• AI-assisted engineering tools.

The end result is an architecture in which LabVIEW remains the orchestration and operator-layer interface for complex hardware systems, while Python hosts features that benefit from rapid iteration and rich libraries. One particularly salient reason to integrate Python in this way is that it integrates cleanly with the latest AI coding agents and tooling, supporting accelerated development.

Why TCP Is the Right Approach

There are multiple ways to connect runtimes, many of which DMC has explored across numerous domains. For example, we’ve worked with MQTT, RabbitMQ, NATS, Redis, as well as other messaging tools and protocols. In this tutorial, we use a simple request-reply pattern. Here, TCP is the best fit since it is:

• Language neutral: Both LabVIEW and Python support sockets natively.
• Low overhead: Efficient for frequent request/response cycles without added protocol information.
• Process isolated: UI/control failures and compute-service failures are easier to contain. No need to manage the lifecycle of a binary outside of two processes communicating with each other.
• Flexible in deployment: Run components on the same machine or distribute later without changing the contract.

LabVIEW Python TCP Reference Architecture

A reusable version of this request-reply pattern looks like this:

1. LabVIEW gathers the request parameters and sends a typed request.
2. The Python service receives the request and executes the requested logic.
3. Python returns typed results plus optional status and timing metadata.
4. LabVIEW processes the reply body as needed.

This same structural loop applies whether the compute workload is vision, forecasting, anomaly scoring, or rule evaluation.

Socket Wrappers for LabVIEW and Python Communication

This is a minimal implementation of a socket wrapper that serves as a skeleton you can use to add type safety, error checking, and other safeguards to make your code more robust. The wire format is a fixed header to ensure that reads are deterministic:

• LabVIEW to Python: 1 byte message type + 4 byte big-endian payload size + payload bytes.
• Python to LabVIEW: 4-byte big-endian payload size + payload bytes, since the message type defines the response.

import json
from enum import Enum


class Lv2PyMessageType(Enum):
    ANALYZE_ANIMAL = 0
    FETCH_WEATHER = 1
    EXIT = 2


class LVSocket:
    def __init__(self, sock):
        self.sock = sock

    def await_message(self) -> tuple[Lv2PyMessageType, bytes]:
        msg_type = Lv2PyMessageType(int.from_bytes(self._recv_exact(1), "big"))
        size = int.from_bytes(self._recv_exact(4), "big")
        return msg_type, self._recv_exact(size)

    def send_message(self, payload: bytes):
        self.sock.sendall(len(payload).to_bytes(4, "big"))
        self.sock.sendall(payload)

    def _recv_exact(self, n: int) -> bytes:
        out = b""
        while len(out) < n:
            chunk = self.sock.recv(n - len(out))
            if not chunk:
                raise ConnectionError("Socket closed")
            out += chunk
        return out

import json
from enum import Enum


class Lv2PyMessageType(Enum):
    ANALYZE_ANIMAL = 0
    FETCH_WEATHER = 1
    EXIT = 2


class LVSocket:
    def __init__(self, sock):
        self.sock = sock

    def await_message(self) -> tuple[Lv2PyMessageType, bytes]:
        msg_type = Lv2PyMessageType(int.from_bytes(self._recv_exact(1), "big"))
        size = int.from_bytes(self._recv_exact(4), "big")
        return msg_type, self._recv_exact(size)

    def send_message(self, payload: bytes):
        self.sock.sendall(len(payload).to_bytes(4, "big"))
        self.sock.sendall(payload)

    def _recv_exact(self, n: int) -> bytes:
        out = b""
        while len(out) < n:
            chunk = self.sock.recv(n - len(out))
            if not chunk:
                raise ConnectionError("Socket closed")
            out += chunk
        return out

Service Loops

The service itself is just a while True loop: wait for a typed message, branch on it, compute, and send the reply. This pattern keeps the service logic straightforward and maintainable.

import socket

PORT = 12345

def analyze_animal(payload):
    # parse the payload for this message and generate your response here
    ...

def fetch_weather(payload):
    # parse the payload for this message and generate your response here
    ...

def serve(sock):
    lv = LVSocket(sock)
    while True:
        msg_type, payload = lv.await_message()
        if msg_type == Lv2PyMessageType.ANALYZE_ANIMAL:
            result = analyze_animal(payload)
            lv.send_message(json.dumps(result).encode("utf-8"))
        elif msg_type == Lv2PyMessageType.FETCH_WEATHER:
            result = fetch_weather(payload)
            lv.send_message(json.dumps(result).encode("utf-8"))
        elif msg_type == Lv2PyMessageType.EXIT:
            break

if __name__ == "__main__":
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(("localhost", PORT))
        s.listen()
        print("Waiting for LabVIEW to connect...")
        conn, addr = s.accept()
        print(f"Connected by {addr}")
        with conn:
            serve(conn)

import socket

PORT = 12345

def analyze_animal(payload):
    # parse the payload for this message and generate your response here
    ...

def fetch_weather(payload):
    # parse the payload for this message and generate your response here
    ...

def serve(sock):
    lv = LVSocket(sock)
    while True:
        msg_type, payload = lv.await_message()
        if msg_type == Lv2PyMessageType.ANALYZE_ANIMAL:
            result = analyze_animal(payload)
            lv.send_message(json.dumps(result).encode("utf-8"))
        elif msg_type == Lv2PyMessageType.FETCH_WEATHER:
            result = fetch_weather(payload)
            lv.send_message(json.dumps(result).encode("utf-8"))
        elif msg_type == Lv2PyMessageType.EXIT:
            break

if __name__ == "__main__":
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(("localhost", PORT))
        s.listen()
        print("Waiting for LabVIEW to connect...")
        conn, addr = s.accept()
        print(f"Connected by {addr}")
        with conn:
            serve(conn)

We can handle these messages from LabVIEW using the built-in TCP functions and pass a wide range of data in the message body.

Lifecycle Management

The next step is to start and stop our Python script along with our LabVIEW code while gracefully handling errors and exits. We can use the System Exec.vi function to accomplish this, as shown in the snippet below.

We can also safely manage the lifecycle of the Python script by, in addition to the EXIT message type, adding handling for socket disconnections by gracefully exiting the Python script if the socket is closed from the LabVIEW side. With this approach, operators can restart Python service, for any reason, by simply stopping and restarting the LabVIEW application, without needing to manage the Python process separately.

Packaging for Deployment

Finally, to deploy this architecture, package the Python script so it can be distributed alongside the LabVIEW build for client systems. This can be done with tools like PyInstaller, which creates a standalone executable from your Python script. You can then conditionally call this executable from LabVIEW using the System Exec.vi as shown below. This approach simplifies testing in development and deployment to production without needing to manage Python environments on the target machines.

Reliability Requirements for Production

Industrial systems need graceful behavior under real-world conditions, including dropped connections, overloaded services, and operator restarts.

Baseline reliability checklist includes:

• Request timeout and retry policy.
• Reconnect strategy with back-off.
• Health check or heartbeat message.
• Structured logs on both sides.
• Fail-safe startup and shutdown behavior.
• Logging of exceptions and edge cases for postmortem analysis.

These patterns are essential for production readiness, and they also build trust with operations teams by ensuring the system won’t fail silently or require manual intervention.

Why Integrating Python Matters for AI Adoption in LabVIEW Teams

This architecture also enables AI adoption. It allows teams to build key software components outside LabVIEW, where AI-assisted development can be faster due to:

• Rapid prototyping of analysis components.
• Quick generation of helper tools and scripts.
• Faster experimentation with algorithms and thresholds.
• Simplified profiling and optimization workflows.

LabVIEW still governs system behavior; Python becomes the iteration engine. This division allows teams to leverage AI productively while preserving control-system rigor.

Key Takeaways

• LabVIEW + Python over TCP is a reusable and extensible architecture pattern across many industrial use cases.
• Keep LabVIEW for orchestration and UI; externalize high-change compute to Python.
• The pattern creates a practical path for AI-assisted software development in industrial systems.

If you want to modernize an existing LabVIEW application, TCP-connected Python services are one of the highest-leverage places to start.

The post LabVIEW + Python Over TCP: A Reusable Architecture Pattern for Industrial Software appeared first on DMC, Inc..

There are, of course, other types of hardware that someone might want to acquire data from. Our clients often need us to integrate all kinds of equipment based on their technical and budget needs from a specialized hipot meter to a general-purpose DMM. One such client needed us to integrate a Raspberry Pi, which was collecting data from a set of sensors, with a LabVIEW app that was already collecting data from NI hardware.

Raspberry Pis are pretty popular among hobbyists and hardware engineers alike for prototyping and actual production use since they’re inexpensive, fairly robust, and have a broad ecosystem of products that work nicely with them. Their GPIO (General Purpose Input/Output) pins make it easy to interface with hardware over different low-level protocols like SPI or I2C. The boards also come with WiFi, Bluetooth, Ethernet, and USB if you need to connect to something over a higher-level interface. This brings us to the question of how to use a Raspberry Pi in a way that is fast and reliable from a LabVIEW application running on a separate PC—effectively using it as a DAQ device.

The Easy Way: FastAPI

Let’s begin with a simple approach—we can set up a minimal REST API service on the Raspberry Pi using FastAPI that, when queried, creates and returns your measurement.

Setting Up the API

FastAPI is a modern Python web framework that makes it simple to create REST APIs. On your Raspberry Pi, you can install it along with a production server like uvicorn:

pip install fastapi uvicorn

Then create a simple API endpoint that reads from your sensor:

from fastapi import FastAPI
import time

app = FastAPI()

@app.get("/sensor/read")
def read_sensor():
    # Your sensor reading code here
    # For example, reading from an I2C device
    value = read_i2c_sensor()
    timestamp = time.time()

    return {
        "value": value,
        "timestamp": timestamp
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

from fastapi import FastAPI
import time

app = FastAPI()

@app.get("/sensor/read")
def read_sensor():
    # Your sensor reading code here
    # For example, reading from an I2C device
    value = read_i2c_sensor()
    timestamp = time.time()

    return {
        "value": value,
        "timestamp": timestamp
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

From LabVIEW, you can use the HTTP Client VIs to make GET requests to <http://your-pi-ip:8000/sensor/read> and parse the JSON response to extract your sensor data.

The Issues

While this approach is straightforward and gets you up and running quickly, it has some significant limitations for serious data acquisition:

Not Truly Time-Series: Each request creates a new measurement on demand. If you’re trying to capture a continuous stream of data, you’ll miss all the samples between requests. This is fine for slow-changing values like temperature readings every few seconds, but inadequate for high-speed data acquisition.

Request Overhead: Every HTTP request involves substantial overhead, TCP handshaking, HTTP headers, JSON serialization/deserialization, and network latency. If you need to sample at high rates (say, 1000 samples per second), making 1000 individual HTTP requests per second is inefficient and will likely introduce timing jitter and missed samples.

For applications where you need occasional readings or the data changes slowly, this approach works great. But for continuous, high-speed data acquisition, we need something better.

The High-Speed Way: Redis Streams

Redis is an in-memory data structure store that’s fast and supports various data types, including streams – perfect for time-series data collection. The architecture here is more sophisticated but provides much better performance:

In production, you may implement the sensor loop in C or another lower-level language for tighter timing, but Python is a good way to understand and prototype the architecture.

1. A Python process on the Raspberry Pi continuously reads sensors and writes to a Redis stream.
2. Redis stores the data in memory as a time-ordered stream.
3. LabVIEW periodically reads from the stream, getting batches of new data.
4. Webdis provides an HTTP interface to Redis, making it accessible from LabVIEW.

Setting Up Redis

First, install Redis on your Raspberry Pi:

sudo apt-get install redis-server

Install the Python Redis client as well:

pip install redis

Configure Redis to start on boot and ensure it’s listening on the network if your LabVIEW application is on a different machine:

sudo systemctl enable redis-server
sudo systemctl start redis-server

The Data Collection Process

Create a Python script that continuously reads your sensors and writes to a Redis stream:

import time
import json
import redis

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def collect_data():
    interval = 0.001  # 1ms = 1000 Hz sampling
    cycle_time = time.time()
    while True:

        # Read your sensor
        sensor_value = read_i2c_sensor()

        # Add to Redis stream
        r.xadd('sensor_stream', {
            'value': sensor_value,
            'timestamp': cycle_time
        })

        # Delay enough to start next loop at the interval rate
        time_to_next_cycle = cycle_time + interval - time.time()
        sleep_time = max(0, time_to_next_cycle)
        time.sleep(sleep_time)
        cycle_time += interval

if __name__ == "__main__":
    collect_data()

import time
import json
import redis

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def collect_data():
    interval = 0.001  # 1ms = 1000 Hz sampling
    cycle_time = time.time()
    while True:

        # Read your sensor
        sensor_value = read_i2c_sensor()

        # Add to Redis stream
        r.xadd('sensor_stream', {
            'value': sensor_value,
            'timestamp': cycle_time
        })

        # Delay enough to start next loop at the interval rate
        time_to_next_cycle = cycle_time + interval - time.time()
        sleep_time = max(0, time_to_next_cycle)
        time.sleep(sleep_time)
        cycle_time += interval

if __name__ == "__main__":
    collect_data()

This process runs independently, continuously writing data to the stream regardless of whether anyone is reading it. Redis handles the buffering and ensures data isn’t lost.

Accessing Redis from LabVIEW with Webdis

Webdis is a simple web server that provides an HTTP interface to Redis. Install it on your Raspberry Pi:

git clone https://github.com/nicolasff/webdis.git
cd webdis
make
./webdis &

Now you can access Redis commands via HTTP. From LabVIEW, you can read from the stream using the HTTP Client VIs to make requests like:

http://your-pi-ip:7379/XREAD/COUNT/<count>/STREAMS/sensor_stream/$

The $ special ID means “only entries added after this request begins” – it’s useful for your initial read when you want to ignore old buffered samples and start with future data. For subsequent reads to get all new data since your last read, you’ll need to use the actual stream ID you received from the previous read instead of $.

The <count> value represents the maximum number of entries to return. Based on your sample rate and how often you want to read buffered data from the device, you will want to modify this value to keep up with data production on the Raspberry Pi.

LabVIEW Implementation

In your LabVIEW VI:

1. Store the last stream ID you read (starting with “$”).
2. Periodically poll the stream using XREAD with your last ID.
3. Parse the JSON response to extract the sensor values.
4. Update your last stream ID for the next request.
5. Process the batch of samples.

This approach dramatically reduces overhead; instead of 1000 requests per second for 1000 samples, you might make 10 requests per second and get 100 samples each time.

Advantages

• Continuous Collection: The Python process collects data continuously without gaps.
• Buffering: Redis buffers recent data in memory, so if LabVIEW is briefly busy, samples remain available.
• Batch Processing: LabVIEW can read multiple samples per request, reducing overhead.

Adding Time Synchronization

When combining data from multiple sources (like your NI DAQ and Raspberry Pi), timestamp synchronization becomes critical. The Raspberry Pi’s clock and your Windows PC’s clock will drift apart over time, making it difficult to properly align and correlate data.

There are several approaches to tackle this problem:

Measuring Clock Offset: Create a calibration routine in which LabVIEW requests the current time from the Pi and measures the round-trip time to calculate the clock offset. Apply this offset to align timestamps. Keep in mind that different hardware clocks will experience drift and you will need to regularly re-calibrate to account for this.

Network Time Protocol (NTP): Configure both systems to sync with the same NTP server. This gets you in the ballpark but won’t give you perfect alignment due to network delays and update intervals.

Conclusion

Integrating a Raspberry Pi as a data acquisition device in your LabVIEW application enables cost-effective, flexible hardware integration. Your chosen approach depends on your requirements.

For our client’s application, we used the Redis approach with time synchronization to integrate their Raspberry Pi sensors with their LabVIEW system, achieving reliable 1 kHz data collection aligned with their NI DAQ data.

The post How to Use Raspberry Pi as a DAQ Device in LabVIEW appeared first on DMC, Inc..

Here are three practical ways DMC has used AI and Python to optimize a real-time inspection system and deliver real results to our clients.

1. Profile Your Pipeline to Find the Bottlenecks

As you move from implementation to production, the slower parts of your code can become painful while remaining hidden. In fact, slow lines of code can show up in surprising places and, depending on how your pipeline is structured, have an outsized effect on your speed. The solution is to bring a magnifying glass to your code by profiling it.

I’ve been a heavy user of Jupyter notebooks inside VS Code, which allows me to use the AI coding agent of my choice to easily set up and run experiments against my inspection pipeline to understand how it runs in practice against specific examples and, more relevantly, how long each section of the pipeline takes to run.

For example, one recent system DMC worked on involved belt masking. A client needed to inspect yellow tortillas moving down a blue conveyor belt, so we had to isolate each tortilla on each frame, separating it from the background for later steps in our analysis. The naive-but-useful approach we started with computed a full-resolution color distance, each pixel relative to the average color of the belt. This gave us a reference for what a functionally correct approach looks like, but profiling the code in conversation with AI showed us that it took up the majority of its time analyzing each frame.

2. Redesign Expensive Metrics to be Computationally Cheaper

Once you know which stage is slow, the next lever is the algorithms inside it. Many vision metrics have a naive implementation that’s accurate but expensive, and a smarter implementation that’s nearly as accurate and far faster.

After profiling the inspection system, we looked for ways to optimize it. A faster approach involved shrinking the frame by 50% on each side to reduce the number of computations by 75%. Comparing against the full-frame approach, we saw minimal reduction in accuracy. Additionally, by computing distances, we were performing a square root operation before comparing the distance to a threshold parameter; we could ditch that operation by squaring the threshold parameter and then comparing the squared distance, which sped up the process even further with no reduction in accuracy.

Eventually, we switched from checking RGB color distance to an axis-aligned range, which had similar accuracy while drastically reducing the computational load per frame. AI was vital here for quickly brainstorming and drafting these alternative formulations so we could compare their performance and accuracy.

3. Use Synthetic Data to Stress-Test Thresholds and Edge Cases

Optimization isn’t only about speed; it’s also about ensuring the system remains correct under pressure and across the full range of inputs. It’s hard to verify with only a handful of real images, which is often the case at the pre-deployment stage of a project.

Synthetic data can fill this gap. By generating test subjects across the entire pass/fail spectrum, including rare failure modes, you can stress-test thresholds and confirm the optimized pipeline still behaves correctly on edge cases.

In our example of the tortilla inspection project, we used a Python script to programmatically generate AI images of tortillas with different levels and types of defects by varying the prompts. For example, each tortilla prompt was given a probability of having a prompt addition specifying a torn edge or a burn mark. After generating 100 tortillas, we used another script to create simulated footage of these tortillas moving down a looping belt texture, with added random noise and motion blur, which we then used to verify our pipeline’s capabilities before we had any real footage to work with. These efforts allowed us to deploy our code into production quickly for our client.

Conclusion

These threThese three practices work together to create a more efficient and reliable system:

• Profiling identifies where to focus your optimization efforts.
• Metric redesign speeds up slow stages exposed by your profiling.
• Synthetic data ensures your system remains accurate across edge cases.

Each of these stages has always been essential to vision inspection, but AI and modern coding tools have made them faster and easier to implement. Additionally, unlike certain uses of AI that have greater exposure to risk, like code implementation, using AI here is less likely to cause major faults in your system. That said, AI should be an accelerator, not a replacement for engineering judgment. It is essential to verify that AI-generated code is correct and aligned with system requirements.

Ultimately, the impact of these practices is clear. By combining these approaches, you can confidently improve the speed and reliability of your real-time vision inspection pipeline.

The post 3 Ways AI Improves Real-Time Vision Inspection with Python appeared first on DMC, Inc..

This article walks through the fundamentals of FAT, how it differs from other testing phases, and how to prepare your system and your customer for a smooth buy-off.

Background

Before we dive into the topic of Factory Acceptance Testing, I want to outline an example of the Project Process DMC follows. After thousands of projects, DMC has found that a phased approach is the best way to make a system production-ready.

• Requirements & Specifications – DMC works collaboratively with the client to determine system requirements. This phase often culminates in a “System Functional Review.”
• Preliminary Design – DMC defines a Preliminary Design as a ~20% complete design, where the high-level architecture has been mapped out and identified risks have been evaluated to the point where a plan is in place. This phase often culminates in a “Preliminary Design Review.”
• Comprehensive Design – DMC defines a Comprehensive Design as an ~80% complete design, with the remaining tasks to be finalized during the Development & Fabrication phases. (i.e., what nuts and bolts will be used to mount the hardware). This phase often culminates in a “Critical Design Review.”
• Development – The software development portion of the implementation phase, where any custom software is created and tested.
• Fabrication – The hardware build portion of the implementation phase, where any hardware is assembled into a complete system.
• Factory Acceptance Testing – The point in the project process where DMC validates the system prior to shipping it to the client site.
• Site Acceptance Testing – The final validation testing performed after the system arrives at the client site.
• Training & Support – After delivery and acceptance, DMC can provide training on using or extending the system, as well as support for troubleshooting and future improvements.

Learn more about DMC’s Project Management that makes this flow happen.

Project Specific Customizations

At DMC, we understand that not all projects are the same, so the Project Phases are customized on an individual project level.

• If a customer already has a well-defined Requirement & Specification document, we can jump straight into the Design phases.
• For small projects, we might combine “Requirement & Specification, Preliminary Design, and Comprehensive Design” into a single “Design” phase.
• For software-only projects, we remove the Fabrication phase and may combine “Factory Acceptance Testing and Site Acceptance Testing” into a single “Testing” phase.

Now that we have a better understanding of DMC’s typical project process, let’s talk about how we can prepare for a successful Factory Acceptance Testing phase.

FAT vs. SAT vs. ATP: What’s the Difference?

Before we dive into any details, let’s define terms that will be used in this blog.

Factory Acceptance Testing (FAT)

FAT is testing at the fabrication location to verify that the system meets the agreed-upon requirements before shipment. FAT focuses on testing that can be performed without the systems or hardware only available at the customer’s facility.

Site Acceptance Testing (SAT)

SAT is a test conducted after the system has been installed at the customer’s facility and is the first opportunity to test with systems or hardware only available at the site. SAT will often repeat tests performed during FAT to ensure the system wasn’t damaged during transport.

Acceptance Test Plan (ATP)

The ATP is the test plan used to execute FAT or SAT. It defines what is tested, how it is tested, and what constitutes a pass or fail. A well-written ATP is essential for efficient execution and clear customer sign-off.

What Does a Successful FAT Look Like?

FAT is successful when all parties are in agreement that the system meets its requirements and is ready for shipment to its final location.

How Do We Ensure FAT Is Successful?

Preparation is critical to ensuring that FAT is an efficient and productive use of time. We will dive a little deeper into how we can maximize testing prior to FAT, define participation and roles during FAT, and how to develop the ATP.

What Testing Should Occur Before FAT?

FAT should not be the first time testing occurs during the project process. Systems and code should be tested as they are developed. Testing strategy is a huge topic and will not be discussed further in this blog.

Who Participates During FAT?

At DMC, customer involvement during the FAT process is highly encouraged. At the end of the day, the customer will own and operate the system, so DMC strives to include the customer in as many aspects of our project process as possible. Some benefits of customer involvement during FAT are:

• A chance to make tweaks to the system without major cost or schedule impacts to the project.
• An opportunity to receive hands-on training with the team that developed the system.
• Provides an opportunity to identify gaps or misunderstandings in requirements.

Regardless of whether or not the customer attends FAT, they should be involved in developing the ATP. Ideally, either DMC or the client would write the acceptance test plan, while the other reviews and approves.

Typically, the customer should be responsible for writing the acceptance test plan to ensure complete test coverage and feel comfortable accepting the system at the end of the day. However, DMC engineers are experts and will often develop the ATP.

How Do You Develop an ATP?

Each item in an ATP should be clear, repeatable, and objective. In order to achieve this goal, each test should have the following core elements at a minimum:

• Test Procedure: Step-by-step instructions
• Acceptance Criteria: Objective pass/fail conditions
• Observed Results: What actually occurred
• Result Files: Logs, screenshots, or data files as evidence

Depending on your specific requirements, you can also consider tracking the Test Type, Test Operator, Test Date, and the approval status of Individual Results. If you’re looking for more information on why you might need an ATP, check out this blog.

Does the Order of ATP Execution Matter?

The order of individual ATP items is crucial for reducing the amount of time spent troubleshooting and debugging the system. The highest system-level impact should come first, followed by more narrowly scoped items. This narrows down potential causes of issues, reducing the overall time spent troubleshooting and debugging the system.

As you test systems and subsystems, start with the simplest components and add more complex capabilities sequentially.

A high-level concept for ATP Execution Order is as follows:

Conclusion

Factory Acceptance Testing is more than a checkbox. By involving the customer early, planning FAT thoughtfully, and executing against a clear, well-structured ATP, teams can streamline buy-off, minimize rework, and set the project up for long-term success.

The post Factory Acceptance Testing 101: Preparing for Customer Buy-Off appeared first on DMC, Inc..

• The fixture is finally working. The measurements are stable. The limits are all set.
• The PLC handshakes don’t fault out. Operators are trained, and parts are flowing.

Everyone celebrates because they should. Getting a new EOLT station deployed is hard work. But then something predictable happens: All the long-term improvement ideas, especially around data management, get pushed to “Phase 2.”

“We’ll come back to the data piece later.”

“Let’s get production stabilized first.”

“We’ll handle reporting in the next budget cycle.”

Except… “later” almost never comes. Once the line is running and hitting daily takt numbers, the momentum shifts to the next urgent project. The data pipeline, how results are stored, validated, structured, analyzed, or even found later, quietly fades into the background.

Meanwhile, the EOLT system is generating an enormous amount of valuable information. Every single test run produces signals that could help improve capability, reduce escapes, diagnose warranty returns, or drive Six Sigma improvements. But unless someone deliberately builds a usable data pipeline, those insights disappear into a pile of CSV files, neglected SQL tables, or are not collected at all.

This blog is about that forgotten half of EOL testing, and what you can do to finally unlock the value that’s already sitting in your factory.

Why EOL Data Is So Important, and So Underutilized

There’s often a huge gap between what leaders think is happening with EOLT data and what’s actually happening on the ground. The expectations typically sound like this:

“We’ve been keeping our test data stored somewhere.”

“It can be accessed by Quality whenever required.”

“We’ll be using this data for SPC once we stabilize production.”

“There’s another project that will be following this one to create dashboards.”

“This data has been cleaned and sorted already.”

In theory, the data is being scrubbed, validated, indexed, archived, and correlated with part numbers, revisions, and operators. And ultimately, used to improve the business. But the reality is usually very different. In most factories, this is what actually happens:

• Data is saved in inconsistent formats by different test stations and engineers.
• Pass/Fail Limits are not version-controlled.
• SQL tables live on a local PC with no backups.
• CSV files pile up in folders named “temp,” “new,” or “old system”.
• Dashboards and data portals never get built.
• Phase 2 never gets funded.

And even when data is stored, it’s often impossible to use:

• Test records include missing or mismatched fields.
• Metadata (revision, operator, equipment ID) is missing.
• Time stamps are inconsistent or incorrect.
• No one knows how to open a *.TDMS file.
• Data is unvalidated, unstructured, and untrustworthy.

Engineers want to use EOL test data, but the pipeline isn’t there. The result, EOLT data becomes “digital exhaust” instead of actionable insight.

What’s most concerning isn’t the volume of information generated, but the value it represents. Every EOLT run provides a window into the capabilities of your process, the state of your design, and the variance in your manufacturing operation. Essentially, it’s the best way for you to keep an eye on the quality of your manufacturing process.

However, without a defined path to capture, structure, and analyze this data, its value is lost, leaving critical insights on the production floor.

What You Could Be Doing with Your EOL Test Data

Your End-of-Line tester should do more than just tell you whether a part passed or failed. When that data is captured, organized, and connected across teams, it can become a powerful tool for improving quality, reducing waste, speeding up production, and making better engineering decisions.

A few of the highest-value ways to use EOL test data include:

• Catch process drift before it becomes a problem – By tracking key measurements over time, teams can spot trends, shifts, or out-of-control conditions before they lead to scrap, rework, or customer escapes.
• Understand whether your process is truly capable – Capability studies help show whether your process is consistently meeting specifications—not just passing tests, but doing so with enough margin and stability to build confidence.
• Make better decisions with trusted measurements – If the measurement system is not repeatable or reliable, the data can point teams in the wrong direction. Connecting MSA data with production results helps prove that the numbers can actually be trusted.
• Enhance testing boundaries and eliminate false failures – Information from previous EOL results can help a team improve their testing parameters, strengthen their weak spots, and ensure they don’t reject good items. It also helps to establish an audit trail for parameter changes.
• Reduce cycle time by removing low-value tests – Not every test adds equal value. By looking at fail rates, test duration, and correlation to real defects, teams can identify tests that may be redundant, rarely useful, or candidates for consolidation.
• Connect factory results to warranty and field issues – When EOL records are linked to returned parts or field failures, teams can identify early warning signs present during production. This can reduce warranty costs and help engineering improve future designs.
• Give each team the information they actually need – Operators, quality engineers, line leads, and product engineers all need different views of the same data. Role-specific dashboards and alerts help make sure the right people see the right information at the right time.
• Limits, set-ups, and software releases – It would be far more valuable if all EOL data could be tied to the specific test limits, set-ups, and software releases actually used during testing.

EOL test data is not just output data. When effectively utilized, it serves as a tool for process improvement, helping teams to enhance quality, minimize variations, reduce cycle times, and make sound product decisions.

The Reality: EOL Data Only Works If the Pipeline Works

Even the best measurements, fixtures, instrumentation, and limits won’t produce value if the data pipeline behind them isn’t healthy. And this is where most organizations quietly struggle.

An EOL test system is constantly generating signals: currents, voltages, timings, displacements, communication messages, temperatures, pressures, waveforms, vision metrics, operator interactions, PLC handshakes… the list goes on. But unless all of that information moves through a clean, reliable, structured pipeline, it becomes impossible to trust, access, or analyze later.

Most companies assume the pipeline will “take care of itself.” In practice, it does not. It requires planning, effort, and maintenance. Experience helps.

What a Healthy EOL Data Pipeline Requires

A robust pipeline needs several well‑defined components working together:

1. Reliable Data Capture – Every test must record the same fields, in the same format, with the same metadata. No missing values. No mystery columns. No different units for the same measurement.

2. Standardized, Versioned Test Records – Measurements, limits, station configurations, fixture revisions, and software versions must all be captured and traceable. When a number shifts, you need to know whether the product changed or the test changed.

3. Automated Reporting and Alerts – Data and analysis are worthless if no one sees it. A good pipeline pushes insights out automatically; it never waits for someone to “go pull the numbers.”

4. Clean, Structured Storage – Data needs to live somewhere durable and queryable (SQL, Azure, AWS, SystemLink, etc.). Not on a local PC. Not in a trove of CSVs on a network share. Not in arbitrary Excel files named “latest_results_FINAL2.xlsx.”

5. Full Traceability – Every record should connect back to the basics. Without this, you can’t trust the results: Serial number, Product revision, Limit set ID, Station ID, Operator, Test sequence version, Part configuration, Time and date, Equipment calibration status, etc.

6. Automated Data Validation – Bad records should be flagged at the moment of ingestion, not discovered months later during a recall analysis.

7. Easy Access for Those Who Need It – SPC needs Quality. Throughput/FPY needs manufacturing. Distributions/histograms need engineering. CAN logs need controls. Yield trends need leadership. Varied needs from varied perspectives, but they all point back to the same source.

Why Most Factories Don’t Get This Right

The underlying issue is simple: making the tester work gets all the attention, making the data pipeline work gets whatever scraps are left.

And typically, that means:

• Data “storage” is whatever the test automation vendor has implemented.
• Organizational structures evolve organically over the years and engineers’ careers.
• Important metadata is missing because it wasn’t captured from day one.
• EOL software revisions shift quietly without updating version fields.
• MES/ERP integrations never happen.
• Dashboards get deprioritized because “production is up.”

As products change, limits evolve, stations age, and operators come and go, the data slowly becomes inconsistent and disconnected. The further you go without a structured approach, the harder it becomes to fix the past, and the less likely the data will be trusted for meaningful decisions.

When the Pipeline Fails, Everything Downstream Fails

Without trust in the data, there’s no way to perform SPC analysis, capability study, limit adjustment, detect drift, troubleshoot warranty claims, shorten cycles, increase yield, or influence future design generations. Or, put differently: poor-quality data feeds make your cutting-edge EOL tester worthless – just a fancy go/no-go light.

And that’s a waste of capability and money. The moment you treat the EOL data pipeline like a business-critical process, test results transform from something that needs to be stored to something that can be learned from.

Why Companies Don’t Fix This, and Why They Should

1. EOL data projects are important, but rarely feel urgent

Most companies understand that better EOL data would help, but it often loses to more visible priorities like getting the line running, hitting takt time, passing audits, and shipping product. The problem is that once production stabilizes, “Phase 2” data improvements are easy to forget. By then, months or years of valuable production history may already be missing, inconsistent, or unusable.

2. Ownership is often unclear

EOL data usually sits between multiple groups: Quality, Manufacturing, Test Engineering, IT, MES vendors, and sometimes the system integrator. Because no single team clearly owns the data’s structure, quality, and usability, everyone assumes someone else is handling it. The result is fragmented data that may exist somewhere, but is not trusted or useful enough to drive real decisions.

3. Fixing the data pipeline is often one of the highest-ROI improvements available

Improving an EOL data pipeline usually costs far less than buying a new test system, but it can unlock major value: higher yield, lower scrap, better traceability, smarter test limits, shorter cycle times, and stronger warranty/root-cause analysis. When capital budgets are tight, this can be one of the smartest ways to get more performance out of the equipment a factory already has.

Conclusion: Stop Letting EOL Test Data Go to Waste

You don’t need a new tester to get new value. If your End‑of‑Line station is running but your data isn’t structured, trusted, or visible, you’re leaving yield, quality, and warranty savings on the table.

The fastest ROI today comes from transforming your EOL results into a clean, reliable pipeline that feeds SPC, capability, dashboards, and decision makers. It’s affordable; it doesn’t disrupt production, and it works with the equipment you already own—making it a smart move in tight capital years.

Maximize impact, treat the EOL data pipeline like a business process: capture consistently, validate automatically, store centrally, version limits/configurations, and surface insights to the right people. When you do, your tester stops being a pass/fail gate and becomes a continuous improvement engine.

Ready to explore our solutions? DMC specializes in modernizing EOL data without ripping and replacing hardware:

• Stand up a standardized results schema (with limit/config/software versioning)
• Build ingestion + validation pipelines from your existing testers
• Create SPC, capability, FPY, and spec margin dashboards
• Automate daily/weekly reports and alerts for quality and engineering
• Connect to MES/ERP and warranty/returns for closed‑loop learning

DMC’s Test & Measurement team routinely works in the latest test technology to help our clients uncover the value hidden in EOL test data. From NI software to Microsoft SQL Server, Ignition, Grafana and more, DMC can help you turn test data into a business intelligence asset.

The post End-of-Line Test: What to Do with All that Data? appeared first on DMC, Inc..

At DMC, we see these challenges firsthand through our work with a wide range of manufacturers in different industries. Our team designs custom test solutions that meet demanding performance requirements while remaining practical, cost-effective, and scalable. By focusing on adaptability and reliability, we help operators build test systems that support the needs of today while looking forward to tomorrow.

Why Testing Matters More Than Ever

A well-designed PCB and PCBA test strategy does more than test products at the end of the line. It supports quality, throughput, and futureproofs manufacturing operations as they continue to scale and evolve:

• Quality Assurance: Catch defects early to avoid costly downstream failures.
• Throughput Optimization: Keep your line moving without sacrificing accuracy.
• Compliance & Traceability: Meet industry standards and maintain audit-ready records.
• Lifecycle Adaptability: Design systems that adapt as your product mix evolves.

There are many reasons today for manufacturers to take a flexible approach when building a test system. Flexibility allows manufacturers to balance precision with speed, validating each unit without bottlenecking production. Automated test solutions also enable constant measurement, surfacing issues before they impact final assembly or the field. This approach not only improves first-pass yield, it also shortens feedback loops.

It’s equally important that modern test platforms are built for change. As products evolve and regulations shift, operators need test systems that can scale easily, integrate new test points, and support expanded data collection without a full redesign every time. Flexible, software-driven architectures make it easier to maintain compliance and protect your testing investment.

DMC’s Approach: Expertise + Flexibility

What sets DMC apart? Deep in-house expertise, industry certifications, and best practices, combined with strategic partnerships. We’ve built relationships with leading fixture manufacturers, instrumentation vendors, and automation specialists. This allows us to deliver end-to-end solutions tailored to your requirements, or any specific pieces of the system you need:

• Fixturing Options: From full bed-of-nails (BoN) systems for high-volume lines, to cost-effective connector-based fixturing solutions for smaller runs.
• Instrumentation: Modular, serviceable, and upgradeable DAQ and rack-based instruments sourced from trusted partners, and selected based on your needs and requirements.
• Software: Several customizable platforms ready to deploy and meet your current and future needs.
• Peripheral Systems: From simple barcode scanners, printers, & PLC automation interfaces to add-on services like MES integration and analytics platform design and delivery.

Fixturing: The Foundation of Reliable Testing

Whether your Device Under Test (DUT) is a bare PCB, a PCB assembly, or a fully enclosed PCB, your connection strategy drives cost, reliability, and timing. DMC can offer consulting and execution for:

• Complete Bed-of-Nails Fixtures: High-reliability, low-cost, or well-balanced systems can be sourced from multiple DMC partners.
• Cost-Sensitive Alternatives: DMC can offer in-house-designed and built fixturing or pogo-pin connector-based systems to reduce your spend.
• Automation Options: Robotic or auto-loading systems for high-throughput environments.
• Flexibility for Low-Volume, High-Mix Assembly: Quick-change fixtures and adaptable test sequences.

Test System Architecture: Built for Your Needs

Every system we design can be tailored to your application and include the components needed to support reliable, efficient testing, including:

• DUT Connections & Fixturing: Based on your project needs.
• Instrumentation: High-power, high-current, and high-voltage measurements are our specialty.
• Overall System Integration: Turn-key workbenches and/or racks with integrated fixtures and peripherals, to cost-saving, simple enclosure-based systems to add to your workbench.
• Software: Modular, intuitive GUIs that make operation simple, and test-engineering-friendly test sequence and parameter modification.
• Peripheral Integration: Barcode scanners, printers, databases, MES, analytics, and more.

For low-volume, high-mix operations, we can often recommend a single flexible test system that supports easy fixture changeovers and multiple DUT configurations, a real cost saver.

Advanced Capabilities

• Multiplexing: Reduce instrumentation costs and/or support multi-DUT setups with smart switching topologies.
• Microcontroller Programming: Need PCBA flashing station capability in your test system? We’ve got you covered.
• Scalable Design: If needed, DMC’s flexible designs let you start small and expand as your needs grow.
• Non-Standard Measurements: From magnetic and optical sensing to RF and high-power applications, we take on complex PCBA test challenges others may avoid.

Software That Works for You

DMC software solutions are not one-size-fits-all; they’re built to deliver speed, flexibility, and futureproofing in one package. With ready-to-deploy base designs and a wide range of platform tools at our experts’ fingertips, we can get your custom application up and running quickly, reducing downtime and fortifying your system for the future. All of our platforms and tools are designed with future flexibility and integration in mind, helping to ensure system effectiveness as demand and technologies change:

• Ready-to-Deploy: Base designs and a wide range of platform tools allow us to get your test software up and running quickly.
• Customizable: DMC can quickly add the software features you need now, while our modular platform tools allow you to grow as your requirements evolve.
• Future-Proof: All of our software platforms and tools are designed with your future flexibility and integration needs in mind.

We can also walk through GUI examples and provide live software demonstrations to show how powerful and intuitive our systems are. For a deeper look at one of our platforms, see The Hidden Cost of One-Off Test Systems.

Why Choose DMC for Test and Measurement Solutions?

DMC has a proven track record with major US manufacturers, supporting their complex test and measurement challenges and shifting technology needs. With multiple regional offices across the country, DMC is able to provide coast-to-coast support wherever our clients operate.

In addition to deep test and measurement expertise, our team also specializes in non-standard measurements and challenging test scenarios, partnering with leading instrumentation vendors to deliver reliable results.

Whether our clients are looking for a complete, turnkey system or modular components that fit within an existing strategy, DMC adapts every approach to meet business goals and project needs.

The post Adapting Manufacturing Operations? Flexible PCB and PCBA Testing Is Key to Success  appeared first on DMC, Inc..

Consolidated Bench Testing Equipment sounds good because its approach addresses this problem directly. One standardized tester controlled by software and composed of modules could replace an assortment of special-purpose benches, lower costs, and get crews back into working equipment sooner. It is also an asset decision-making strategy consistent with the way the American Public Transportation Association and the FTA think about assets.

The Real Problem Is Tester Sprawl

Anybody who worked in a depot will know what this means: the depot will have one bench dedicated to doors, one bench to brake or pneumatic functions, one bench to propulsion or traction, yet another one to HVAC, yet another bench to module level faults identification, plus perhaps some temporary benches which eventually become permanent. This is precisely the situation captured by DMC: older equipment, mixed fleets, changes in vendors, and lack of facilities at the depot make consistent troubleshooting and validation of LRUs and other replaceable units very difficult. Moreover, the agency may need to support multiple benches for different types of components, such as electronic, electromechanical, mechanical, and pneumatic. Simply stated, the bench room stops acting as a coherent system and becomes more of a museum of one-offs.

The issue here is that sprawl leads to secondary maintenance requirements. Asset management guidance specifies that agencies require detailed information on the availability of spare parts, suppliers, failure rate, outages, costs, manuals, technical documents, and test data. However, according to APTA, many transit agencies face issues because this information may be missing, outdated, not easily available, or scattered across paper files and different IT systems. On the human resources front, rail vehicle maintenance already requires a wide variety of expertise covering different subsystems such as propulsion, brakes, HVAC, doors, communications, monitoring, and automatic train protection and operation (ATP/ATO). Any additional bench increases the complexity of hardware knowledge and troubleshooting skills.

The comparison to the left is a synthesis of DMC’s CBTE architecture, transit lifecycle management guidance, and established calibration and system health practices.

A simple analogy helps. Traditional bench fleets are like carrying a ring full of keys for doors you barely use. Consolidation is more like a badge system: you still need the right access rules, but you stop maintaining metal for every single door.

Why the Lifecycle Bill Keeps Growing

It is here that the business case really comes into its own. Both the APTA and the Federal Transit Administration make clear that effective asset management must take lifecycle considerations, risk, and performance into account when deciding how to program investments and allocate money in the operations-and-maintenance budget. This means that the true cost of ownership does not end at the expense incurred in acquiring or building the test bench. Rather, it includes the ongoing costs of replacing parts, service contracts, calibration, software degradation, documentation updates, training refreshers, and all those costly delays that occur whenever one specialized test bench stands out as the only available resource. The guidance on measuring costs from the National Institute of Standards and Technology highlights another critical component of that total expense: calibration and traceability provide the foundation for accurate and credible measurements. When an operator maintains five different benches, it is often maintaining five different calibration processes as well.

This is also one reason why “we already own the test benches” may be a dangerous assertion. Old and specialized benches that have been deemed just useful enough to weather the annual budget cycle can suddenly become the very reason why the maintenance schedule stops.

What Consolidation Really Fixes

The best aspect of this design philosophy is that consolidating does not mean becoming monolithic. DMC’s Consolidated Bench Test Equipment (CBTE) design is said to be configurable in the sense of a framework that integrates modular instrumentation, application-specific interfacing, safety conditioning, and automated test software under one roof. DMC also highlights expandability options based on interchangeable test adapters, fixtures, harnessing, instrumentation, and processes, rather than replacing an entire test bench. The best analogy, however, is not that of one magic machine, but of a single drill with a set of interchangeable drills. The tool itself does not change much, but the bit is swapped for a new purpose.

From a practical standpoint, that makes maintenance a lot easier in some concrete ways. For one thing, spare parts inventory requirements go down since common hardware and software elements can be sourced with greater ease. Calibration becomes simpler, too, since technicians will need to manage a smaller number of base units with which traceability can be maintained. Cross-training of specialists will be easier since there will be just one interface and troubleshooting philosophy to learn. Automated test outcome assessment and guided user troubleshooting are also part of DMC’s message, while cloud-based pages hint at test-station monitoring.

Outcomes to focus on in a consolidating effort are, therefore, quite simple to name. The goal is a reduction in support costs per fixed sub-system, fewer bench-specific spare SKUs, fewer calibration events annually, lower mean repair times, shorter repair times for each LRU, and higher availability of tested benches. Only such gains justify adopting the concept in practice.

How to Migrate Without Losing Service

The migration process should intentionally be dull. Begin with a complete list of legacy benches, LRUs supported, interfacing considerations, calibration requirements, repeating failure modes, and problematic subsystems. Identify common tasks based on similarities in signal types, load testing, safety interlocks, harness families, data logging capabilities, and technician workflows. Establish a baseline CBTE core to pilot in a single high-volume or high-pain subsystem before scaling out using the same common core but with additional interchangeable adapters and processes. This approach dovetails with DMC’s modular architecture and lifecycle-planning guidelines and underscores the importance of understanding the economics of CBTE migration as demonstrated by modernization studies, which show that it depends strongly on workload, sustainment costs, and rehosting effort.

As you head towards that moment of truth, three questions should be paramount. Does your new bench test station properly emulate actual fleet operations and maintenance procedures? Will it be capable of meeting your existing fleet’s standards for electronic equipment testing, including EMC compatibility and shock and vibration requirements? Can it scale without needing to create a new core every time you add a vendor change or LRU? These are precisely the reasons why rail standards for electronic equipment, EMC integration, and shock/vibration matter, as well as why modularity, maintainability, UL508A panel build capabilities, and quality ISO 9001 systems management should resonate in your implementation.

If this looks familiar, the next productive conversation may not involve another piece of niche test equipment. Instead, it will entail comparing your overall sustainment responsibilities—spares, calibration, training, downtime, and scalability—against the reality of the depot with respect to a comprehensive, consolidated architecture for testing and diagnostics. Visit the DMC Mass Transit Consolidated Bench Test Equipment page or explore our wider range of Test & Measurement Automation products.

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That’s why early engagement with a test system integration partner matters. Whether you operate in the automotive, aerospace, energy, semiconductor, medical devices, or consumer electronics, involving the right partner early can reshape your project’s trajectory, reducing costs, enabling innovation, and accelerating delivery.

Early Input Leads to Smarter Test Strategy

Engaging a system integrator like DMC early in the project lifecycle helps teams make better decisions sooner. During the exploratory phase, DMC provides critical insight into test requirements and system architecture, informing on project scope, uncovering cost-saving opportunities, and aligning test capabilities with production and program goals.

In one recent project, early discussions led to the decision to split a complex end-of-line test process across multiple stations. This modular approach allowed the customer to better utilize their most expensive asset (high-power battery cycling equipment) by moving test processes that do not require high power to lower-power, lower-cost stations. The result was a more efficient test flow and significant capital savings.

In another case, DMC’s Test and Measurement team proposed a high-power multiplexing system that reduced the number of test assets needed to meet production rates. By automating these connections with well-designed software, the team effectively shared the high-power, high-cost equipment across multiple test stations, saving the client over a million dollars.

And in a third example, a DMC customer initially requested a bed-of-nails test system for a printed circuit board assembly (PCBA). After initial discussions, DMC engineers learned that the test’s purpose was a basic functional checkout prior to further assembly. All required tests could be performed through the already-installed PCBA connectors. DMC eliminated the need for an expensive bed-of-nails fixture and replaced the device-under-test (DUT) connection with a cable harness terminated in custom-designed, high mating-cycle, pogo-pin style connectors. This simple change, along with a reduction in DAQ system precision and accuracy requirements, cut hardware costs by 50 percent.

Phase 1 Exploratory Projects Reduce Risk and Clarify Direction

A Phase 1 exploratory project gives you a low-risk way to validate assumptions before committing to major capital or locking in system requirements. These engagements are typically short, fixed-cost engineering efforts focused on answering the right questions early, when changes are still inexpensive.

During Phase 1, DMC works with your team to:

• Clarify test requirements and production constraints
• Evaluate multiple system architectures and test strategies
• Identify cost, schedule, and scalability tradeoffs
• Define a clear path from prototype to production

In one example, a one-week engineering sprint helped a client define requirements and lay out two test system concepts: an initial system to support early builds and a scalable production system for higher volumes. This early clarity informed capital planning, aligned stakeholders, and prevented costly redesigns later in the program.

Phase 1 projects are intentionally flexible. You can move forward with full implementation, adjust scope, or pivot entirely based on what’s learned. Regardless of the outcome, you gain a clearer understanding of risks, costs, and long-term implications. Early collaboration sets the stage for smarter decisions. Clients can choose to proceed with full implementation or pivot based on feedback from their integration partner, setting the foundation for a test system that meets today’s needs without limiting future growth.

Broad Expertise and Strategic Insight Across Industries

While some examples stem from battery testing or electronics, the same principles apply across industries. DMC’s engineers bring experience from hundreds of test system deployments across major industries and relationships with suppliers of emerging test and measurement technologies.

By engaging early, you gain access to lessons learned across platforms and applications, including:

• Selecting the right instrumentation and switching topology
• Designing scalable software architectures built for change
• Integrating safety-rated systems and compliance considerations
• Consideration of emerging test and measurement technologies

The cross-industry perspective of a test system integration partner can help you avoid common pitfalls and build systems that are cost-effective, flexible, and ready to evolve.

Conclusion: Don’t Wait, Engage Early

If your team is planning a new test system or preparing for a major upgrade, don’t wait until specifications are locked or problems surface on the production floor. Early engagement with a test system integration partner gives you better options, clearer tradeoffs, and fewer surprises.

Whether it’s a short exploratory project or just an initial conversation, involving DMC early can help reduce risk, control costs, and design a test strategy that scales with your program. The difference between a good system and a great one often comes down to decisions made at the very beginning.

The post It’s Never Too Early to Engage With Your Test System Integration Partners appeared first on DMC, Inc..

Connecting at the Conference

This year at the event, the DMC team will participate in technical sessions, breakout groups, and forums throughout the two days. As a long-time NI partner, these conversations offer an opportunity to exchange innovative ideas, explore emerging best practices, and stay up to date on NI’s technology roadmap.

Every year, NI Connect offers attendees an opportunity to connect on innovations in testing and automation. This year, attendees will participate in discussions covering CompactRIO applications, timing and synchronization best practices for PXI test systems, and new approaches to using AI to support LabVIEW development, among other topics spanning the two‑day event.

In previous years, DMC has demonstrated production-ready solutions built on NI technology, including a compact, configurable HIL automated test system and a Battery Production Tester (BPT). These demos highlight the various ways we use NI platforms in our tech solutions to build scalable, flexible test frameworks for manufacturing and validation projects.

Our team has also been actively involved in Leadership and Partner Forums, and with seven DMC engineers attending this year, we look forward to continuing those conversations and contributing to breakout groups and forums throughout the conference.

DMC’S NI Partnership

Since 1997, DMC has partnered with National Instruments to deliver instrumentation, measurement, and automated systems, with our team successfully integrating NI tools and products across a variety of projects and industries.

This partnership reflects our continued commitment to technical excellence and software development, and we are proud to be recognized as an NI Center of Excellence, a distinction earned through rigorous evaluation of our software engineering practices, community engagement, and technical leadership.

In addition to the Center of Excellence designation, our collaboration with NI spans multiple specialty areas, including NI Migrations & Upgrades and our NI Vision Specialty partnership, recognizing our ability to design and integrate system-level vision solutions.

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