Data visualization transforms complex data into clear insights through charts and graphs, helping users quickly spot and address production issues and make informed decisions.

An Industry 4.0 mindset and a “lights-out” style of operation is driving quality and manufacturing teams to integrate measurements and process controls more tightly. The hope is that localized, closed loops will provide great benefits, including lower manufacturing costs, lower labor costs, and improved product quality.

One of many driving factors behind this automation growth is that the convergence of multiple technologies — including AI, robotics, and machine vision — has enabled the industry to democratize automation by making it easier to deploy and operate.

The hard card, or traveler or build book, has many names. And while computerizing quality management is no longer news, that stack of papers always seems to be hanging around.

Integration of the latest artificial intelligence (AI) technologies enable even deeper insights. Users achieve more reliable interpretation of complex information in less time while boosting productivity and reproducibility.

In the quality management domain, AI undoubtedly has potential in different areas. It would be easy to think that AI could be a threat to less modern tools like statistical process control or render SPC obsolete.

Regression analysis helps quality teams improve their process standards. In simple terms, it helps these teams understand how variations in the manufacturing process affect the quality of the final product.

Quality 4.0 is still in a definition phase. In this article we present learning quality control, our vision.

Software vendors are focusing on three trends to distinguish their offerings and better serve their customers.

Industry leaders are now seeking ways to simplify processes, cut costs, and get more done with fewer people. Fortunately, the tools and technologies required to accomplish these goals are already here.