Thanks for that, Ann. In another interesting piece of news, the productivity data released yesterday for the first Q of 2012, overall productivity fell 0.5%. However, manufacturing productivity rose 5.9%. Manufacturing in the U.S. is on a tear.
Rob, a recent study says that a growing number of large US manufacturers are looking at onshoring: bringing production back to the US. Although this article discussing it highlights plastics, the study is about all manufacturers:
That makes sense, Ann. A larger company will realize the incremental savings and improvement much more quickly. This is part of a trend in U.S. manufacturing where automation and the cost savings that goes along with it is helping to make U.S. manufacturing more competitive worldwide. Newsweek has a cover story this week on the efficiency of U.S. companies.
Rob, one of the main reasons companies, especially smaller ones, put off investing in machine vision is the initial expense and hassle. They are not necessarily less expensive than human inspection except at the large scale. The cost differential in smaller systems seems to depend to a large degree on how much money they save in product returns and wasted materials from defective products.
That's good to hear the MV systems are being used for improvement and not just a matter of replacing humans with a less expensive solution. There seems to be a lot of automation replacing humans lately. It will be interesting to see how IBM's Watson does in medical diagnostics.
Rob, automated MV systems are always superior to the human eye in speed. Ditto for accuracy when programmed correctly. Not all MV systems are for production lines, and the type and function depends on where on the line--or where else on the floor--the system is and what type of inspection it's doing. Then variety can be surprising. That said, MV systems are often, if not usually, put in place to go with existing automated systems, so have often been an afterthought. But that's often for larger manufacturers. Some smaller manufacturers aren't very automated, if at all, but install MV systems of some sort to improve quality control.
Tool_maker, you made me laugh. Yes, I assumed that either people or automated machine vision/inspection systems actually test parts. Having covered the latter subject for a few years, though, I did hear a lot of stories about companies not doing a very good job of either designing good inspection procedures, or of using the data their inspection systems provided.
Ann, your comment indicates that you think QC personel actually test parts. Too many times process and paper work are the only things checked. I was involved with a major project concerning a bearing shield. The customer kept rejecting the parts until they were in a "line down" condition and a company engineer came to our site to solve the problem.
We personally checked over a thousand rejected parts, but could not find one which was out of tolerance. However, the incoming QC would not approve the shipment because the dimensional variation, although always within tolerance, was great enough that an SPC chart indicated the process was incapable of producing good parts. It was only when the customer's engineer got hold of his plant manager and explained that all of the assemblies sitting on his idle production line could be completed with parts that had been 100% inspected and found to be within tolerance that we were able to ship the parts.
My point, if the inspection procedure is flawed, it does not matter whether the parts were inspected or not.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.