A good QMS will control nonconformities by acting as a defense line, catching discrepancies and abnormalities in products before they ever reach the customer.
Manufacturers thrive in a competitive market by prioritizing customer needs: price, quality, and delivery. However, long-term success hinges on quality, which ensures products perform as expected from manufacturing through to post-sale. While price is influenced by costs and market dynamics, and delivery by productivity, quality is key to building trust and customer loyalty.
Higher volumes of new batteries and faster turnaround of recycled materials from those batteries—along with testing of used and repurposed ones—demand the most efficient quality-inspection approaches possible.
EVs could represent 45 to 58 percent of all vehicles by 2030, with the lithium-ion battery market expected to grow over 30 percent annually. The question is whether battery quality can keep up with this surge.
John Martin, AM Research Director, America Makes, and Christine Bernat, Associate Director, Standards Facilitation, American National Standards Institute, describe how the latest standardization roadmap is affecting the industry, trends with additive standards, and how the wrong type of inspection can prevent you from reaping the benefits of additives.
The hype around "big data" has mainly aimed at niche market sales without delivering expected benefits. Similarly, the business world's obsession with Lean Six Sigma has shown minimal returns on investment. In contrast, over the last 25 years, I've leveraged small Excel files to significantly cut costs and increase profits, often by millions of dollars, through a reliable sequence I discovered.
As robotics use grows throughout processing, packaging, and logistics environments, it’s important to understand your options — and how to keep up with safety guidelines.
As robots gain prevalence in manufacturing, emphasizing their safe use is vital. This includes understanding safety features, challenges, and best practices across all robot types, such as industrial, collaborative (cobots), autonomous mobile (AMRs), and humanoid robots, to navigate their complexities effectively.
The push for smaller, more complex device components has spiked the need for precise, non-damaging metrology, with 3D X-ray microscopy (XRM) leading the way. This technology offers high-resolution measurements critical for quality control in the electronics and manufacturing industries.
Machine vision projects often face challenges such as slow progress, difficulty in getting quotes, cost overruns, and unreliable operation. These issues require recognizing and adapting to the unique nature of machine vision projects compared to other types of projects.
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.