No manufacturer likes to hear that a customer has spotted a defective product that slipped through their quality checks. Yet there is only so much that manual inspections can catch. With a little help from AI, however, you can deepen, and so vastly improve, your quality control processes, “seeing” more about your product before it leaves the factory floor.
AI-powered quality control (QC) is one of the most exciting uses of AI in manufacturing, and it’s possible for manufacturers of all sizes to implement.
Going Beyond Traditional Quality Control
Despite the many advances in manufacturing technology that support your QC team, chances are the process still relies on human eyes and manual measurements. Even the best team can get tired or distracted, or simply can’t catch the microscopic flaws that digital methods can. It’s suggested that manual inspection can miss 20%-30% of faults.
That’s where AI-powered quality control is set to transform how manufacturers handle QC processes.
Modern computer vision systems can work tirelessly, able to measure accurately down to fractions of a millimeter and “see” deeper into a finished product than a QC worker can. Meanwhile, machine learning applications can use data to learn your quality standards over time, flagging anything that doesn’t match for further inspection.
How Computer Vision Becomes Another Quality Inspector in Your Team
Machine vision systems are pretty much what they sound like, a system of cameras that offer 360-degree perspectives. The system captures these images, and feeds this digital information into an AI-powered software to interpret it. Once properly calibrated and set to track the characteristics you wish, it will scan each item, noting any deviations from your ideal, and removing them from the production line for further inspection.Â
Today, you’ll find vision systems in pretty much every industry, from food and pharmaceuticals to manufacturing and even construction. They’re a simple and cost-effective way to improve both production line and QC efficiency, as well as boost QC team productivity significantly.Â
This pairing of computer vision systems and machine learning applications can:
- Spot surface defects: Like scratches, dents, or discoloration that human eyes might miss.
- Measure dimensions: Using precision systems that surpass manual measurement.
- Check assembly completeness: Benchmarking each item against your standards to ensure every component is properly installed.
- Verify packaging integrity: Helping you catch potential issues before products ship to customers.
What makes these systems even more practical for small and medium manufacturers (SMMs) is that they typically integrate directly into your existing production line or QC stations. You don’t have to rebuild everything from scratch, just improve what you’re already using.Â
Going Further With AI-Powered Quality Control: Dimensional Accuracy Tracking
Another foundation of strong QC is dimensional checks. Smart manufacturing is changing the game completely, with AI-powered systems able to measure and track every part as it moves down the line.Â
It does this through precise 3D models. Something like that you may see in AutoCAD. It compares the physical product against these set specifications in real time. If it’s outside of tolerances, it can be flagged and removed.
Preventing Shipping Issues with Packaging Inspections
Naturally, not all quality problems faced by customers occur during manufacturing. Products arriving damaged or incomplete are another issue, and sometimes, packaging at the facility is the root cause. AI can help reduce these failures through final packaging checks like:
- Label placement and readability: This ensures barcodes scan properly.
- Seal integrity: Checking for incomplete or damaged seals.
- Product positioning: AI verifies that contents are properly secured.
- Box weight/dimension consistency: To catch package defects or missing components before shipping.
How Predictive Maintenance and AI-Powered Quality Control Support Each Other
For those SMMs who are also exploring AI in manufacturing’s predictive maintenance offerings, there’s an unexpected bonus.
AI quality systems can also offer early warnings about equipment issues. If your computer vision system, for example, starts detecting more defects than is typical for a specific machine, it’s a strong indicator there may be a need for maintenance. These systems can even generate data to feed to your predictive maintenance machine learning applications for greater insight across production.Â
Operational Efficiency That Pays with AI-Powered Quality Control
While every SMM is different, with different needs and QC setups, implementing AI QC typically offers:
- Significant error reduction: The industry expects a 20%-50% reduction in defect rates, depending on the product and typical QC processes. One AI solutions provider suggests 25% is a great benchmark.
- Reduced inspection costs: With greater accuracy and faster processing, you save on staff costs, and relieve the stress on your QC team.
- Higher detection accuracy: AI-powered QC typically offers between 97% and 99% detection accuracy. For example, researchers created an AI-powered visual inspection machine with a 99.86% accuracy rate at capturing defects in casting products.Â
- Tighter standards: AI-powered QC can help you meet (or exceed) your targets and specifications more effectively. A study on QC in CNC manufacturing found that AI helped to reduce discrepancies in desired vs. estimated surface roughness significantly.
AI-powered QC can require a solid initial investment, with typical estimations of $100,000 to $300,000. Although it can be pricier to implement than some other smart manufacturing solutions, it will also pay for itself pretty quickly. One manufacturer saw a remarkable 281% ROI on their AI-powered QC at the one-year mark.
To minimize your initial investment, you can consider these options:
- Retrofit existing stations: This offsets new equipment costs.
- Partner with local technology providers: A provider that understands manufacturing can work within your budget and needs.
- Lease or finance systems: This helps to spread costs over time.
- Start with software-only solutions: If you are using existing cameras, this is a budget-friendly way to upgrade what you already have.
- Start small: Focus your first AI system on the quality issue that costs you the most money or customer complaints. You can expand down the line.
 Industrial automation doesn’t have to break your budget when you start strategically.
Putting AI-Powered Quality Control to Work
If you’re ready to give AI-powered quality control a try, success starts by:
- Identifying your most costly quality issue
- Documenting current inspection processes
- Setting measurable improvement goals
- Finding the right technology partner to support you
With a clear initial understanding of your biggest QC challenges, you can find the right solution for you. AI-powered quality control helps you never let your customers down, while supporting operational efficiency and profitability. If you’re ready to step up your QC game, IMEC will support you through making the right AI choices to support your growth.
*This article was developed through the combined expertise of contributors from IMEC and Goodman Lantern.
