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Data and Information Rich – Closing The Loop

Data Rich and Information Rich

One of the long-standing challenges of managing a 100% data sample is being data rich but information poor. In today’s modern manufacturing facilities, the fast cycle times and large data sets can often paralyze the people responsible for managing the overall quality of the parts produced. This data overload is compounded as companies lose skilled workers to retirement or attrition. The talent and experience gaps create a very reactionary culture in many facilities where the human resources can only react to critical quality alarms. Plant floor personnel have less and less time to be proactive and have even less time to perform the data analysis required to search for trends in common cause and special cause variation data. Advances in machine learning and artificial intelligence can aid modern manufacturers by automating the analysis for users and eliminating the reactive approach to quality spills.

Automatic Plant Floor Analytics

It is now a reality for companies to leverage breakthroughs in data analytics to identify root cause faster. Tools like the Argus software from Perceptron act as ‘engineers in a box’ crunching countless calculations behind the scenes while production runs uninterrupted. Now the large data sets get processed in real-time, with the software searching for correlations, upstream and downstream contributors to process variation, and special or common causes to inform quality professionals with suggestive answers to the problem, instead of merely pointing out there is an issue.

Data Rich Environments – Perfect For Machine Learning

The amount of data generated during every production cycle in a manufacturing plant can be staggering. The data is often pristine with absolute accurate systems measuring in the 100-micron range every second, every cycle. Incorporating a machine learning layer is as simple as purchasing a software package and database that can handle all the data inputs in real-time. From there, the machine learning models will take over, looking for trends in the data based on existing measurement data and CAD information to notify operators of what is happening to their process and product. This is in stark contrast to the traditional methodology where plant personnel are alerted that there is a problem, not what the problem is or how to potentially solve it.

Use Machine Learning and AI To Augment Not Replace

At the end of the value chain it is still people that perform most of the corrective actions based on the information provided by these new proactive systems. Think of the efficiencies to be gained in facilities if the workers can skip the hours or, in some cases, days of data analysis required to solve problems. Now the system does the analysis and provides the guidance on where to go first: Station 5 and locating pin. Valuable human resource time gets re-allocated to problem solving and you become data rich and information rich, which is a major win-win for manufacturing.

Taking Information Intelligence to the Next Level 

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. As we covered in our previous blog, 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 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

Perceptron Argus Analysis Software

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 leaves 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 cost associated with installing a separate inspection station and ensures the point of discovery for a quality issue is early in the process before 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 a 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.

Article from Perceptron blog.

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