Why Manufacturing Is On The Cutting Edge Of Machine Learning

Why Manufacturing Is On The Cutting Edge Of Machine Learning

Machine learning is often associated with tasks like sales and marketing and has managed to gain massive traction in industries such as healthcare, but many people still don’t realize the full extent of what these tools can do. The manufacturing industry is a prime example of this. Often pigeonholed as technical, but not really advanced, manufacturers are actually on the verge of machine learning, using such software to supplement new additive manufacturing processes and bolster system function. By examining the ways manufacturers are using machine learning, we may uncover yet more ways machine learning can drive modern business.

For Accuracy And Efficiency

Among the companies that have adopted machine learning as a tool for enhanced manufacturing is Siemens, one of Europe’s largest industrial manufacturers. At Siemens, machine learning helps speed up the manufacturing process, streamlining projects so that they can be completed in one stage and with less overall waste. Less waste also means greater production yields; Siemens has also found that by using machine learning along with 3D printing data, that they can eliminate much of the pre-manufacture trial and error process.

A Prevention Program

Among businesses that have undertaken to learn about machine learning, a leading priority has been to use this newfound knowledge to enhance mechanical functions and avoid system failure. This is as important in huge factories as it is for small operators, perhaps even more so for small businesses that don’t have the financial and material buffer to support downtime.

With this issue in mind, machine learning innovators have been honing diagnostic algorithms that can not only identify machine problems, but also identify their source. And, going forward, these systems will ideally help businesses establish a preventative maintenance schedule that will reduce breakdowns and minimize downtime, supporting all elements of the service economy.

Schedule Synchronization

One major challenge for manufacturers is managing production schedules, especially in shops that specialize in custom machining. With engineer-to-order products, there is no standard order, and that means machines have to be reconfigured for each run. As a result, starting projects in the wrong order can mean more urgent requests are late or that the business needs to interrupt a project to complete a rush job.

By implementing machine learning as part of the order process, businesses can optimize production workflows based on different order requirements. This may mean producing shared parts from two different orders simultaneously, reordering orders as needed, or making other changes. Historically, this would have required constant review of orders by staff members and been generally time-consuming. With the right machine learning system, though, such optimization can happen entirely in the background.

Manufacturers are committed to implementing innovative systems, and this makes the industry an exciting frontier for those interested in artificial intelligence, 3D printing, and machine learning. These tools can make expensive custom projects affordable, make small operations more productive, and increase profit margins. For big businesses, this is icing on the cake, but for smaller operations, the understanding of and investment in machine learning technology could represent a critical competitive advantage.