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New Approaches to Design as Important as New Tools

Jan. 17, 2020
Machine Design discusses how strategy is as important as technology with Philipp H.F. Wallner, industry manager for industrial automation and machinery, MathWorks.

Q: What products do you see that are currently solving design engineers problems.

Wallner: I see two key trends or challenges that come with Industry 4.0 focused on the questions of:

  • How can design engineers embrace the growing complexity of mechatronic systems?
  • How can they make use of the vast amount of sensor data generated by today’s industrial equipment?

The first question can be addressed by design workflows that enable engineers to use virtual representations (or simulation models) of the equipment to verify the behavior of the mechanical system, the electrical system, and the embedded software prior to testing on physical machinery. Model-Based Design allows users to build comprehensive multi-domain models for extensive tests in simulation, virtual commissioning, and as a basis for digital twins.

For the second question, it is important to have tools available that enable design engineers to implement algorithms based on signal processing and statistics methods like machine learning, particularly, for instance where engineers need to perform predictive maintenance or estimate the remaining useful life of a piece of equipment.

Q: Do you feel these tools are attacking the root cause of the design engineers problems?

Wallner: In a world of faster and faster technical innovations, design engineers require tools and environments that provide them with an additional layer of abstraction. With apps and predefined blocks as well as with industry-related examples, products such as our MATLAB and Simulink toolls enable engineers to prototype, verify, and implement new mechatronic systems or algorithms in shorter time.

Also, techniques for targeting different hardware platforms through automatic generation of C/C++, IEC 61131-3 or HDL code help overcome the challenge of legacy hardware dependence.

However, the right tool choice can only be part of the solution. At the end, it is also important to establish the right mindset of “doing things differently than in the past” within the company in order to stay competitive. We see this with a number of established companies in the machine builder industry. They have been effective over many years with implementing new software functionality offline and then testing it on the physical equipment.

The senior management is often averse to new design methods like simulation and AI as well as to the respective tool chains. This is why we then work with individual engineering teams and their leaders on making their projects successful in order to then help them convince their management. In addition, we work closely with universities to ensure that young engineers enter the job market with the right skillset and mindset.

Q: How do you see the future? How will products/solutions evolve?

Wallner: For the past several years, the automation industry has discussed a vision of “sample size one”—how can production lines produce one-of-a-kind goods without encountering long changeover-times or other inefficiencies? With Industry 4.0 and tools like the ones mentioned above, this vision will eventually come true and fulfill the requirement of individualization in production.

Today’s machines are set up in a fixed, inflexible manner on the shop floor—commissioned, parameterized. and tuned for one specific product produced repeatedly for months or even years. Tomorrow’s production lines must be flexible. They must be built from multiple mechatronic modules that can easily be rearranged.

There must also be an AI that parameterizes and tunes machines according to the next, individualized good manufactured on the line. AI-based systems are acting based on experience or data respectively, quite similar to human beings. This makes them way more flexible than hardcoded software programs that need reconfiguration or reparameterization every time a new product is manufactured on the production line.

AI has the potential to become a game changer for the manufacturing industry. This will go far beyond use cases like predictive maintenance that are already a reality today. AI-based systems will be in the center of tomorrow’s flexible production facilities, orchestrating machines and flexibly rearranging entire production lines while minimizing the consumption of energy and other resources.

Depending on the requirements, these algorithms will be deployed on non-real-time platforms as well as on real-time systems like modern PLCs. The rapidly increasing calculation power of industrial controllers and edge computing devices, as well as the use of cloud systems, help achieve a new dimension of software functionality on production systems.

Q: What are the buggest challenges manufacturers and engineers will face in the next decade?

Wallner: The biggest Industry 4.0 impact will affect the engineers’ skills. By relying on technology and tools from companies like MathWorks, more engineers and scientists (not just data scientists) will work on AI and Model-Based Design. Factories will require people who can build models, deal with large data sets, and handle the respective development tools.

Companies building and operating industrial equipment and machinery need to adapt their job postings to reflect this by hiring skilled engineers who come with knowledge in modeling, simulation, and data analytics and with an open mindset in order to prepare for a future in which Industry 4.0 reflects just the beginning of factory evolution.

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