3 Areas Where AI Can Boost Productivity Of Mechanical Engineering

Artificial intelligence (AI) apparently has become a convenient approach among emerging technologies and has consequently reached the traditional sector of mechanical engineering. In an article published at German magazine Produktion (production) Accenture gives an overview and describes how huge productivity gains can arise and mechanical engineers can open up for them.

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Up to 39 percent higher return on sales by 2035: This is the quantified thrust that artificial intelligence might have on machine and plant construction. Due to Accenture, AI not only allows remarkable efficiency gains but also enables new growth.

Algorithms For Workflows And Decisions

Technology is largely ready for the market; first experience and best practices do exist. Even mechanical engineering companies are able to tap significant benefits through the use of data, machine learning and other AI procedures. Algorithms can speed up processes or take over completely, help employees access knowledge and relieve them of making decisions. Used correctly, AI opens up new approaches in data analysis and may enable a development of novel, much improved industrial products with considerable added value. In practice, following Accenture, these are the most important scenarios in mechanical and plant engineering:

Smart Data Recovery
Smart Working
Improving Smart Products
Complexity Low Medium High
Known / Common Tools
  • Digitized data management and workflows
  • AI algorithms or services (machine learning / – vision, speech processing)
  • Chatbots and “voice assistants”, co-bots,
  • AI-based apps e.g. Robotics Process Automation
IBM Watson, Microsoft Cortana, Google Assistant, Amazon Alexa …
Prerequisites
  • IT-supported data management
  • Willingness to use cloud technology and Analytics-as-a-Service / comparable on-premise solutions
  • AI algorithms or services (machine learning / – vision)
  • IT-based data management
  • Digital management of knowledge
  • Digital mapping of work processes
  • Technical requirements for co-bot use
  • Digitalized data management and workflows
  • Availability of Analytics / IIoT / Digital Twins / – Thread,
    AI algorithms / services (machine learning / – vision, speech processing)
  • AI-capable IT i.e. IaaS and PaaS services.
Description Algorithms for smart data collection and evaluation:

  • Image recognition
  • Big data analytics
  • Machine learning = integral part of simplest IoT solutions – only one could unlock full value contribution of smart AI exploitation.
  • Completely different information awaits targeted recycling
  • Discovers patterns
  • Detects deviations
  • Improves decisions
  • Significantly increasing effectiveness and efficiency in purchasing, planning, human resources, finance, and sales.
Use of AI to simplify or complete routine tasks.Trained AI to help employees to work independently. AIs are getting better and better. Use of “supporting” AIs is possible for internal processes e.g. manufacturing. In industrial products and solutions, AI increases their utility – opening up access to new revenue and business models and additional revenue.
Smart products Helping a large manufacturing company significantly strengthen its own sourcing through Big Data and Artificial Intelligence. Helping to understand task and context-specific access to knowledge.Co-bots: Robots learning simple manual operations to take over them – extremely flexible. Already implementable:

Included as new user interface:  Industry solutions recognize their respective users, learn way of working, preferences and intentions.

1st step: Use of speech recognition solutions in industrial products, for operating industry software.

Self-configuring machines e.g. to adjust themselves. Self-optimizing machines ensuring a production line independently improving own effectiveness and efficiency.

Up next:

Self-controlling machines, e.g. completely independent warehouse robots, self-propelled forage trucks.

AI Is Ready To Be Used – If A Company Is Prepared

With solutions available on the market companies can already implement applications of most of the scenarios. In order to get all benefits of the new processes, appropriate strategies and plans are needed. For data collection and use, for example, it is recommended: the interaction between employees and AI, use itself and for providing the necessary technology – as well as governance structures to ensure ethical AI use.

Next: Certification for Artificial Intelligence Planned

The German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) and TÜV Süd announced that they will start a cooperation to certify systems using Artificial Intelligence (AI) to develop a ‘TÜV for algorithms’. The experts will explore the learning behaviour of the AI ​​systems to be able to control its reactions. Therefore they focus on the development of an open digital platform for OEMs, suppliers and technology companies named ‘Genesis’ to validate Artificial Intelligence and thus create the basis for certification.

AI systems are becoming increasingly popular for electronics of autonomous vehicles to a large number of possible traffic situations be able to master safely. TÜV Süd experts estimate 100 million situations per fully automated driving function. Such systems do not react deterministically and for this reason not exactly predictable. They learn rather from the traffic (Deep Learning) and draw own conclusions for the ‘right reaction’ – making autonomous decisions. To act always as defined by the traffic safety, TÜV Süd will validate and certify the underlying algorithms. In the future, users of new Genesis will be able to upload their data and used Modules to the platform and get – after verification – a corresponding certificate for functional safety- OpenClipart-Vectors / pixabay.com

 

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