Quality assurance in manufacturing is demanding and expensive, yes, but also absolutely crucial. After all, selling flawed goods results in returns and disappointed customers. Harnessing the power of image recognition and deep learning may significantly reduce the cost of visual quality control while also boosting overall process efficiency.
According to “Forbes”, automating quality testing with machine learning can increase defect detection rates by up to 90%. Machines never tire, nor lose focus or need a break. And every product on a production line is inspected with the same focus and meticulousness.
Yield losses, the products that need to be reworked due to defects, may be one of the biggest cost-drivers in the production process. In semiconductor production, testing cost and yield losses can constitute up to 30% of total production costs.
Time and money for quality
Traditional quality control is time-consuming. It is manually performed by specialists testing the products for flaws. Yet the process is crucial for business, as product quality is the pillar a brand will stand on. It is also expensive. Electronics industry giant Flex claims that for every 1 dollar it spends creating a product, it lays out 100 more on resolving quality issues.
Since the inception of image recognition software, manufacturers have been able to incorporate IP cameras into the quality control process. Most of the implementations are based on complex systems of triggers. But with the conditions predefined by programmers, the cameras were able to spot only a limited number of flaws. While the technology may not yet have been worthy of the title game changer, the image recognition revolution was one step further.
Deep learning about perfection
Artificial intelligence may enhance the company’s ability to spot flawed products. Instead of embedding complex and lengthy lists of possible flaws into an algorithm, the algorithm learns the product’s features. With the vision of the perfect product, the software can easily spot imperfect ones.
Visual quality control in Fujitsu
A great example of how AI combined with vision systems can improve product quality is on display at Fujitsu’s Oyama factory. The Image Recognition System the company uses not only helps it ensure the production of parts of an optimal quality, but also. supervises the assembly process. This dual role has markedly boosted the company’s efficiency.
As the company stated, the solution lacked the flexibility today’s fast-moving world demands. But powering up an AI-driven solution allowed it to quickly adapt its software to new products without the need for time-consuming recalibration. With the AI solutions, Fujitsu reduced its development time by 80% while keeping part recognition rates at 97%+.
As their solution proved successful, Fujitsu deployed it at all of its production sites.
Visual quality control is also factoring in the agricultural product packing arena. One company has recently introduced a high-performance fruit sorting machine that uses computer vision and machine learning to classify skin defects. The operator can teach the sorting platform to distinguish between different types of blemishes and sort the fruit into sophisticated pack grades. The solution combines hardware, software and operational optimization to reduce the complexity of the sorting process.
As automation becomes more widespread and manufacturing more complex, factories will need to employ AI. Self-learning machines ultimately allow the companies forward-thinking enough to use them to reduce operational costs while maintaining the highest quality possible.
However, an out-of-box solution is not always the best option. Limited flexibility and lower accuracy are the most significant obstacles most companies face. Sometimes building an in-house team of machine learning experts is the best way to provide both the competence and ability to tailor the right solutions for one’s business. As building the internal team to design visual quality control is more than challenging, finding the reliable partner to gain knowledge may be the best option.