The advancements in automotive technology have helped revolutionise the manufacturing industry. Despite the advances made, quality control remains predominantly reserved for humans. However, manual part identification and quality checks can cause major bottlenecks. Decreased output and tentative production timelines are commonplace in manual-focused factories.
Factors like similarity are problematic for traditional inspection systems. Manual checks are repetitive and anomalies often go undetected. This issue is magnified when the products under inspection are part of a large assembly of similar products. To minimise the number of faulty products, quality inspectors will conduct multiple inspections. This is not only time-consuming, but also introduces human error into the process.
Sparkd is working with manufacturers to help address these challenges. We optimise computer vision software for part identification and inline quality inspection.
When inaccurate part identification affects quality compliance
When inaccurate part identification affects quality compliance, financial and reputational damage is inevitable. Inaccurate processes can result in defective or mismatched product batches. Customers receiving faulty products will be apprehensive about continuing to buy from the manufacturer. Correcting these errors can also lead to wasted time and resources.
We have seen companies suffer at the hands of inaccurate processes. For example, one of our customers working in the field of aerospace manufacturing found that a significant percentage of their product batches were mismatched before reaching the assembly phase. The procedures they were using failed to identify certain anomalies among similar products. As a result, they were forced to perform multiple manual checks. However, defective or incorrect products still made it through the inspection process. This was the result of human error, which is unavoidable, regardless of discipline.
The challenge of identifying many similar parts
Manufacturing is a standards-driven industry. Customers expect compliance, accuracy, and even perfection in today’s market. In certain industries, perfection is even critical. Imagine you are developing products for medical devices or automobiles. In this case, a faulty part could quite literally mean the difference between life and death. Unfortunately, the level of accuracy required to achieve perfection is not always easy to achieve.
Automating part identification and inline quality inspection could be the answer. Computer vision and deep learning technology are viable solutions to these challenges.
The Benefits of automating inline quality inspections
Many manufacturers are already utilising lean manufacturing principles. By doing so, they increase their throughput and overall efficiency. However, they often overlook computer vision as part of their quality assurance strategy. Manufacturers remain hesitant to make the leap to automation for a number of reasons. It’s not easy to digitise an ecosystem that has always been human-led. However, the benefits of automating part identification and inline quality inspection are undeniable. Let’s take a look at a few of these benefits:
- Reduced cost. Employing staff for multiple rounds of quality inspection is a huge expense. Automated inspection is usually performed at a fraction of the price.
- Increased efficiency. Automated inspection means you can run more parts per day than you ever could before. You won’t have staff taking breaks or losing focus. Operations will also be simplified for workers operating and monitoring the production line.
- Improved quality. Computer vision systems can help nail down accuracy and quality. Products are more likely to make it through the chain of production defect-free. Computer vision technology can capture inexhaustible amounts of multi-dimensional data. Using this data, quality control can be performed almost flawlessly.
- Better tracking. The ability to track and trace parts helps streamline processes. Manufacturers will be able to visually track and control production on the line with computer vision.
- Stronger reputation. It goes without saying that a strong reputation relies on consistently high-quality products. Beyond that, customers may also feel more at ease buying from a company using top-of-the-line automated technology. Customers are also more likely to trust a company with an in-house production system.
Computer Vision and Deep Learning for inline part identification and tracking
Through image analysis, computer vision can determine the nature of any product type. With deep learning, computer vision systems can identify minuscule differences in products. These models use real-time data from sensors and cameras to conduct quality control procedures. The accuracy of these algorithms can accurately forecast operational outcomes. In turn, enabling businesses to optimise their processes.
When working with large volumes of products, a multi-camera system can be installed. This system is positioned at the beginning and end of a production line. It ensures that all parts are carefully identified and tracked. A comprehensive system like this ensures products do not get sorted into the wrong batches.
Computer vision and deep learning solutions are critical to smart manufacturing. While these models may not be a one-size-fits-all solution for every production line, the benefits are undeniable. At Sparkd, we understand that accuracy is a critical objective on the factory floor. We design bespoke computer vision solutions for inline quality inspection. Make human error a thing of the past and consider making the leap to computer vision.
Work with Sparkd today for your AI needs.
Check out our other articles:
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- 4 Challenges to Integrating Machine Vision in Manufacturing
- Computer Vision vs Machine Vision
Author: Johanna Walsh