Meeting the Demands of Modern Quality Control With Robotic Assistance
Robots are nothing new in the manufacturing industry but still hold untapped potential. Many manufacturers understand the importance of automating production processes but rely on manual quality control systems. That needs to change.
Quality checks are detail-oriented and highly repetitive, making them ideal use cases for automation. As demands for higher-quality products and more throughput rise, robots’ potential becomes increasingly difficult to overlook.
Fewer Bottlenecks
Humans can deliver a high level of reliability in quality control, but doing so takes time. As a result, inspections typically create production bottlenecks. Robots can remove those inefficiencies without sacrificing accuracy.
Modern machine learning techniques allow computers to track visual information up to 1,000 times faster than human vision. In simple, pass/fail quality inspections — such as determining if a container is full to the appropriate amount or ensuring proper alignment — this lets machine vision inspect products at the speed of production. More robust quality inspections still enable cameras to detect flaws in a fraction of the time a human analyst would.
Of course, achieving this speed requires careful implementation. Machine vision systems require lighting to maximize contrast on the object itself while minimizing it elsewhere. However, this is fairly easy to achieve through LED lighting and shielding against ambient light.
Higher Precision
Robot-assisted quality control is also more precise than manual alternatives. Repetitive tasks make it easy for people to get distracted or tired, making mistakes more likely. Robots, by contrast, deliver the same accuracy every time.
Machine vision models compare footage of the products they inspect to the same data about what constitutes a pass or fail every time. Using the same specific metric every time lets these systems significantly improve inspection reliability. Training models on examples of common flaws further boosts their precision by teaching them particular errors to look out for.
Some facilities have cut material waste by 90% by implementing this kind of automation. Their higher standard of accuracy lets manufacturers detect flaws earlier and more reliably, leading to mitigation steps to prevent waste further down the production line.
The need for this kind of reliability in quality control is also rising. As electronics get smaller, their metal components become prone to breaking if they’re too thin but may not function correctly if they’re too thick. The line between the two can be remarkably small. Manual inspections are too unreliable for this slim margin for error.
Ongoing Improvements
Similarly, manufacturers face increasing pressure to optimize their workflows as a whole. While lean manufacturing may be giving way to goals of flexibility and resilience, organizations must embrace ongoing improvements to remain competitive. Most manufacturers plan on spending upwards of $5,000 on analytics software in the coming year, but these tools are only effective with sufficient workflow data. Robots provide that information.
Whenever an automated quality control system notices a flaw, it leaves a digital record of the incident. Over time, this data can unveil larger trends, like repeated instances of the same issue, making it easier to pinpoint where processes must change to prevent future errors.
Achieving the same information richness with manual inspections would require extensive data entry. Manual data entry can result in 40% of records containing errors in an average calibration process, making these methods unreliable and time-consuming. Automating data collection through robotic sensors is faster and more accurate, as it removes the error-prone human element from the equation.
Software robots take these benefits further. Machine learning engines can analyze data from quality inspection robots to produce actionable insights on where and how to improve. Automated systems are typically better at spotting trends in large, complex data sets, so they’re preferable to manual methods in this analysis.
Mitigated Labor Shortages
If nothing else, implementing robotics in quality control will help manufacturers overcome persistent labor challenges. Despite major improvements over the past few years, durable goods manufacturing is still short 616,000 workers, and the unemployed workforce isn’t large enough to fill every open position.
Recent advances in machine vision have made robots reliable enough to fully automate quality control checks. Earlier versions produced too many false positives or lacked flexibility for less predictable errors. These issues aren’t as prevalent now that there’s more manufacturing-specific data to train these models on and machine learning techniques have improved. Consequently, manufacturers can assign this work to machines.
Automating repetitive quality control tasks lets manufacturers employ their human workforce elsewhere. Facilities can then sustain higher output without sacrificing inspection quality despite being unable to find more workers.
Modern Quality Control Needs Robotics
In years past, robots were too inflexible and machine vision too limited for automated quality control to be viable. That’s no longer the case, and this shift has come at an opportune time. Quality and efficiency demands are becoming substantial workforce disruptions.
Automation is an essential step forward amid these challenges. Robotic assistance in quality control will be a key differentiator between top-performing manufacturers and the rest in the future.
Author: Ellie Gabel – Associate Editor @ Revolutionized
