Quality control is an essential part of any manufacturing process. Units that come off the production line need to be inspected to determine whether they meet specifications for the final product and to ensure that damaged, defective, or unsafe items don’t end up being shipped to customers. Quality control methods differ by industry and are often specific to each manufacturer and their products: business and production leaders decide how many samples need to be tested, at what intervals and to what specifications. But when it comes to actual inspections, most manufacturing quality processes look similar: a trained inspector visually evaluates each unit and decides on whether to pass or fail it based on agreed-upon acceptance criteria.

Experienced technicians can be very efficient at their work. Humans are good at learning on the job, making quick decisions and adapting to changing conditions. But people are also prone to making unintended errors, crumbling under pressure, and getting fatigued and distracted, especially when performing repetitive tasks. Plus, there’s the issue of labor costs, which are a major factor in an already expensive manufacturing process.

Naturally, manufacturers have long been trying to automate quality control processes and replace (or at least augment) human inspectors with cameras, lighting systems, and software solutions. But traditional machine vision systems were designed to follow a set number of rules, making them inflexible and unable to adapt to slight changes in the product, a noisy environment, or even unfrequently seen, unexpected defects. So, even if these static legacy systems did deliver an improvement in speed and volume of inspections, they often were inconsistent, required retesting and caused production line downtimes.

Applied Machine Learning Takes Visual Inspections to the New Level

AI and Machine Learning have introduced advanced intelligence to quality control processes. The new generation of visual inspection systems are able to learn and dynamically adapt to changing conditions, make decisions based on new information, and gather and organize quality data beyond “pass” or “fail” criteria, helping manufacturers implement continuous improvement practices and understand ongoing quality issues. There are many advantages to replacing outdated manual quality processes with automated inspection systems:

1. Reliable, continuing detection: Vision systems powered by machine learning and edge computing are as smart as human operators, but can work tirelessly without losing focus‍
2. Greater inspection accuracy: Automated inspection solutions perform tasks the same way, every time. ML can find even the smallest flaws, spot new defects with anomaly detection, and evaluate items against extremely tight tolerances‍
3. Embedded analytics: ML-powered technologies find and classify defects and immediately flag them for further analysis. Inspectors can then look deeper into the root cause and implement process improvement steps as needed. Cloud-based software can help analyze trends, uncover the source of quality problems and chart quality over time‍
4. Real time data in the cloud: Manufacturers receive real-time production reports, accessible anytime, from anywhere‍
5. Traceability throughout the production process: Cloud-connected visual inspection systems add transparency to the entire process. They can even provide an audit trail, if needed, to settle disputes with clients or minimize waste in the event of a recall‍
6. Higher efficiency: Not only are automated visual systems more effective at detecting defects, they also free up human inspectors to perform higher-value tasks, making employees more efficient and accelerating the entire post-production process‍
7. Faster time to value: Cloud-based visual inspection solutions are quick to set up, configure and update. They can also be repurposed around the manufacturing floor in a matter of hours.

Quality control doesn’t need to be expensive, time consuming or error prone. The new generation of ML-powered technologies automate the quality inspection process to make it faster, more resilient, and more efficient. For manufacturers, this translates directly into higher yields, better quality, less downtime, fewer recalls, and ultimately – higher profits. The data gathered from automated inspections can provide valuable insights to any manufacturing team, helping them implement continuous improvement and adjust their processes to maximize quality. To learn more about implementing a next-gen machine learning visual inspection system, visit: www.elementaryml.com.