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Most of us have smart phones, some may even be addicted to them, but have you given any thought to how these products are manufactured? The average consumer might believe that this technical marvel that has become ubiquitous must certainly be manufactured by high tech equipment. The truth is, that while component assemblies are manufactured in this manner, final assembly remains a manual process performed by contract manufacturers. China remains the leading producer of high volume consumer electronics due to readily available supplies of both components and labor.
So how does a US-based manufacturer, for example, ensure product quality when final assembly is in China? One way is for engineers from companies such as Apple, Microsoft, Google and the like to rack up significant air miles regularly traveling to and from China to monitor production and troubleshoot problems. Two such former Apple engineers are looking to change this paradigm. Startup venture Instrumental has launched a solution that applies machine learning to the assembly line. The solution combines inspection stations equipped with a hi-def camera and lighting with machine learning software that enables engineers to remotely inspect products. The inspection station takes lots of images of the product as it progresses through the line while the software enables engineers to remotely search and compare images in a first pass at issue identification and correction. The company claims the solution is suitable for in-process work on large and small data sets. It is not a real-time solution, but can save significant time and resources. The company continues development work on pass/fail on line capability.
Potential Instrumental customers are the designers and sellers of products. In turn, these companies install the systems at the manufacturing site. To date, the company claims client success in the consumer electronics manufacturing industry with installations at Foxconn and Flex among others. Initial use cases involve issue discovery and traceability. For discrete manufacturing, the application of machine learning has the potential to be another tool in the quality assurance toolbox.
So how does a US-based manufacturer, for example, ensure product quality when final assembly is in China? One way is for engineers from companies such as Apple, Microsoft, Google and the like to rack up significant air miles regularly traveling to and from China to monitor production and troubleshoot problems. Two such former Apple engineers are looking to change this paradigm. Startup venture Instrumental has launched a solution that applies machine learning to the assembly line. The solution combines inspection stations equipped with a hi-def camera and lighting with machine learning software that enables engineers to remotely inspect products. The inspection station takes lots of images of the product as it progresses through the line while the software enables engineers to remotely search and compare images in a first pass at issue identification and correction. The company claims the solution is suitable for in-process work on large and small data sets. It is not a real-time solution, but can save significant time and resources. The company continues development work on pass/fail on line capability.
Potential Instrumental customers are the designers and sellers of products. In turn, these companies install the systems at the manufacturing site. To date, the company claims client success in the consumer electronics manufacturing industry with installations at Foxconn and Flex among others. Initial use cases involve issue discovery and traceability. For discrete manufacturing, the application of machine learning has the potential to be another tool in the quality assurance toolbox.