Metal AM Winter 2020

Page 134

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Machine Learning and AM

Analytics of yesterday F-F collision No helmet F-H collision Overspeed Line cross



No helmet warning detect in camera zone C, at time: 17:35:38 No helmet warning detect in camera zone G, at time: 17:35:36 No helmet warning detect in camera zone F, at time: 17:35:20 No helmet warning detect in camera zone E, at time: 17:35:19 No helmet warning detect in camera zone F, at time: 17:35:07 Line cross warning detect in camera zone B, at time: 17:34:52

Fig. 9 This image shows the dashboard of the BGS tool, where we can see the area where there is a non-compliance which puts out an alert in real time

and make instant notifications for human intervention. To do this, the company employs deep learning technology, a form of supervised learning. BGS digitises the environment, including health and safety considerations, in manufacturing. It is effectively a real-time safety solution that can be trained to assist in complying with local, national, and international safety standards. The use of ML allows digitisation of current safety operations, but in the process also creates a tool for virtual safety audits (Fig. 9). Updates can then be incorporated instantly to advise human observers as new threats emerge. In line with today’s coronavirus (COVID-19) safety measures, for example, wearing a face mask would be important, and compliance can be measured quickly. As BGS puts it, “These digital updates to safety standards, machine specifications and required training are of great importance to advanced manufacturing technologies like Additive Manufacturing, where the technologies continue to evolve at a breakneck pace.”


Design Finally, ML can fully close the data feedback loop by influencing new designs to be fully adapted to the AM process, as well as fully leverage its benefits. With poorly-adapted design arguably being the Achilles’s Heel of this technology, it is becoming an increasingly important requirement that the manufacturing process begins not at the part build, but at part design. This will be achieved by harmonising several ML approaches’ results to feed back into step zero of manufacturing operations: Design. Materials, process and simulation data that advise producibility, thereby value proposition, could ideally be likened to specific features that comprise a mechanical design engineer’s toolbox. Quality monitoring could mature from simple pass or fail metrics to a dynamic rating that indicates the best inputs to achieve the best outputs. Indicators sophisticated enough for design advisory will likely require all the types of output vectors, intelligently weighed to properly serve the described purpose.

Metal Additive Manufacturing | Winter 2020

Summary of benefits for tomorrow We see the ultimate benefit of Machine Learning in AM as the reduction of guess work for more databased decision making. The current approach for most companies is a lengthy confidence-building process with static data points, which is not only costly, but tedious and difficult. AM is complex and multi-disciplinary, and humans can have difficulty taking in many data fields coupled with time, temperature, movement, and materials kinetics. ML will utilise the copious amounts of data generated during the AM process for training the algorithm in real time. We see the main benefit of this as increased confidence, which then manifests in programme savings which will speed up the industrialisation of the technology. As we have described, ML is used in the workflow preceding and following the build event. Before building, it is present in design and process robustness analysis. Following the build process, it is

© 2020 Inovar Communications Ltd

Vol. 6 No. 4

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