Conversations around quality in metal additive manufacturing often focus on the flashy application of high-frequency, in situ, real-time monitoring systems and the neural networks or machine learning required for map-reduction of the mountains of data generated. There is, however, an often-overlooked aspect of consistently making high-quality parts: calibration.

Standards and regulations rule the aerospace industry. Inspections and reviews are crucial to passenger and flight safety, but compliance with these standards can become time consuming and expensive. 

Looking back at my quality career since 1984, I remember contributing to a quality manual of a Motorola Division in 1987-88 for ISO 9001 certification. 

Manufacturing often involves the fabrication of products that are made up of multiple smaller parts or components. Assembling these parts into finished products can be complex and labor intensive. 

The American Society of Mechanical Engineers (ASME) has published a new standard that identifies elements that will impact measurement uncertainty from thread calibration processes. 

Nondestructive testing (NDT) dates back to the late-1800s, to the railroad industry. The “Oil and Whiting” method of crack detection was used to find damage in heavy steel parts.

The world of machine vision imaging is largely performance driven. We’re looking for features on a part, finding edges, gaging, etc. 

If you subscribe to Quality, it seems safe to assume that quality is a priority for you. But even when it is a priority, achieving high levels of quality is an ongoing challenge that requires effort from every member of the organization every day.

There’s no question about this column. I accept the blame for what appears in this monthly effort for better or worse. This column is all about the standards I often refer to in my rants. I frequently encounter folks who question the information these standards contain and sometimes the question is valid but there are ways to challenge or change technical details within them. 

Machine vision systems consist of several component parts, including illumination, lenses, camera, image acquisition and data transfer, and image processing and measurement software.