Test & measurement
Improving operational efficiency
Traditional machine vision solutions wreak havoc on operational efficiency and the industry is ready for change. As the late Japanese industrial engineer Shigeo Shingo once said, “Improvement usually means doing something that we have never done before.” Here Miki Gotlieb, VP of Operations at Inspekto, explains how Autonomous Machine Vision impacts operational efficiency in a way that has never been seen before.
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raditional machine vision solutions, which have been around since the 1980s, are implemented in a project-
based approach. The process requires the selection and acquisition of numerous components, including cameras, lenses, lighting, filters, computing platform and software from a range of suppliers. The QA manager is therefore subject to the lead times of the vendor of each piece of equipment — while the resulting overall lead time is governed by the long lead item (LLI). Once the integrator receives the
components, they can then integrate them with one another, alongside the tedious task of preparing the software selected and then building a hard-engineered solution on the shop floor, before beginning the software training process. For a simple application, implementation can take several weeks to a
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few months. For a more complex solution, typical time frames are several months and, in some cases, go up to an entire year. In addition to these lengthy waits, since
traditional machine vision solutions are specifically designed for a location on the production line, it is nearly impossible to move the solution from one location to another. Alongside this, the diagnostic tools for a traditional machine vision solution are often unique too, which means they are an expensive investment. In most cases the manufacturer is unable
to diagnose a fault themselves, due to a lack of machine-vision specific knowledge and must instead rely on the integrator for assistance. This inability to address the issue immediately in-house delays manufacturing, which may have to be halted while the fault is addressed.
To further reduce the downtime should a machine vision solution become faulty is to stockpile spare components. A factory with several system configurations will keep spares of all unique system parts in order to achieve a decent MTTR and minimise the downtime in case of a malfunction. This increases costs and effort for the parts which must be maintained and managed.
A new wAy Of wOrkInG Since the launch of the Autonomous Machine Vision (AMV) first product entries at VISION 2018 in Stuttgart, these challenges can be completely overcome. While AMV systems can be purchased for 1/10th of the cost of a traditional solution and installed without the help of an integrator 1,000 times faster, they also bring about a vast array of operational benefits to the manufacturer.
February 2019 Instrumentation Monthly
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