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Use of Machine Learning for Engineering

The NAFEMS Engineering Data Science Working Group advocates  the use of engineering data science (EDS), including machine learning, AI, and optimisation methods to improve product design process and draw meaningful insights from data to support  product design decisions. The working group is a vendor neutral, cross-industry, international body of experts operating in the field of EDS.

We are running a short (5 question) survey in advance of the NAFEMS World Congress 2021. With this survey, we aim to learn more about the status of data science in engineering. This will help us in planning our activities for most relevancy and impact.

The results of the survey will be discussed by members of the Engineering Data Science Working Group in a panel session at the 2021 NAFEMS World Congress. A synopsis of the results will also be shared with the simulation community after the event. 

We have included example answers to demonstrate what we are looking for in terms of a response but please do not feel limited by these examples.

Question Title

* 1. Briefly describe your background. Please include details of your industry, role, background.

Example answers:
  • CAE analyst in automotive and aerospace for 12+ years. My expertise is CFD. I have a certificate in machine learning from University of X.
  • I work in product design consulting. I am a mechanical engineer with a masters in MBD. I am interested in learning more about data science.

Question Title

* 2. Briefly describe your current use of Data Science. Please include use cases, tools, methods, data type, management and availability, hardware.

Example answers:

  • 3D simulations are slow for what-if studies during the initial design step. We have been using regression models. Our data is created using design of experiments. I would like to investigate if there are better methods now. We do not have a formal data management system, but we are investigating. We have an in-house cluster for running simulations.
  • Want to learn from test data, no tools or methods are identified yet, data is stored in test system

Question Title

* 3. What are your immediate needs that would help you increase or start using Data Science. Please include use cases, tools, methods, data type, management and availability, hardware

Example answers:
  • Need to learn more to identify priorities for use cases
  • We need to develop expertise in detecting anomalies and failure identification from time series analysis.

Question Title

* 4. What are the challenges that stand in the way of you being able to demonstrate the value of Data Science? Please include use cases, tools, methods, data management and availability, hardware.

Example answers:
  • Understanding of the technology within the company
  • Organized data for good use cases
  • Not my primary responsibility but something I do on the side

Question Title

* 5. The NAFEMS Engineering Data Science Working Group are forming a community that is open to everyone with an interest in EDS. No expertise is needed. If you are interested in joining the community please visit the Technical Communities section of the NAFEMS website . 
You will need to create and sign into the NAFEMS website in order to join the  EDS Community. 

If you do not want to signup for the community but would like to be invited to the first community event please leave your email address in the box below.

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