Machine vision technologies enable the development of hardware and software that can examine its surroundings and derive conclusions based on what it observes. The concept is not new, but recent advancements enable a wide range of fascinating industrial solutions, from defect detection to vastly better food inspection processes. That is what we will be looking at in this three-part series.
Let us begin the series by discussing the use of machine vision in the detection of defects.
Surviving in today’s increasingly competitive market necessitates supplying a product of near-perfect quality. While most established manufacturers can run their production processes with amazingly low defect rates, customers want 100% defect-free products. Therefore, the ability to spot defective items before they make it to market while maintaining high production rates is critical for every manufacturer today.
Early detection of uncommon defects also provides manufacturers with an opportunity to improve their quality standards. Defect detection is a prime illustration of the potential value that machine vision technology can unlock in the manufacturing industry.
Machine vision defect detection systems are superior to manual inspection in tracking products on the production line, offering significantly greater precision rates, improved product quality, productivity gains, and reduced production costs.
Faulty Components Cost Manufacturers a Lot
Certain levels of defects are unavoidable throughout the manufacturing process of mechanical components. These defects may include unwanted abrasions, holes, pits, and scratches on various pieces that exit the production line. Such defects can result from design flaws, inadequate manufacturing equipment, metal fatigue, and unsuitable working conditions—or any combination of these factors.
Defective components increase production costs, impair product quality, reduce product lifespan, impact customer satisfaction, and result in a colossal waste of resources. In some cases, defective items can even endanger people’s lives—think of the airline or medical industry.
Machine Vision Is a Disruptive Force In Defect Detection
Traditionally, defect detection was performed manually by human inspectors, who were inherently prone to weariness, lack of attention, and bias.
Defect detection has become increasingly technology-driven over the last decade, thanks to developments in machine vision technologies. Smart cameras and similar technologies are already assisting manufacturers in delivering high-quality inspection in shorter cycles, lowering latency and costs, and setting the bar that exceeds the capabilities of even the most experienced and competent inspectors.
Machine vision technology is a combination of software and hardware that performs image capture and processing functions. This technology employs Machine Learning and defect-prediction models to learn and infer from the manufacturer’s data autonomously. In other words, these models determine which features are crucial on their own—and create new implicit information that governs which combinations of features influence overall product quality. Automated manufacturing defect detection solutions boost productivity and accuracy while dynamically adjusting to detect different types of defects across industries.
There are various defect detection applications in Industry 4.0, but machine vision-driven visual inspection is by far the most dominant. Sensor-based quality control technology allows for data generation, processing, and interpretation for real-time quality control without disrupting the production process.
Early detection decreases waste caused by defective parts, reducing scrap and rework. Furthermore, sensor data can guide rework actions necessary in the event of product damage, maximizing rework efficiency. Defect analysis, defect prevention, and defect testing are other use cases supported by machine vision-driven defect detection systems.
How Does It Work?
It starts with image pre-processing. Machine vision systems use components such as digital sensors and industrial cameras with highly specialized optics to capture the cosmetic and dimensional qualities of the product. The data captured also includes label information, seal validation, and data code. Based on deep learning model requirements, the data is augmented with video frames, pictures, and other datasets. It is then converted to a digital format and archived.
The deep learning classifier is trained using both good and bad samples. The model is trained using datasets of acceptable characteristics and irregularities for it to be tested with a human approach. Image classification, object detection, and image segmentation are all used in this step.
The more data points there are and the better the data quality, the better the model. Using test data, the model is trained and tested for accuracy—and as new data is collected, the model is retrained, resulting in a more ingenious defect detection solution.
Benefits of Machine Vision-based Defect Detection In Manufacturing
Manufacturers benefit significantly from machine vision-based defect detection systems. These are some of the main benefits:
- Lowered operational expenses.
- Increased manufacturing volume while maintaining high quality.
- Early defect detection prevents faulty products from being moved down the assembly line.
- Greater production efficiency and shorter cycle times.
- Improved incoming material inspection.
- Achieving—and often—exceeding human-level accuracy
- Using previous data to identify problems and enhance future manufacturing processes.
McKinsey estimates that the advantages of defect detection and other Industry 4.0 applications will generate a potential value of $3.7 trillion for manufacturers by 2025. Considering these prospects and the general hype surrounding Industry 4.0, it should come as no surprise that 70% of businesses have already started to experiment with Industry 4.0 solutions like machine vision-driven defect detection systems.
Manual product quality inspection can be time-consuming and labor-intensive. It is also prone to inconsistency, which can result in rejections and recalls. This increases the production costs as well as the wastage of resources. Machine vision-based defect detection solutions reduce the need for human intervention by automating and optimizing your quality processes. They can help businesses accomplish increased efficiency, lower costs, less waste, and improved customer satisfaction.