As businesses increasingly rely on machine vision to enhance quality, improve productivity, and increase the bottom line, technology providers are relying more on industrial computing solutions that enable faster processing speeds and higher efficiency, or that support new tasks altogether.
As consumers demand higher-quality goods at steadily increasing volumes, the chief benefits of industrial automation — speed, accuracy, and consistency—become more important to businesses worldwide with each passing day.
In March 2023, after a GPT model passed a biology exam, Bill Gates noted on his blog that AI could save lives and address climate change. If AI can tackle such significant challenges, it can also help with issues faced by manufacturers, metrologists, and quality control professionals. This article will examine the challenges metrologists encounter and highlight potential AI-driven solutions in the metrology value chain.
Initially seen as science fiction, machine vision in manufacturing faced hesitance due to high costs and lack of awareness. However, interest has surged, shifting the focus from "Can it be done?" to "How will we do it?" This reflects significant transformative changes in the industry.
Automated processes are vital in industrial production, with robots handling finished products and sorting parts for quality assurance. Equipped with 3D cameras and machine vision, they accurately identify and grasp items from disordered bins.
Calibration is essential for maintaining quality and safety in industries like pharmaceuticals, food and beverage, and chemicals. The future looks to digital certificates and AI technology to make calibration processes faster and more efficient. This article discusses how AI is set to transform calibration.
Regulatory bodies like the FDA and MHRA are adapting guidelines to accommodate the evolving AI landscape, stressing the importance of innovative compliance approaches alongside traditional software regulations, including audit trails, electronic records, and signatures.
Integrating AI into quality control processes requires a thoughtful approach that goes beyond mere technology adoption. Here are some proven strategies to ensure successful AI empowerment in quality control.
The recent Boeing door plug failures highlight the urgent need for better quality control. Utilizing AI, particularly Large Vision Models (LVMs), offers a promising solution for enhancing quality assurance by providing unparalleled precision, efficiency, and scalability.