A Rose by Any Other Name…

“No Fault Forward” is the hot new catchphrase in the assembly industry. It means, essentially, that mistakes should be caught when and where they happen and not somewhere downstream in the process. If that idea sounds familiar there is a good reason.

Making perfect parts and assemblies every time has been the goal of manufacturing since day one. Substituting one catchphrase for another does not change that. Perfection, of course, is an impossible goal. But with the technologies available today detecting “bad” parts or assemblies and keeping them out of the process most definitely is not.

The key to achieving that goal is combining sensors, servo technology and specialized software to detect bad parts and assemblies in real-time. My company, Promess, has been providing this kind of in-process monitoring solution to customers for more than 30 years, helping them achieve “No Fault Forward” results long before that phrase was coined.

The key to in-process monitoring is to design the station or process with the correct feedback to allow the machine to measure performance in real-time. This doesn’t just happen by accident. It requires process feedback parameters to be thoroughly analyzed before the process is finalized and the system is built.

It’s all too common to design, and maybe even build, an assembly station “the way we always have” and then tack-on process monitoring as an afterthought. In many cases, an innovative assembly engineer would cobble together a homemade science project solution. Sometimes they even worked. But when the engineer moved on to his next project, no one could support, enhance or repair it.

As you might expect, the usual result of this approach is sub-optimal performance in detecting bad assemblies. Even worse, it virtually eliminates the possibility of improving the process by using data collected during the process to not simply detect bad parts, but actually to improve the process by adjusting it in real-time.

This requires direct intent and planning. The steps include:

1. Developing a clear definition of what constitutes an assembly that is OK to move to the next station.
2. Determining what type of attribute it is and how to measure it in the station:

1. A process attribute. A common example is found in a press fit operation where the force required to press the parts together is a clear and important parameter in determining a good or bad assembly. The force/position signature cannot only determine that the press fit interference was proper, but it can detect damaged or cracked parts, dirty parts, proper or improper lubrication and many other defects, none of which can be detected in an end-of-line test station. Process attributes like press force can only be measured inside the process.
2. A part attribute. The most common example is a physical dimension. If a part attribute has to be measured in the station that capability must be designed into the station as a primary task. It can’t be tacked on as an afterthought.

Intelligent assembly systems will play a key role in the global transition to Industry 4.0. The fourth industrial revolution will increasingly depend on data-driven assembly systems to meet global market’s requirements for ever higher levels of productivity and quality.

The tools required to make that exist today and the technology required to put them to work exist today. Whether it’s called “No Fault Forward” or some as yet unknown future buzzword, the results will be the same—more consistent products, reduced manufacturing costs and more satisfied customers. It’s time to make it happen.

Glenn Nausley is president of Promess Inc.