Safety An indirect application of ML in AM is in the field of safety. Today, we employ cameras and other detection devices for safety and security monitoring, but this approach is not truly effective in preventative safety, only offering event data after the fact.
Be Global Safety (BGS) has employed ML to train its algorithm to spot unsafe situations 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.”