About the Hackathon

Look forward to a varied weekend full of challenges, networking and exciting insights into our work. Together with our staff and our partners from the International Center for Networked, Adaptive Production, you will face the challenges of Industrie 4.0 in a real manufacturing environment and use your coding knowledge, technical skill and team spirit to solve them.

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Tasks

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Partners

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Tasks

We provide you with various tasks on the topics of deep learning, 5G, digital twin, smart devices and worker assistance.

Task 1 – Worker Assistance

The Atlas Copco Group serves customers through innovative compressors, vacuum solutions, generators, pumps, power tools and assembly systems. We are a global and diverse Group of many strong brands and around 34 000 employees representing different cultures in more than 180 countries.

The Airtec division is the heart of the compressor technique business area, because in Airtec we manufacture and assemble the core elements of our compressors. In our factory we have operators working in 3 shifts, to keep the machines running 24/7. The installed base includes turning lathes, 5 axis workcentres, millturns, grinding machines and robots, mainly machining steel and cast iron parts. This makes the inside of the factory a real industrial environment, something to keep in mind during the hackathon.

For this hackathon we want to create a digital operator assistant, focusing on triggering and encouraging the operator to provide feedback on why certain machines are not running or have stopped. This feedback should then be stored together with the source event in a database for later analysis.

This will include following tasks:

  • Pick the platform
  • Origins: connecting to the source
  • End game: connecting to a sink source and back end
  • Big Brother option: connector for third party real time acces
  • The holy grail: create the best possible GIU for operators
  • The not so holy grail: create admin front end and supervisor dashboard.
  • Task 2 – Digital Twin

    The sink-EDM process is not that describable by machine data due to its specific characterization: The electrode is removing the material based on electronic discharge and you never know where material is removed. This problem can be solved with data! You are given a set of machine data that resulted from a sink-EDM process. Your goal is to allocate the quality relevant numerical values on the surfaces of the virtual CAD model of the work piece. The results should be presented in a work piece »Digital Twin«: The data points have to be visualized and help the operator to understand the process much better and thus increase its quality.

    This will include the following tasks:

  • The first step is to do some good old coordinate transformation to bring the measurements into the work piece position
  • Now do some head over heels and map the numerical data from the electrode surface to the work piece surface
  • For a better analysis of the data at a later stage, you have to decide in which data base format you want to store the data and how to ensure to store the results in an AAS data structure
  • Finally be the artist and visualize the data on the demonstrator geometry
  • Task 3 – Deep Learning

    How nice would it be to have a central intelligence in production that is monitoring and analyzing arbitrary process parameters automatically with regard to given specification limits of these process parameters?

    This is exactly where the trend is going to and you can contribute to that together with us!

    Therefore we will provide you with labelled data sets showing values of process parameters over time. The according plots and the histograms of these process data sets show certain characteristics or patterns that you shall be able to classify.

    This will include the following tasks:

  • Classify if the location and/or the scattering of a process is changing over time (labelled data)
  • Count the number of peaks in a histogram
  • Classify the kind of probability distribution based on the histogram (normal distribution or something else?) (labelled data)
  • Count the number of different probability distributions mixed together and classify them
  • Identify jumps, outliers and trends along a curve of process parameter values over time
  • Task 4 – Deep Learning

    What if you could predict the quality of the parts in production already during production?
    Then, you would only have to measure the parts that are predicted to be critical or you can improve your processes, or you could just skip the quality checks at the end. This would save resources, time and money.

    Try to get out the most of the data we will provide to you and predict the quality of the underlying parts!

    The structured data consists of columns representing input parameters and columns representing output parameters. Try to predict the output parameters based on the input parameters by regression and/or classification analyses!

    This will include the following tasks:

  • Pre-process the given data (different files representing different processes with different inputs and outputs)
  • Implement different approaches (e.g. decision trees, gradient boosted trees, neural networks) to predict the outputs
  • Study the accuracy and the computational performance of the best approach for each given data set
  • Still not enough? Can you make use of automated machine learning libraries (e.g. Auto-Kera, Auto-Sklearn) to find the best approach for each data set automatically?
  • Task 5 is coming soon. Stay tuned.

    Who are our partners?

    FAQ

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    • Who can participate?

      You can participate if you study computer science, engineering, communication design or take similar course, want to prove your skills and are at least 18 years old at the start of the hackathon.

    • When and where will the hackathon take place?

      The hackathon will take place from October 25–27, 2019 at the Invention Center. The Invention Center is located in the Cluster Produktionstechnik, Campus-Boulevard 30 in Aachen.

    • How much does it cost to participate in the hackathon?

      Your participation is completely free of charge.

    • How big can a team be?

      There should be a minimum of 2 and a maximum of 5 participants in a team.

    • Can I register without a team?

      Yes, of course! We will also form teams on site.

    • What do I have to bring?

      In any case, you will need your computer and charger. You are also happy about headphones and a power bank for your mobile phone. The other participants will certainly be happy if you bring change of clothes and something to freshen up – unfortunately there are no showers. We don't have sleeping mats and sleeping bags for you and you can bring them with you if you need them. You don't need to bring food and drinks – we'll get them for you.

    • What about the rights to my intellectual property?

      You definitely own the code! You submit the code for evaluation and allocation. You do not transfer any rights to the ICNAP.

    • Can I win anything?

      Yes, the best teams can look forward to winnings that we will announce here soon.

    • What if I have more questions?

      More detailed information will be sent to you before the event. If you have a question that is burning under your nails, you can contact us at community@icnap.de