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..

Smart Manufacturing and Intelligent Automation

To get started, there are a lot of automation buzzwords out there that you may recognize: Artificial Intelligence (AI), Machine Learning (ML), Augmented Reality (AR), Virtual Reality (VR), Blockchain, the Internet of Things (IoT), the Industrial Internet of Things (IIoT), Cobots, Integrated Robotics, Additive Manufacturing, Machine Vision, Flexible Manufacturing, Smart Sensors, Digital Twins, Predictive Maintenance, Key Performance Indicators (KPIs), Overall Equipment Effectiveness (OEE), Automated Guided Vehicles (AGVs), Software as a Service (SaaS), the Edge, Edge Processing, Cloud…it goes on and on. The exciting thing to know is that each of these is, to some level, real, and each of these has its place and can be used independently or additively to add intelligence to a system.

As an automation system adds technologies, there are additional buzzwords used to describe the result such as Industry 4.0, Smart Factory, Smart Manufacturing, and Intelligent Automation. I like the last two because they do the best job of holistically describing the technology-driven approach of utilizing connected machinery, software, and tools to identify and improve manufacturing performance. Smart Manufacturing is not an off-the-shelf technology but is instead the combination of multiple technologies to enable the use of live data for real-time optimization of plant operations.

Data and Connectivity

Data is the backbone of smart manufacturing, enabling a more connected, efficient, and responsive automation system. Real-time communication between machines, sensors, and central systems allows for immediate monitoring of equipment performance and production processes, leading to faster response times to any issues that arise. And with that data comes enhanced efficiency through the reduction of downtime and optimization of processes.

But a smart manufacturing system is more than just the automation controller (usually a programmable logic controller known as a PLC). Sharing data across different levels of the Automation Pyramid—from the plant floor to the cloud—opens up new ways to streamline operations and connect the information technology (IT) and operational technology (OT) worlds. This data connectivity can link both enterprise systems and manufacturing equipment to provide better visibility into production, increase efficiency by flexibly manufacturing exactly what you need, and reduce cost by minimizing waste and optimizing resource usage.

ISA-95 and the Automation Pyramid

I mentioned the Automation Pyramid as it’s an important part of an Intelligent Automation system. ISA-95, an international standard for integrating enterprise and control systems, provides a model and terminology for aligning manufacturing operations with business processes. The standard offers a layered approach to manufacturing operations often referred to as the Automation Pyramid. This pyramid breaks operations into distinct levels, each building on top of the other, and provides a structured approach to data flow through a common data exchange framework.

The bottom of the Automation Pyramid (Level 0) is the physical process and the Field Level sensors that interact with it. Building upon that is the Control Level (Level 1), where inputs are being monitored, decisions are made, and outputs are controlled. The Supervisory Level (Level 2) sits on top of the Control Level. This is where Human-Machine Interfaces (HMI) and Supervisory Control and Data Acquisition (SCADA) applications lie to oversee operations and provide operators with the necessary information needed to monitor and control the process. Level 4 adds Manufacturing Operations Management (MOM) and Manufacturing Execution Systems (MES) to optimize the manufacturing process, integrate quality control, and provide visibility into the production process to facilitate the efficient execution of manufacturing tasks. It is also the layer that interfaces with the top layer of the pyramid, the Enterprise layer (Layer 5), which includes business planning, inventory, logistics, and other ERP functions. If followed when architecting and implementing a Smart Manufacturing system, the Automation Pyramid provides consistent and interoperable data exchange across all levels of manufacturing operations.

Advances in the tools and technologies available at all layers of the pyramid have led to making it much easier to implement Smart Manufacturing systems that follow the ISA-95 model.

Field Devices

In the past decade, there has been an explosion in the number of available sensors, actuators, and drives. Smaller form factors and better electronics provide more sensing options in challenging environments. Smart sensors and actuators are doing more processing at the device level, increased adoption of AS-i and IO-Link makes sensors easier than ever to connect, and the increased data available provides more in-depth diagnostics and configuration options. Sensors can now be found with Wi-Fi, Bluetooth, Zigbee, and even cellular connections to allow for direct MQTT communication with IoT platforms running in the Cloud or wireless connectivity to more traditional control systems. Variable Frequency Drives (VFDs) are including built-in data to feed AI/ML models, like those integrated into Rockwell’s FactoryTalk Analytics Guardian AI, to turn drives into sensors and provide predictive maintenance functionality. Low-cost VFDs, like the Siemens G120C series, allow for simple commissioning and diagnostics over Wi-Fi using only a cell phone. More data, easier integration, and better reliability mean the intelligent manufacturing automation systems being built today will provide better long-term value.

Control Devices

PLCs have long been the workhorse of the factory—designed for use in less-than-ideal operating conditions, running 24/7 for years at a time, and providing flexibility for expansion. While improvements in hardware continue to occur, it’s been improvements in software that have really led to smarter applications. Motion controllers have largely disappeared, replaced by integrated motion in controllers like the Siemens S7-1500T, which can support up to 128+ axis. Rockwell’s Integrated Robotics now allows robots to be directly integrated with ControlLogix PLCs, and unified machine control allows OEMs to more easily integrate, control, and coordinate advanced motion technologies. Both of these solutions simplify machine operation and maintenance for the end user, who must only understand and support one system. Soft PLCs (PC-based PLCs), like those developed by Beckhoff, can now turn almost any PC into a real-time control system while integrating web-based HMIs like TwinCAT HMI on the same hardware. Increased adoption of global communication standards, like OPC-UA, by all manufacturers means it’s easier than ever to communicate between devices.

While traditional motion control applications have been absorbed into PLCs, incredible new technologies have arisen to provide faster, more flexible, and easier-to-program smart conveyance systems. Rockwell’s MagneMotion Lite and QuickStick platforms provide a modular and scalable material handling solution with precise positioning, routing, traffic control, and no maintenance. Beckhoff’s XPlanar allows for 2D transport and six (!!) degrees of freedom motion that can open up new worlds for machine and process designers.

Across all of these, improvements in development IDEs allow companies like DMC to reduce time and cost by leveraging more reusable code and providing more flexible solutions with increasingly modular architectures. Improved simulation software allows for advanced simulation and even virtual commissioning, ensuring that machine and factory start-ups go smoothly and on schedule. New advances allow for better collaboration on multi-developer projects, facilitate code reviews and merges, and integrate with third-party source control tools like GIT.

Supervision (HMI & SCADA)

HMI systems have long been hardware-based—you bought a physical device and programmed it to communicate with a PLC or controller. While this can be convenient, and hardware-based HMIs are still being used, the modern trend has seen the evolution of web-based applications that can turn any computer, phone, or tablet into a cutting-edge HMI. Openness and extensibility are the name of the game, as platforms such as Rockwell’s FT Optix, Siemens WinCC Unified, and Beckhoff’s TwinCAT HMI allow for HMI development that can be deployed to an almost limitless number of targets. Object-oriented designs, web-based tools, simulation testing, and more powerful scripting mean development times can be reduced while delivering more advanced applications.

In the world of SCADA, we’ve seen Inductive Automation’s Ignition eat up market share as it continues to evolve. Its versatile, scalable, and modular architectures allow users to scale applications up as needed, while its cross-platform compatibility and support of industry standards like OPC UA, MQTT, and RESTful APIs facilitate integration with a wide array of devices and systems. However, the combination of its user-friendly development environment and transparent and cost-effective pricing model have really been the drivers of its strong growth. Despite all of that, the traditional players are still forging forward with continuous improvements. Aveva (formerly Wonderware), Iconics, Siemens WinCC, and Rockwell FactoryTalk SE continue to see wide use, and each offers its own compelling advantages. For example, Siemens’ WinCC OA platform offers incredible flexibility and scalability, being used for some of the largest and most important systems in the world due to its performance and reliability.

Regardless of the platform, the trend is the same. More open communication with devices and systems, tighter integration with enterprise databases like Oracle and SQL, and cross-platform web-based options for access and control. As these platforms become more powerful and new tools and features are released, we see the increased movement of traditional MES and MOM features into the SCADA layer (things like OEE, for example).

Management (MES)

At the top of the OT space, the MES layer is the bridge between our traditional factory automation systems and business systems. Need to integrate inventory tracking with your manufacturing process? Do FDA regulations require detailed track and trace capabilities to tie raw ingredients to finished products? Want to optimize your production schedule based on your real-time orders to minimize waste? This is where MES (Manufacturing Execution Systems) comes in. They are often highly customized based on the organization and the toolchains in use but provide configurable models and frameworks to solve the most common problems. At DMC, we have frequently utilized Sepasoft, Tulip, and Simatic IT to build problem-solving MES systems for our clients.

Advances in the MES world continue to follow the same trends we see elsewhere:

1. Smoother integration with external systems and smart devices. MES systems are increasingly integrating with IoT devices and smart sensors to collect real-time data from the shop floor. This integration enhances data visibility and allows for better monitoring and control of the manufacturing process. Additionally, the use of edge computing in MES helps process data closer to the source, reducing latency and providing improved analytics and decision-making.
2. Advanced data analytics and AI. Predictive analytics, machine learning, and AI are being integrated to better analyze the huge amounts of data available, optimize production schedules, and improve decision-making processes.
3. Expanded API support. Modern platforms are continuing to improve already robust APIs and web services for integration into even more enterprise systems, data sources, and third-party applications.
4. Moving to the cloud. Cloud-based systems provide better scalability without the need for significant infrastructure improvements. Cloud MES deployments can pull data from multiple sites to allow for enterprise-level reporting and also be a central repository for reusable code across all MES deployments.
5. Security. Cybersecurity should have been mentioned at every level of the pyramid, but with the level of data available in MES systems, continued security updates to protect from cyber threats.
6. Digitalization. Replacing pieces of paper, excel documents, etc., with things like digital forms, digital work instructions, or CAD drawings through the MES screen. To eliminate the need for manually typing data from paper into a database or Excel for analysis, the information is directly entered into a system that both stores and analyzes it, providing valuable insights for decision-making.

Enterprise (ERP)

Finally, we’re at the top of the pyramid! ERP is a software system used to manage and integrate an organization’s core business processes. ERP systems facilitate the flow of information between all business functions within the organization, providing a unified view of operations and enabling better decision-making. In most cases, these systems are outside of the OT domain, although we frequently integrate with them to facilitate a truly smart manufacturing system.

The Benefits of Intelligent Automation

So, what’s the big deal? Why move beyond simple automation? This is a good question and not one with a universal answer. For some organizations, smart manufacturing and intelligent automation may not be necessary. There can be a high level of upfront investment, and the ROI may not be there to justify the business case. But for the others, the benefits can be huge. Increased efficiency, reduced downtime, better visibility in the manufacturing process, the ability to scale production to meet demand, flexibility to allow for new products and features, catching out-of-tolerance products earlier, and tweaking the process to eliminate scrap…the list can go on.

I’ve worked with customers to build smart manufacturing cells linked to their inventory and ordering systems. Through intelligent automation, we can flexibly manufacture products as they are sold— shipping custom-ordered products within two hours of order placement—without a need for multiple production lines and with significantly reduced labor costs. We’ve implemented OEE tracking systems to identify where and when bottlenecks occur so resources can be properly allocated and efficiency can be maximized. By utilizing the diagnostics available, we developed lines that allow for quick troubleshooting so that downtime can be minimized and systems can be repaired quickly. We’ve developed track-and-trace applications in the food and beverage industry that allow manufacturers to precisely identify which products need to be recalled in the event of discovered contamination. With the ever-evolving technologies, the ever-growing availability of data, improving software, and a large team of automation experts, DMC can help you achieve the results you are looking for.

What We Can Do For You

This month marks my 14th anniversary at DMC and with it, 14 years in the Automation Industry. In that time, I have had the opportunity to be a part of some truly amazing projects and to work with some incredible clients. Looking back, it’s fun to think about the transformation and growth I’ve been involved with as our clients have used increasingly more advanced technologies to meet their business needs. While the intrinsic satisfaction of seeing our clients reach new levels of success provides a lot of motivation, 2024 has been a fun year due to the level of national recognition DMC has received for being a leader in the Automation and Controls industry.

In March, DMC was awarded the national Rockwell Automation System Integrator Innovation Award. The turnkey design/build project for which DMC was recognized provided our client with a truly flexible manufacturing cell capable of producing customized products from 100+ base SKUs with minimal operator involvement. This step helped our client move from simple automation to smart manufacturing overnight and delivered immediate results to their bottom line.

“The System Integrator Innovation Award recognizes DMC for demonstrating an innovation solution for their customer that helped solve and address business problems, drove transformation, and delivered meaningful and measurable business outcomes using best-in-breed technologies,” Amy L. Graff, North America Partner Marketing Manager at Rockwell Automation, said.

Shortly after, DMC was named the 2024 Control System Integrator Association Integrator of the Year Award. This award recognizes DMC’s significant contributions to the advancement of the association and the automation industry as a whole.

So, whether you are just starting your journey toward automation or looking to take the next step toward smart manufacturing, contact us and see what DMC can do for you.

The post Advancing Manufacturing with Intelligent Automation appeared first on DMC, Inc..

Five Levels of Automation

According to the ISA-95 standards there are five levels of automation (also referred to as the “Automation Pyramid”. Those are: 

• Level 4: Enterprise management (ERP)
• Level 3: Manufacturing operations planning (MES and MOM)
• Level 2: Supervising (HMI and SCADA)
• Level 1: Control (PLCs)
• Level 0: Field devices

Purpose of the MES Layer

This blog series will be focusing on Manufacturing Execution Systems (MES) which lives in Level 3. The main intent of the MES layer is to act as a messenger between the ERP and the SCADA and PLC/HMI layers. For instance, in a car manufacturing firm, the ERP will have a customer order for 10 blue cars along with the design of the car model they want to build. The SCADA system will send commands and recipe data to the PLCs and acquire data while the PLCs are sending commands to the devices assembling the car.  

In this system, the missing functionality is taking the customer order information, scheduling it to the applicable production lines, and sending the car model specific data that is in the ERP to the control layer. This task is typically completed by the MES layer. The MES layer takes in an idea of a product and then creates a plan to execute it in the manufacturing environment.  

The basic building blocks of a typical Manufacturing Execution System include:

• Create Work Orders, Part Numbers, and Recipes 
• Schedule work orders 
• Material tracking and traceability 
• Overall equipment effectiveness (OEE) 
• Downtime tracking 
• Quality control and statistical process control (SPC) 
• Digital work instructions 
• Tracking consumption of inventory and relaying that information to the ERP or WMS 
• Labor management 

The lines between the different ISA-95 levels can sometimes be blurred and functionality that may be a traditional MES feature you may see in the ERP or SCADA layers. This is normal and gives you the flexibility to design your systems to best fit your processes. For example, sometimes the ERP will have work orders or SCADA may calculate OEE. 

MES Platforms

There are multiple industry-wide used platforms for MES, each with their own set of features and capabilities. Some common platforms are: 

• AVEVA MES  
• Rockwell FactoryTalk ProductionCentre
• Sepasoft (built on Ignition)
• Siemens OpCenter
• TrackSYS
• Tulip

Sepasoft

Sepasoft offers real-time visibility into the operations of the plant floor to help improve efficiency and reduce costs. 

Sepasoft MES Solution for Food Processing Facility
DMC developed a custom Manufacturing Execution System (MES) application for a large manufacturer in the food and beverage industry to optimize and automate their system.

FactoryTalk

FactoryTalk is an industrial automation software from Rockwell Automation that improves plant efficiency and productivity. 

Factory-Wide MES Data Collection System
DMC implemented an extensive data collection system for a client in the confectionary industry offering the client a closer lens into their processes with automated data aggregation.

Ignition

Ignition is a development environment from Inductive Automation that allows you to create an industrial software application. It’s one of the fastest growing HMI/SCADA platforms today.

Optimizing Load Times in Ignition Perspective
Ensure that screens load fast and actions are snappy when using Ignition Perspective to create bigger and better projects.

Creating Dynamic Ignition Perspective Projects, Part One: Bindings and Transforms
Learn how to start a Perspective view in Ignition, create bindings to tags and properties, and use binding transforms to correctly display that information.

Creating Dynamic Ignition Perspective Projects, Part 2: Button Event Actions
Learn how to use button events to write to component properties and tags in Ignition.

Creating Dynamic Ignition Perspective Projects, Part 3: Embedded Views and Flex Containers
Learn how to combine objects into an embedded view in Ignition and create a dynamic layout using a flex repeater.

Creating Dynamic Ignition Perspective Projects, Part 4: Parameters, Indirect Bindings, and Flex Repeaters
Learn how to add parameters to your embedded view in Ignition, drive indirect tag bindings, and maximize our layout efficiency in a flex repeater.

The post Intro to Manufacturing Execution Systems (MES) appeared first on DMC, Inc..

DMC’s Denver office in the LoDo district of downtown Denver was a home away from home for our visiting team members. We enjoyed informative sessions, product roadmaps, and hands-on training sessions during the day, and food, live music, and fun activities each evening!

Check out our highlights from the Summit:

Rockies Game

Before the Siemens Summit began, DMCers got together to enjoy a Denver Rockies baseball game!

Our hosts at DMC Denver entertained the group with pizza and beverages at their office. We then traveled to Coors Field to see the Colorado Rockies play the San Diego Padres.

Welcome Reception

After DMC wrapped up our welcome events, the Siemens Summit Kicked off on Monday night. The welcome event, sponsored by the Siemens Solutions Partners,  featured live music, great food, and fun activities such as archery, a fly fishing demo, and simulated calf roping.

There was also a geocaching scavenger hunt, which Leon Grossman and I won! 

Best Brews of Denver

On Tuesday, the DMC team, some of the Siemens Solution Partners, and DMC clients met for a beer tasting tour of Denver. The group traveled together on two trolleys from the event center to DMC’s Denver office’s rooftop to enjoy barbecue and beers before heading out for the evening. The trolleys stopped at two breweries, Odell Brewing and Black Shirt Brewing, before heading back to the hotel to rest before another full day at the convention center.

Summit Demos

DMC Demos with Ice Cream

 

DMC made ice cream at the Siemens Solution Partners Display for visitors during the day. Attendees stopped at our booth to have a quick ice cream snack on the way to see presentations. Built by Prism last year, improved by DMC this year, and paid for by the Sponsoring Siemens Solutions Partners,  the ice cream machine offered all of our guests a reminder of how the integration of programs like these can create imaginative (and delicious) results!

The DrinkBot

DMC’s DrinkBot utilizes a Siemens S7-1200 PLC, 7″ Comfort Panel, Siemens G120, and Siemens 1FK7 Servo Motor with DRIVE-CLiQ encoder to control the operation. The DrinkBot provides eight different alcohols or syrups and six different mixers for a large variety of drink options. 

DMC’s Presentations

Five DMC Representatives were invited to present at the Automation Summit this year, check out their presentations below.

Dynamic and Scalable Systems Using WinCC OA
Leon Grossman

Project Manager Leon Grossman led a presentation on Siemens WinCC OA applied to a client’s application. He demonstrated using this flexible, scalable platform in conjunction with Siemens and third-party components for a complex application. The presentation highlighted some of the critical features of WinCC OA, how it could solve the most challenging applications, and as a bonus, announced the release of the WinCC OA Open Library v1.0!

Auto Code Generation and Rapid Brewery/Distillery Automation
Ryan Landwehr

Project Director Ryan Landwehr showed attendees how the Siemens Brewing template, a TIA Portal based auto code generation tool created developed in partnership by DMC and Siemens, can be used to rapidly create complete automation in rapid breweries and distilleries of all sizes. From craft distilleries to regional breweries, see how the Siemens Brewing Template can help make automation easy!

To learn more about how DMC can facilitate your food and beverage manufacturing automation, contact DMC today.

Multi-site WinCC 7 Development with Centralized Process Historian & Information Server from
John Michael Frullo

John Michael Frullo, Project Engineer, illustrated ways in which WinCC 7 tools and standards can be used to facilitate rapid project development across multiple locations. Frullo also demonstrated how multiple plant locations can be easily connected to a centralized data storage and automated reporting system.

Flexibility and standardization using dynamic IO addressing and option handling from
David Berno

I presented solutions developed by DMC that fully leverage S7’s hardware and development flexibility to create efficient and agile manufacturing deployments.

Taking your Siemens PLC s7-1200 to industry 4.0
James Condon

James Condon, Project Engineer, led a presentation on Siemens PLC Industry 4.0. The presentation covered how Industry 4.0 can offer better efficiency for existing industrial equipment.

Thank you, everyone, who attended the 2019 Siemens Automation Summit. We can’t wait to see what’s new in 2020!

As a proud Siemens Solution Partner, DMC is ready to step in with our extensive knowledge and expert solutions. Learn more about DMC’s partnership with Siemens. 

The post 2019 Siemens Automation Summit Highlights appeared first on DMC, Inc..