The day started with an excellent FedEx Day kick-off video and breakfast in each office.

The DMC team from coast to coast completed a huge range of projects ranging from adding LED lights to the Chicago office’s slide to creating an IoT coffee solution in Denver to a React Native ping pop app in New York.

Here is a look into some of the projects from FedEx Day 2019!

Boston

Fast Automation Facts Videos – Frank M, Christina, and Brian S.
Frank, Christina, and Brian created short how-to videos for simple automation tasks, such as how to download/upload with different types of PLCs. These videos can help automation engineers before service calls or when ramping up on a technology to become more familiar with the specific tasks outlined in the videos.

Check out all of our Fast Automation Fact videos. 

Sky-in-the-Pi Upgrade – Adam W.
The Sky-in-the-Pi project is a solution that can run any type of configured “weather” with light and sound for any indoor location. You can go from a dark winter day to a relaxing breezy fall or a warm beach day while sitting indoors. It is also used as a learning tool for Python, Node.JS, and DMX controls.

Adam upgraded the old Sky-in-the-Pi project to demonstrate greater stability, expand debugging, and capture more of the operating instructions in GitLab. As a bonus, he targeted upgrades for development and animation of the lights and improvements to the sunrise/sunset color scheme and moonlight simulation.

LogJam – Tom, Sophia, and Lillian
The LogJam team continued their development of “LogJam,” an internal log parsing/filtering tool used by both the Application Development and Test & Measurement teams. They added to the tools available to DMC engineers for troubleshooting and debugging systems.

Specifically, this team added functionality that supports arbitrarily complex expressions. These new functionalities also improved the flexibility of UI and filtering for different log types, and allowed for parsing based on Regex in addition to basic string delimitation.

Chicago

Cyber Security Exploration – Leon, Nick S, and Tim Johnson
This team created content for a cybersecurity class for DMC. They explored the difficulty of accessing data out of an S7-1200 PLC with Wire Shark while connected to the PLC with TIA Portal. This upcoming cybersecurity class will help DMC engineers provide more secure systems for DMC’s clients and prove to be a differentiating factor as cybersecurity is becoming an ever more important part of the industrial landscape.

North Tower Lab Setup – Ben G, Brandon, Ryan P, and Furqan
Ben, Brandon, and Ryan cleaned up the mounting and wiring of existing automation hardware and increased the space available to mount new automation hardware in the new North Tower Lab. The team cleaned up existing projects and then installed a third rail on two of the panels to create mounting space for temporary projects.

While the team organized and cleaned the lab, Furqan created a .NET application that keeps track of the items in the North Tower Lab using barcodes. He created an item check-in/checkout system using a handheld barcode scanner.

LabVIEW Camera Drivers – Katherine, Rose, Andy K, and Madeline
Through several recent LabVIEW projects, DMC developed an Object-Oriented camera driver toolkit. This team’s goal was to continue to improve this toolkit so that it has better-stimulated capabilities and more options for testing methodologies. They also cleaned up the toolkit to get ready for packaging into a VI package for use on future projects.

From completing Git tutorials to training Andy and Rose in LV OO concepts, this project helped DMC by providing better camera drivers, which can be used on a wide range of client projects and create an example for how to use these camera drivers in several different modes.

DMSlide – Molly, Alex H, Nick L, Eric L, and Emily B.
This team’s goal was to install addressable RBG LED lights in the DMSlide with a sensor to auto-activate the lights when someone enters the slide. This project showed off DMC’s embedded capabilities while lighting up the slide with cool patterns and colors.

The hardware and software used in this project include: APA102C RGB addressable LED strips, LED Channel with Diffuser, Nucleo L476RG microcontroller, Ultra-Sonic Distance sensor HC-SR05, and Mbed RTOS.

Embedded Automated Hardware Test Rig – Alex K, Matt G, Jeremy, Ji-Hoon, Eddie and Tim Jager
Part of DMC’s Embedded team wanted to increase their capabilities of automatically testing embedded hardware projects using the GitLab CI. They aimed to familiarize themselves with several testing tools and methods. The team separated into sub-teams focusing on individual parts of the test process, including the test server, test framework, and multifunction test PCB.

They generated several examples of how to implement automated tests using IceTea and an Analog Discovery to set up the necessary infrastructure to support running continuous tests on DMC’s embedded projects.

“Get-a-Room” Bot – Andrew M, Jacob C, and Christopher
Andrew, Jacob, and Christopher developed a Slack bot that you can ask for the available collaboration rooms for spontaneous meetings. For the “back-end” of the bot, this team used the Microsoft Bot Framework to put together a bot quickly. From there, they leveraged the Microsoft Graph API to query the Outlook calendars for the rooms at DMC Chicago to determine the availability of different rooms.

The team deployed the bot to a Web Bot service on Azure and authenticated with Azure Active Directory using an Azure AAD app registration. They then set up the app to run as a service so that users don’t have to sign in with their individual Active Directory accounts. Finally, the three registered the bot with DMC’s Slack domain, and it just worked.

Flow Development – Mike D, Dan M, and Jamie H.
Members of our DWS team created and updated Flows to easily automate regular DMC tasks. This team automated DMC’s mentor lunch scheduling system to reduce the time spent looking for mentor lunchtime slots. By doing this, the three utilized one of Flow’s newest features: searching for meeting times. We also got to spend time with Mike’s new puppy, Kona!

Automating Time Checking – Accounting Team and Ken
Ken taught the accounting team a more efficient way to fish for their own data to improve the current data entry/manual processes. Ken provided Accounting with ways to merge data from multiple sources to replace spreadsheets and lists the accounting team had been manually creating from QuickBooks. The tips and tricks that Ken taught the Accounting team will allow them to be more efficient in the future.

Dallas

New Office Setup – DMC Dallas Team
The DMC Dallas team, consisting of John W, Jacob R, and Ryan L, had just moved into their brand-new office space and spent their time during FedEx Day setting up their new office. The team is super excited to finally have their own office and show it off to the rest of DMC.

Denver

Drinkbot to Mindsphere – Otto, Jimmy C, Oliver, and Matt M.
This Denver team worked to publish data from DMC’s Drinkbot to Mindsphere. They tracked syrup, alcohol, and soda usage during Drinkbot’s operation in the PLC. They then used a Raspberry Pi to pull data out of the PLC and send it to Mindsphere. In Mindsphere, the team displayed the usage in a couple of pie charts for easy viewing of what the most popular ingredients are.

This proved DMC’s capabilities to quickly and efficiently pull data from a machine and display it in Mindsphere in a meaningful way. This also adds another level of Siemens technology to Drinkbot, which goes to many trade shows and events. It also proves our capability as a Mindsphere Integrator. Learn more about this awesome project in Matt’s blog. 

Denver IoT Coffee – Jesse J, Jim, Kirsten, and Alec
Some of our Denver team created an IoT coffee solution to allow engineers to view the amount of coffee in the kitchen from their desks. This solution also will enable people in the kitchen to see how many people were still drinking coffee in order to determine if they should make a whole new pot of coffee, or just use the Keurig.

The team created four scales with load cells to weigh the three coffee containers and the coffee in the coffee maker to determine how much coffee is in them. They then created a desktop app to display the weights.

Marble Bot – John S, Ryan L, Andrew N, Ben C, and Josh Z
On FedEx Day 2018, the Denver team started a proof of concept of the Marble Bot. This year, the goal was to complete a working version of Marble Bot. Using an HMI, a user creates a design for the bot to make. Once started, the machine will recreate the design by dropping marbles into 16 columns, building the image pixel by pixel.

The machine uses hardware, including a Siemens 1200 PLC, Siemens 7″ Comfort Panel, Siemens ET200 RIO, Siemens S120 Drive and Motor (rotator) and a stepper motor (auger).

Houston

New Houston Lab Setup – Elizabeth, Maddie D, and Gabby
DMC Houston had just moved into a new office space, so this team focused on setting up their new lab on FedEx Day. The three unpacked boxes, emptied out tiny cubbies and drawers, and organized everything into the new cabinets. They created an inventory of all the hardware the Houston office currently has as well as a wish list of hardware they need or want.

SuperViewer UI Update – Ceci
Ceci updated the SuperViewer user interface to have a light and a dark mode theme. The setting can be configured by the user and retained the next time that the application is started. This continues to improve the TDMS Viewer currently available on the DMC website. The SuperViewer is an expansion that is meant to be modular and easy to scale for customized applications.

New York

Ignition Perspective Train Demo – Paul, Zach G, and Jay
This team created a new demo for the New York office that uses the new mobile-friendly Ignition perspective module. Eventually, this project will hopefully be used as a demo that DMC employees can bring to career fairs and other events. The team wired up a model train to be controlled by an S7-1200 PLC. Using electrical relays, and by utilizing the existing controls that came with the model train set, a system was created in which the PLC could control the speed and direction of the train.

This team created an Ignition perspective project in order to communicate with the PLC. Ignition Perspective is new with Ignition 8.0 and makes it much easier to create a mobile-friendly Ignition platform.

DMC was able to successfully create a project that can control the train’s motion and speed. The user is also able to start and stop the train from this Ignition project. Check out more details here. 

React Native Ping Pong App – Serena, Robert, and Becca
The goal for this project was to port over functionality from the existing ping pong web app into a React Native mobile application with an Azure-hosted backend API. This team created a .NET Entity Framework Core application that serves as an API and is hosted on Azure. This application provides endpoints that, when hit, will interface with the database that is also hosted on Azure. The database schema is identical to the one used by the current ping pong application.

They also created a React Native application that allows users to select players for a game, start a game, and complete a game. The appropriate HTTP requests are sent to the server to update the database through the existing stored procedures in the database.

Seattle

AWS IoT Sitewise – Kevin F.
Kevin’s goal was to connect a Siemens PLC to Amazon AWS cloud using the Siemens built-in OPC UA server and Amazon IoT Sitewise. IoT Sitewise is Amazon’s IIOT edge service currently in Beta Preview. He set up a Siemens PLC to generate sample data that could be pushed to the cloud as well as configured the PLC’s internal OPC UA Server.

Kevin then installed and configured Amazon AWS IoT Greengrass and IoT Sitewise on a Linux PLC. These components were configured to talk to an AWS service and the Siemens PLC.

Unified PoolVision – Ross, Phil, Dan F, Tyler, Steven F, Jacob B, and Mike H
This team developed an easily deployable PoolVision system for detecting and identifying balls on a pool table. They leveraged cutting edge depth cameras and machine learning techniques to assemble an accurate system that can be trained through a Training Wizard that involves no preexisting knowledge of machine learning.

They also included an easily extensible, WebSocket-based backend API that can be utilized for a wide variety of stat-tracking or video-related frontends. The system is based on an Intel RealSense Depth camera and a Jetson Nano single board computer.

St. Louis

St. Louis Lab Setup – DMC STL Team
The STL team took the day to set up and organize their lab in the St. Louis office. This lab has been a work in progress and the busy St. Louis team finally got it done this FedEx Day!

This year’s FedEx Day was not only super successful but also a fun time for all the offices. We are excited for what’s in store next year and how our projects from this year help DMC as a company.

Check out the video below for a quick recap of FedEx Day 2019 as well.

Learn more about DMC’s Company Culture.

The post DMC’s FedEx Day 2019 Innovations: From Slack Bots to a Light Up Slide appeared first on DMC, Inc..

For FedEx Day 2019, a group of DMC Denver engineers decided that we wanted to add functionality to DrinkBot to track its usage. DrinkBot tends to travel frequently between offices, and we wanted to be able to stay up to date on its stock and wear from anywhere in the world. We decided that the best tool for this job would be Siemens Internet Of Things (IoT) platform, MindSphere.

Collecting the Data

Before we could upload the data into MindSphere, we first needed to collect it on our Siemens S7-1200 PLC. We built a buffer into our existing DrinkBot project to track all DrinkBot usage. This buffer would continue to fill with data until the data was pulled into MindSphere and the buffer was reset.

For each bottle of liquor, we tracked every time a shot was poured into a drink, as well as the time that the shot was poured. For the various sodas and mixers in DrinkBot, we included a tag tracking the amount of each mixer poured into the drink. We also tracked the current positions of the Siemens G120, and Siemens 1FK7 Servo Motor, and the total usage of these motors.

Transporting the Data

After collecting the usage data on the PLC, we needed to prepare the data to upload to MindSphere. Data processing was done on a Raspberry Pi 4. First, we pulled the data onto the Raspberry Pi using a Python script, and the Snap7 library for communicating with a PLC, as well as cleared the buffer.

The Snap7 library provides straightforward functions for accessing and updating PLC data. It is as simple as specifying the IP address of the PLC you want to communicate with, the data block you want to pull data from, and the byte addresses of the specific data you need

# Create a Snap7 client object
client = snap7.client.Client()

# Connect to the PLC
client.connect(ipAddress, 0, 1)

# Read data from a PLC Data Block (DB)
data = client.db_read(
    dbNumber,
    startingByteAddress,
    dataLength
)

# Create a Snap7 client object
client = snap7.client.Client()

# Connect to the PLC
client.connect(ipAddress, 0, 1)

# Read data from a PLC Data Block (DB)
data = client.db_read(
    dbNumber,
    startingByteAddress,
    dataLength
)

The MindSphere platform has many Application Programming Interfaces (APIs) for different services. For our task, we needed to create an asset in MindSphere for DrinkBot and update the different aspects of DrinkBot (i.e. soda usage). Our aspects were tracked on MindSphere as time series. We could then update these time series using IoT time series service API calls in C#. This would create a log of DrinkBot usage on the MindSphere platform.

Using The Data

Once we collected the DrinkBot data onto MindSphere, we needed to present it in some form. To do so, we created a custom MindSphere application. This application, using ReactJS, pulled all the DrinkBot aspect data, and created a pie chart displaying the usage of the different drinks by the machine over any given time period.

// Create URL to query against
const fetchString =
    "/api/iottimeseries/v3/timeseries/" +
    "{AspectId}/{Variable}" +
    "?from=2019-09-03T00:00:00Z" +
    "&to=2019-09-10T00:00:00Z";

// Since the query occurs from the client,
// grab the XSRF token from the browser cookie
const token = Cookies.get("XSRF-TOKEN");

fetch(fetchString, {
    credentials: "include",

    headers: {
        // Expect JSON response
        "Accept": "application/json",

        // Authentication token for the API
        "x-xsrf-token": token
    }
})
.then(response => response.json())
.then(responseJson => {
    // Store the received data in React state
    this.setState({
        bottleShots: responseJson
    });
});

// Create URL to query against
const fetchString =
    "/api/iottimeseries/v3/timeseries/" +
    "{AspectId}/{Variable}" +
    "?from=2019-09-03T00:00:00Z" +
    "&to=2019-09-10T00:00:00Z";

// Since the query occurs from the client,
// grab the XSRF token from the browser cookie
const token = Cookies.get("XSRF-TOKEN");

fetch(fetchString, {
    credentials: "include",

    headers: {
        // Expect JSON response
        "Accept": "application/json",

        // Authentication token for the API
        "x-xsrf-token": token
    }
})
.then(response => response.json())
.then(responseJson => {
    // Store the received data in React state
    this.setState({
        bottleShots: responseJson
    });
});

In the future, we hope to include the utilization of the motor position and runtime data, as well as expand on the dashboard to allow for greater breakdown of data.

Summary of Technology

• Siemens S7-1200 PLC
• Raspberry Pi 4
• MindSphere IoT Platform

To see how we can help you get started on a project, check out our Siemens PLC programming services and contact us today!

Learn more about DMC’s partnership with Siemens.

The post Adding Siemens MindSphere Tracking to the DMC DrinkBot appeared first on DMC, Inc..