How AI and Machine Learning Are Revolutionizing Quality Control

Machine learning promises to revolutionize quality control by automating the worst and most tedious parts of the process. AI has been used for a while, but now it's ready for prime time...is your business?

Workers and specialists can then focus on other things, such as product improvement and iterations. AI can speed up quality inspection while reducing cost, using machine vision to inspect parts with more speed and accuracy than a human can achieve. But it holds other possibilities as well.

Predictive Quality Analytics: Harnessing AI to Foresee Defects

Predictive analytics is set to be a game changer for improving quality. Many companies have large amounts of data on quality that isn't put to good use. With AI, you could sort and analyze this data and ultimately develop tools that foresee issues in advance and notify workers.

This would allow human inspectors to focus on areas that are a problem, or further use of AI to check "problem" areas. It would also allow you to look at potential issues. For example, if defects commonly show up when a part is stamped, you could analyze the stamping machine and see if it needs to be repaired, upgraded, or replaced.

Predictive quality analytics require AI to crunch the data involved. It combines well with Lean and Six Sigma management techniques to encourage continuous improvement, with the AI spotting patterns as soon as they occur and potentially detecting anomalies in your entire ecosystem, such as supplier issues.

Automated Defect Detection and Response Loops: AI at the Heart of Detection

Machine vision has been used for defect detection for many years. Traditionally, a human QA expert would tell the computer what to look for and flag. However, this only works when the difference between a good and bad product is obvious.

AI could learn which aspects are important and create its own rules, which are often better, and a human supervisor could always bring the AI back on track if it does start creating bad rules. These are deep learning models that collect data and are then trained to create a specific model. You can train these models on your own data if you have it, or start with data sets created by similar manufacturing processes. Data sets don't even have to be industry specific to make a good starter point.

When the AI flags a product incorrectly, this can be used to provide feedback and improve the algorithm. If the AI misses a defect, it can be provided with that data. Human inspectors should still supervise the AI, but most of the process can be completely automated.

Process Optimization: Enhancing Efficiency and Reducing Waste

Quality Control is designed to prevent defects from escaping into the wild as well as gather historical data, and ensure continuous improvement activities are working. AI could reduce feedback loops for continuous improvements to near zero. This has been a dream for many years, but machine learning could make it a reality. With this increased efficiency, waste of both product and employee time is reduced significantly and Quality Control efforts continue to improve.

AI could be used to analyze QA processes themselves to explore when false negatives and false positives occur and help develop processes to reduce them. If you have to do a recall, AI could look at the circumstances and help you establish the cause.

Machine learning feedback and predictive analytics would also work towards optimizing your process, especially in continuous improvement situations.

Case Studies: Real-World Applications in Industry

AI-based quality control is showing more and more real-world applications.

Let's take the location of milled holes in bumper beams. The hole locations are tightly toleranced and need to be in the same location. However, due to the inevitable random variation, normal feedback loops tend not to reduce variation to the levels the customer would prefer. Machine learning allows this feedback loop to be automated and, better than that, adjust tolerances on the fly. This improves production flow and reduces waste. If your company has a similar problem, a ML model may help as long as you have the right data...which is always key!

Even food could benefit from machine learning. Machine learning models, particularly Logistic Regression, could help identify the quality of milk and ensure that the highest quality milk is bottled for consumers, as well as helping dairy farmers identify factors impacting the quality of their milk and thus substantially improve waste as well as potentially supporting animal welfare.

Raising Awareness: How AI can Manage Tasks and Facilitate Awarenesss from Results

When a quality problem goes unnoticed, it can rapidly turn into a quality catastrophe. AI can provide results and reports, sending them automatically to the right people, so that human experts can spot quality problems early.

AI can also produce reports and graphs that can be shown to stakeholders in order to support investments in new equipment or personnel. It can be used to raise awareness both with leadership and shareholders so that quality problems can be fixed quickly and investments directed in ways that continue to improve quality over time.

AI Freedom: Your Engineers' Future

Having engineers do the tedious work is not a good use of their time. By using machine learning to take trivial variation issues and set them up as active feedback loops, engineers are presented with a data model that allows them to work on equipment and process improvements that impact the company's financial future in a positive way. Engineers could use their creativity to build new processes that will be better and faster, or spend more time developing new products.

Implementing AI for quality control does, of course, have its challenges. Machine learning algorithms need to be fed good initial data, and if you have not been garnering your own, datasets can cost money. Employees and stakeholders may also be skeptical...employees afraid they will be replaced and stakeholders concerned that the automated system will not do as well.

Nonetheless, if you can overcome these challenges, AI-based quality control could greatly reduce costs, improve quality, and help protect your company from recalls and liability.

Want to dive deeper into how AI is reshaping quality and engineering?

Join us at the PeakAvenue Conference on September 23-24 for expert insights in our panel “Will AI Revolutionize Your Engineering and Manufacturing Process?” and the hands-on “Innovation Workshop: AI & eQMS.”

Explore our agenda, save your seat and be part of the AI revolution.