Measurement disputes over gage calibration continue despite the results of the studies noted in my previous column.

Two-dimensional and 3D X-ray technologies are among the most useful nondestructive testing methods. They enable the inspection of an object’s internal features without having to disassemble the sample or destroy the part in the process. 

It is a cold hard fact that steel production in Europe can hardly be made economically viable unless manufacturing facilities use the very latest equipment and technology. And there is no alternative to operating 24/7.

The American Measuring Tool Manufacturers Association (AMTMA) is an organization whose members manufacture, supply, and/or calibrate precision gages and measuring instruments. If you use this type of equipment, the odds are it came from one or more AMTMA member companies.

Whether you work in a quality control laboratory at a major automotive manufacturer or are performing research at a university, it is common to encounter a universal testing machine that was manufactured before the 21st century.

Thread classes for product threads, and by extension the gages used to inspect them, can become a bit of alphabet soup. Some find the requirements confusing.

The ASQ Inspection Division Conference brought quality professionals to Louisville this week to learn more about measurement in the digital age. Keynotes by Mahr and Google provided a closer look at today’s quality challenges.

Readers of this column will be familiar with the subject of measurement uncertainty since I comment on it from time to time, as I did last month. Those readers that have not been that interested in it will certainly run across it on reports from their calibration sources.

When it comes to building cost-effective 3D vision systems, is it better to use a component-based (i.e., camera, laser, lens, brackets, calibration targets) or all-in-one (i.e., smart) approach? 

Whether an imaging system measures dimensions, verifies colors, or determines shape, the purpose of machine vision is to distinguish an object from its background.