Manufacturers and quality teams stand to benefit from Automated Machine Learning (AutoML). The technology can streamline their processes, boosting quality improvement, maintenance and analytical insights.

Machine learning models have great potential to help manufacturers improve quality control processes.

Progress in machine vision processing is charted in major developments that prove significant staying power.

A sub-discipline of artificial intelligence (AI), deep learning (DL) has become a breakout technology in high-profile market sectors such as retail and high-tech.

According to MasterControl’s 2022 Digital Quality Maturity Study conducted by Cicero research, the majority of quality organizations are going digital, have plans to go digital within the next five years, or believe this is where they need to go to achieve compliance and remain competitive.

Manufacturers can unearth valuable insights from test data from various sources by employing statistical algorithms and machine learning to establish patterns and predict future outcomes and trends.

In the machine vision marketplace the term “AI” typically refers to deep learning platforms that enable industrial automation and inspection. To appreciate the value proposition of AI in this context, it’s helpful to understand how the technology has evolved over the past several decades.

How you can identify opportunities for AI in your machine vision applications.

The emergence of autonomous mobile robots (AMR), which can independently transport materials around a facility using built-in Lidar scanners and 3D cameras, can make automating tasks faster, less expensive and easier to deploy overall.

In all types of industries, machine learning (ML) tools are finding the needle in the haystack of data, augmenting quality and safety professionals with a new kind of intelligence that can unlock hidden data patterns that are impossible for the human mind or eye to absorb.