Achieving "high-precision inspection" with "easy operation." AI visual inspection system [Phoenix] solves quality inspection challenges.
In the production site, quality inspection is an important process. It has been found that companies face various challenges regarding "improving inspection quality" and "labor-saving" in this quality inspection. To address the challenges of quality inspection faced by the manufacturing industry, we have developed AI learning and verification software, **Phoenix Vision**, and AI visual inspection software, **Phoenix Eye**. **Main Features** - Easy to use without any knowledge of AI due to simple operation design. - Equipped with multiple AI algorithms, allowing for optimal learning suited to inspections. - Includes rule-based systems, enabling hybrid inspections. - Compatible with existing peripheral equipment. - Real-time inspection. Since we have a development environment in-house, we continuously incorporate the latest technologies and quickly commercialize products, equipping them with the latest AI algorithms. So far, we have implemented our solutions in various manufacturing companies, including leading companies in Japan's automotive and food industries. If you are looking into reviewing inspection processes or gathering information about AI, please feel free to contact us. We will explain the benefits of AI and provide case studies.
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Do you have any concerns like these regarding quality inspection? - "There are differences in inspection quality among inspectors during visual inspections." - "The accuracy of existing image inspection machines is poor." - "The inspection difficulty is high, and it can only be inspected visually." - "I want to eliminate dependence on individuals and improve productivity and efficiency." - "The settings for image inspection machines are complicated, and we cannot configure them in-house." - "Handling a variety of small lots is difficult with conventional image inspection machines." - "I want to minimize human errors as much as possible." Purpose of implementing the AI visual inspection system [Phoenix]: ◆ Automation of the inspection process ◆ Assurance of quality through improved inspection accuracy ◆ Yield improvement through accurate judgments ◆ Reduction of personnel for visual inspectors ◆ Capability to handle a variety of small lots ◆ Possibility to use a combination of AI and rule-based methods or switch between them depending on the inspection items By utilizing AI in visual inspection, why not clear the challenges of visual inspections and conventional image inspection machines and aim for improved productivity? *To give you an idea of how AI visual inspection can be utilized, we are currently conducting free sample verifications. If you have any interest, please feel free to contact us.
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Purpose: Automation of the inspection process AI visual inspection is an image inspection method that utilizes deep learning technology. Specifically, it is a technique that automatically optimizes the parameters (weights) of a deep neural network so that the optimal result (NG) is output for the input image (defective area). In traditional inspection machines, system integrators would set the optimal rules (standards) to build the model, but AI can automatically capture the characteristics of defective areas and construct the model. As a result, it can achieve a high recognition accuracy that surpasses human capabilities, even in cases where the surface condition is complex and simple rules cannot be applied. "Three" Benefits ◆ High robustness in inspection It can inspect metals and fibers with complex surface conditions, as well as food products with shape variations, without any issues. ◆ Reduces the risk of defective products being released and prevents large-scale losses Achieves high-precision inspection with the same inspection standards. ◆ Automation of inspection based on the standards of skilled inspectors Stable inspections are possible, leading to the standardization of inspection criteria and a reduction in adjustment work hours.
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Inspection Case 1: Carpet Tear Inspection In conventional visual inspection systems, it was difficult to inspect products with gradations, as shown in the image, leading to numerous false detections. With [Phoenix], by training the latest AI equipped with it to learn the characteristics of tears, it can detect only the defective areas without reacting to the patterned parts.
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Inspection Case 2: Bone Residue Inspection of Sausages It is possible to detect foreign objects and bones mixed inside sausages with high precision. By combining preprocessing technology with the latest AI algorithms, 【Phoenix】 has achieved high-precision detection of small bones and foreign objects.
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Inspection Case 3: Scratch Inspection on Steel Plates In cases where the inspection target has a complex surface, such as metals, traditional visual inspection systems tend to react to variations in brightness of good parts, leading to unstable inspections. With 【Phoenix】, by training the AI only on the defects we want to detect, we can accurately identify only the defective items even in inspection targets with complex surfaces.
Company information
Our company supports DX (Digital Transformation) in the manufacturing industry. Japanese manufacturing is facing significant challenges such as "intensified international competition" and "labor shortages," making the improvement of productivity through digital utilization increasingly necessary. However, the reality is that digital utilization is still not progressing on the ground. The reasons for this include various uncertainties such as: - "Not knowing when to utilize it" - "Not understanding the value of data utilization" - "Not knowing how to proceed" At VRAIN Solution, we aim to resolve the various uncertainties that companies face and realize their DX through consulting, AI algorithm provision, data analysis, AI application sales, system development, and a wide range of services utilizing the latest technologies. If you have any concerns or questions regarding DX, we would be happy to assist you.