By Beth Pedersen, Staff Writer, MasterControl (Medilink WM patron)
By 2035, it is projected that artificial intelligence (AI)-powered technologies could increase labour productivity by up to 40 percent across 16 key industries, including manufacturing, according to a recent report by Accenture and Frontier Economics(1). In dollars and cents, that could equate to a $3.8 trillion increase in economic output in the manufacturing sector alone, or an almost 45 percent increase compared to baseline estimates. With that degree of growth potential, it is clear that AI has become – and will remain – an integral factor in manufacturing.
Because of its dependence on heavy machinery and equipment, manufacturing is considered a capital-intensive industry. And as such, it is particularly well-suited to the application of AI technologies.
“AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more,” says Andrew Ng, Google Brain creator and computer science professor at Stanford University(2).
The concept of AI, or machines being able to carry out tasks and processes based on previous inputs or data, has existed since the 1950s. It replicates the way the human brain works with respect to memory, perception/sensing, learning, identifying patterns, and performing mathematical calculations. It follows, then, that as our understanding of the human brain evolves, so does our conceptualization of AI. And as that happens, new applications in the manufacturing space emerge. This article provides a snapshot of just a few of the latest ways that AI is changing manufacturing.
A time-consuming yet critical task found in most manufacturing environments is the visual inspection of produced goods to ensure they meet quality specifications. Usually, this involves people manually inspecting samples taken from a production batch or lot. Depending on the size and nature of the goods produced, workers are often assisted by microscopes and other technology to more easily spot defects. Deviating product is then passed to quality managers to assess severity and scope, triage, investigate root cause, and determine proper disposition.
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