There is a school of thought in manufacturing that quality inspection is not necessary to build a high-quality vehicle. When major programs are launched, the focus is on building the part, and often the inspection systems are last in line for installation and commissioning. Additionally, when budgets are trimmed, inspection dollars are the first to be thrifted or re-allocated. When this happens, the plant engineers pay the price. They heavily rely on inspection data while refining the process and tooling during launch and later when they scramble to troubleshoot line stoppages. One way to ensure that everyone is served by your manufacturing strategy is to combine the build and inspection into the same operation. In closing the loop, Data-Rich and Information Rich, Artificial Intelligence (AI) plays an important role in augmenting the automation of data analysis. This same concept applies to building parts using AI and machine learning to make adaptive robot guidance even more powerful for modern manufacturers.
The Rise of Robot Guidance Solutions (RGS)
In the late 1980s, Perceptron released the world’s first robot guidance system. The system used a laser-based machine vision to measure the opening for a windshield then guided an industrial robot to center the windshield into the opening. When this system was installed, a whole new industrial capability was born. Today, from simple pick and place operations to complicated best-fit panel-loading, robot guidance is a mainstay of industrial manufacturing. As manufacturers strive to automate more operations to improve productivity, they can deploy robot guidance systems to maximize speed and ensure high quality in assembly operations. RGS has become a truly versatile tool for multiple industries.
Closed-Loop Manufacturing
Robot guidance and quality work in concert throughout the modern manufacturing facility. One way these two technologies work in harmony is in closed-loop manufacturing. One example of closed-loop processing is what Perceptron termed “deck and check” when they began applying their technology to load automobile roofs and then subsequently verifying the dimensions of the roof ditches before the vehicle left the build operation. This breakthrough created an in-station process control (ISPC) strategy for loading roofs with higher dimensional quality and immediate verification of the quality of the part before releasing it from the welding station. The benefits of ISPC are significant. ISPC reduces the production line space and costs associated with installing a separate inspection station and ensures the point of discovery for a quality issue is early in the process before the significant value has been added to the vehicle.
Adaptive Feedback and Control
One of the holy grails of machine vision for robot guidance has been true adaptive feedback control (AFC). With AFC, process and quality inputs are monitored and adjusted in real-time, creating a manufacturing process that responds to all the inputs to produce an assembly that is truly custom fit to the individual parts and process inputs. Add Machine Learning to this process and you could be on the doorstep of “lights out” manufacturing with an adaptive process that learns as it builds. Utilizing the networked data and analytical horsepower creates feed-forward automation and a self-teaching manufacturing process. Harnessing this power could lead to a process that does much of the “heavy lifting” for us while ensuring the highest quality parts at the required line rates.
To know more, please check Perceptron.
