Check out the May 2021 edition of Quality, feature additive manufacturing, GD&T's new rule, force testing, software development, best practices and much more!
Shane Collins often hears people say that there aren’t any additive manufacturing standards. This is frustrating because he’s worked on additive standards for more than 10 years and there are dozens out now.
Cylindrical ring gages have a number of inspection purposes. They are used as Go/No-Go gages to inspect the outside diameter of a shaft or rod. They are masters for dial bore gages, internal micrometers and coordinate measuring machines (CMMs).
Over the past 50 years, computer programming languages have made several significant advancements all focused on making a computer easier to use in a more human-readable format. If you wanted to learn programming in the early days you would have had to learn Assembly Language, a set of binary machine operation codes that instructed the microprocessor how to perform each step.
The pandemic has turned the global supply chain on its head. Manufacturers of materials and finished items are under unprecedented pressure to manage a disrupted workforce, while responding to ever-changing customer demands, in many cases with profound urgency. It’s enough to make one’s head spin.
In management literature you will often encounter the concept of a best practice. However, until recently no method was available to assess a best practice.
In Geometric Dimensioning and Tolerancing (GD&T) there has been a long-standing conflict between the worlds of specification and measurement. The goal of specification is to ensure that components will function; the goal of measurement is to ensure that manufactured components meet those functional requirements.
Here we'll cover the specifics of a capability analysis and several sticking points in interpreting and taking action on information from the analysis.
In their day to day work activities, both quality and manufacturing professionals are involved in conducting audits; collecting, analyzing, and interpreting data; plus preparing a variety of reports and summaries of the data to document product quality.
When researching material for my thesis many years ago, I discovered there was no “silver bullet” for the organizational model for a continuous improvement effort. There is no single model that works for everyone; it varies from organization to organization.
I’ve been pleased to see so many organizations embrace a robust approach to quality improvement through methods like Lean and Six Sigma. There are indeed some detractors out there, but for the most part these are people that have observed failed deployments of quality initiatives.
Quality 4.0 integrates the features from Industry 4.0 with traditional quality tools to achieve operational excellence, improved overall performance, and innovation. Quality 4.0 combines people, processes, and technologies to accomplish these goals, along with complete digitalization of quality management systems theory.
Manufacturing industries have been striving for years for a robust quality control solution which can reliably sort out bad parts of the dispensing process without causing much production downtime. What the industry craves is a robust 3D solution that provides a 360° view of the bead regardless of the dispensing direction.
Collaborative robots helped manufacturers keep production lines running during the pandemic. They're accessible to small- and medium- sized businesses. And their simplicity just may help to shrink the skills gap.
Collaborative robots, or cobots, help humans and robots work together safely. Small, medium and large companies are increasingly choosing these human-friendly versions over traditional industrial robots, which are complex to use and are typically relegated to safety cages.
Artificial intelligence, machine learning, and deep learning are interrelated concepts involved with computer-based learning from vast amounts of data – and then making predictions based on that information. This article will show how these technologies can provide good alternatives to traditional image processing, and how software works to make this happen.
A smart sensor is simply a sensor that can communicate to provide real-time diagnostic data and adapt based on that data. The data can be used to optimize a system, reducing costly downtime and increasing quality.