Quality control in manufacturing and engineering relies heavily on compression force testing to determine material strength, durability, and stability. This article highlights the key uses and equipment needed for such testing.

Time series analysis enables manufacturers to track quality data, revealing patterns, trends, and anomalies to maintain consistent production standards. This method can be applied to daily production output or hourly quality measurements.

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.

In the realm of quality control, utilizing predictive analytics through Minitab's tools can transform proactive quality management.

Quality professionals use deep learning AI to quickly identify and prevent significant product defects, bringing a substantial leap forward in quality assurance and process improvement. Unlike traditional automation controllers, deep learning allows computers to proactively grow their knowledge base and adapt to evolving circumstances.

In-process verification uses high-tech tools like 3D scanners and laser trackers on the factory floor to monitor products as they're made, allowing instant analysis and immediate problem-solving.

Reverse engineering is the process of taking apart a product to understand its design and functionality. This knowledge is helpful for creating similar products or improving existing designs.

This article explores the transformative impact of incorporating rotary stages into coordinate measuring systems, enhancing their capabilities and the efficiency of the measurement process.

DOE is a method that helps manufacturers improve processes by understanding the relationship between factors and the output. It involves defining the problem, selecting the right design, conducting the experiment, analyzing the results, and implementing changes.

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