PRIMA Additive Manufacturing Pilot Adaptive Control Technology

PRIMA Additive Manufacturing Pilot Adaptive Control Technology
Summary

Prima Industrie is a world leader manufacturer of laser machines for metal components. Prima has recently started with a new activity in Additive Manufacturing systems, investing in both technologies of metal powder bed and direct energy deposition. The scope of this use case is to enhance process monitoring and control for producing metal components and make Additive process more productive and robust.


The next generation of manufacturing systems will be based on advanced processes, where additive manufacturing (AM) leads the technology trend. If AM has done a first quantum leap shifting from prototyping to production, the second relevant leap is now directed to make series production a reality. Next Generation of AM systems go towards a cost-effective solution by embracing a more robust process and higher process quality. To reach this aim, it is important to improve monitoring and control aspects towards process reliability and ZDM in terms of part dimension, surface roughness, part quality. Improving the real time monitoring and closed loop control can lead to reduce development time, enhance part quality and reduce waste and cost of production.


In this pilot, additive manufacturing machines for powder bed and direct deposition will be considered to enhance process control for producing metal components. Traditional solutions and new concepts of machines will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform and control loop approach. In laser-based additive manufacturing, production time has a great influence on the economic efficiency of the production process. To increase the productivity but also reliability of such processes, a zero-defect AM strategy is targeted. Starting from modular devices for real time detection of the process, it will be possible to collect data, deploy new parameters to adapt the machine control to the actual task and communicate data at management level, where not only the single machine is considered; approach will consider both new systems and new concepts of machines in parallel with AQ control loops.

More information
Web resources: https://www.primaadditive.com/
Country: IT
Address: Torino Pianezza 36, Collegno 10093
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    Comment:

    The POWDER BED Additive technology will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform approach.

    Data monitored from the machine tool and meta-information generated by different applications running at edge level will be collected and elaborated by the data analysis tool to extract useful information to be sent to the decision support system.

      Comment:

       Main technologies that will be adopted in this pilot:

      • Augmented reality (TTS)
      • Sensors system for process monitoring (FRAUNHOFER)
      • Digital twin (TTS)
      • Data analytics (SYNESIS)
      • Decision support system (ATLANTIS)
      • Secure updates sharing with Blockchain tech (EGINEERING)
      Comment:

      The ambition is to create a modular monitoring and control system that can be used with many different sensors and process models. The models need to be adaptable to the actual task, for a specific geometry or dedicated material processing conditions. Real-time process and machine signals need to be analysed in by machine-learning algorithms to find structures and pattern related to the required key quality indicators (critical defects per track, distortion, keeping of dimensions).The system will be also connected to a higher-level factory data interface which allows to exchange process information and reassign the production strategy based on additional factory conditions.

       

        Comment:

        Thanks to this new approach with modular adaptable signal processing system and a strong interaction with data space and simulation tools trough the platform, will be possible to detect anomaly and have anequipment condition reporting , reduce reject rate by application of data-driven process model that has been derived by AI algorithms, increase OEE by recommending process adjustments to the operator or directly change the parameters in real time, so to reduce also the operator costs.