If your manufacturing organization has grown beyond a few locations, it’s critical to get everyone on the same page and keep them there. Learn how to standardize operations and reduce complexity across all your facilities.
X-ray fluorescence is an elemental analysis tool that has been a mainstay of test labs for decades. A versatile NDT method that demands only minimal sample prep and can be run by novice operators, it is perhaps most valued for delivering accurate results quickly. Today, evolving XRF capabilities are moving this quality assurance workhorse into critical new roles in a widening spectrum of industries.
The process of analyzing gage variability is often highly structured, involving an examination of the gages themselves for sensitivity to temperature changes, magnetic fields, and other factors. These are the easy ones. The second area of variability has its source in gage operators themselves, who may have different levels of training, experience, fatigue, and even attitude.
Quality professionals know the value of good measurement systems. They know that without trustworthy, high quality data you cannot make good business decisions. Unfortunately, most business people and many engineers don’t understand this value.
Most improvement projects have a goal—like reducing defects. Teams often want to jump in and start gathering data so they can solve the problems. Checking measurement systems first may seem like a waste of time, but a Gage R&R study is a critical step in any analysis involving continuous data.
There’s a popular and effective exercise, taught in business schools everywhere, called a SWOT analysis. The concept is to analyze the entirety of a company, organization, or institution by listing its Strengths, Weaknesses, Opportunities, and Threats.
New applications in quality control and the development of novel fluorescence probes, particularly those based on expressed proteins, have greatly increased the sensitivity requirements of fluorescence spectroscopy.
On Demand As part of their product quality planning cycle, companies collect dimensional measurement data for manufactured parts to better understand their production process and to perform continuous improvements. But how can this data be trusted to determine the stability and capability of a manufacturing process, as well as its ability to meet dimensional requirements?