三明 Product Lineup

By conducting visual inspections with robots, not only inspections but also tasks such as palletizing, depalletizing, aligning, assembling, stacking, boxing, screwing, and inserting can be automated in a space-saving manner. This contributes to reducing the burden on employees, minimizing workforce size, and addressing labor shortages through tasks other than inspections. 【Inspection Examples】 - Vials - Iron - Irregular shapes - Stainless steel - Polyhedra - Fittings - Deformed containers - Shafts - Cosmetic containers - Cams - Metal parts - Packing - Rubber products - Fitted parts - Automotive parts - Sheet-like workpieces - Non-self-supporting transportable workpieces - Workpieces requiring multi-faceted inspection 【Detectable Defects】 - Foreign objects - Dirt - Scratches - Dents - Shorts - Contamination At Sanmei Co., Ltd., we have numerous achievements in AI inspections and line inspections in addition to robot-based visual inspections (rule-based). *2D picking/3D picking (random picking) is optional.

We would like to introduce our "Robot Renewal Construction" services. Inspection and diagnosis can be performed based on the model and serial number of the robot and controller. We propose replacing aging robots with high-efficiency robots. Additionally, we can also propose automation of the upstream and downstream processes during robot renewal. By implementing DX, we can build WES and WCS. 【Features】 ■ Regular inspections of robots extend their lifespan ■ Battery replacement prevents data corruption ■ Robot renewal improves productivity *For more details, please download the PDF or feel free to contact us.

The "Curved Surface Inspection Robot" is a scanning inspection system designed for large and curved workpieces, combining a collaborative robot (20kg payload) manufactured by Yaskawa Electric with a line camera from Dexis that features a unique dynamic tracing function. By moving the camera while capturing images, it can simultaneously acquire up to 8 lines of both specular and diffuse reflections, which is characterized by a short tact time. By storing image inspection records, it can be utilized as a traceability system, contributing to enhanced quality assurance and traceability. 【Features】 ■ Utilizes a space-saving collaborative robot from Yaskawa Electric ■ Capable of capturing various defects at once with simultaneous imaging of up to 8 lines ■ Compatible with various curved workpieces such as consumer goods, home appliances, and traffic signal equipment parts ■ Contributes to labor and energy savings in visual inspections and improves inspection quality *For more details, please refer to the materials. Feel free to contact us with any inquiries.

In the home appliance industry, the appearance quality of products greatly influences customer satisfaction. Scratches and distortions on the casing not only diminish the product's value but can also lead to customer complaints. The curved surface inspection robot addresses challenges in the visual inspection of home appliance casings by combining a collaborative robot from Yaskawa Electric with a camera from Dexis. With simultaneous imaging of up to 8 lines, it captures various defects at once, contributing to improved inspection quality. 【Usage Scenarios】 - Inspection of home appliance casings - Inspection of parts with curved surfaces - Automation of visual inspections on production lines 【Benefits of Implementation】 - Reduction of labor in visual inspections - Improvement in inspection accuracy - Strengthening of traceability 【Features】 - Utilizes a space-saving collaborative robot from Yaskawa Electric - Capable of capturing various defects simultaneously with up to 8 lines of imaging - Supports various curved workpieces such as household goods, home appliances, and traffic control equipment parts - Contributes to labor and effort reduction in visual inspections and improvement in inspection quality - Follows the distortion of lighting lines to obtain images with uniform brightness Rice cookers, refrigerators, air conditioners, bathtubs, toilets, washbasins, etc.

By generating pseudo-defective images, we can create appearance inspection AI from a small number of defective products (data). This accelerates the utilization of AI in the appearance inspection process. AlliomVision is capable of generating pseudo-defective product images using deep learning. This eliminates the need for the enormous time and effort required to acquire defective product images. With the introduction of AlliomVision, a high-precision "appearance inspection AI model" can be completed in a short period, allowing for the rapid construction of an appearance inspection system. (Developer: AI Cube Inc.) [Features] - Pseudo images generated by AlliomVision can be used in any AI development environment of your choice (optional feature). - We provide dedicated software that performs image acquisition from cameras necessary for the appearance inspection system, inspection using AI models, and display and storage of inspection results. There are proven results in salad plating inspection and fish bloodline inspection, among others.

In the automotive industry, maintaining a consistent exterior quality of vehicle bodies is essential for improving product reliability. Particularly for body parts with complex curved surfaces, uniform inspection is crucial. Inadequate inspection can lead to missed detections or false positives. Our curved surface appearance inspection robot combines Yaskawa Electric's collaborative robot with Dexis cameras to achieve scanning inspections of large, curved workpieces. 【Application Scenarios】 - Inspection of complex curved areas of automotive bodies - Detection of appearance defects such as scratches and foreign object adhesion - Automated inspection on production lines 【Benefits of Implementation】 - Stabilization and improvement of inspection quality - Reduction of labor and effort in the inspection process - Increased productivity through shortened takt time

In the aerospace industry, visual inspection of parts with complex shapes is crucial to ensure the safety and reliability of aircraft. Accurately assessing surface scratches, irregularities, and the condition of welds is essential for accident prevention. Traditional inspection methods have been time-consuming for extensive and precise checks, making labor-saving a challenge. Our curved surface visual inspection robot addresses these issues, achieving efficient and high-precision inspections. [Application Scenarios] - Surface inspection of aircraft and similar structures - Inspection of parts with complex curved surfaces - Visual confirmation of welds and joints [Benefits of Implementation] - Reduction of inspection takt time - Standardization and improvement of inspection quality - Cost reduction through labor savings - Strengthening of traceability

In the shipbuilding industry, extensive and complex curved surface inspections are required for verifying the appearance of the hull. In particular, accurately assessing the condition of surface scratches, foreign objects, and welds on the hull is essential for safe operation and maintenance. Traditional visual inspections have limitations in terms of the range and accuracy of what can be checked, requiring significant time and effort. Our curved surface appearance inspection robot provides an efficient and highly accurate inspection solution to address these challenges. 【Application Scenarios】 - Checking for scratches, dents, and dirt on the hull's outer plates - Inspecting the condition of welds - Detecting paint peeling and cracks - Inspecting curved surfaces of large structures 【Benefits of Implementation】 - Reduced inspection time and improved work efficiency - Enhanced inspection accuracy and reduced oversight - Cost savings through labor reduction - Strengthened quality assurance and traceability

In the home appliance industry, the appearance quality of products is directly linked to user satisfaction, so inspections that do not overlook defects such as scratches, dirt, and molding defects on the casing are required. In particular, casings with curved surfaces or R-shaped surfaces are difficult to check with traditional inspection methods due to challenges in achieving uniform lighting and angles, leading to variations in inspection quality and the risk of oversight. Our curved appearance inspection robot addresses these challenges by providing high-precision inspections specifically designed for large and curved workpieces. 【Application Scenarios】 - Inspection of scratches, dirt, and foreign matter in the casings of home appliances (refrigerators, washing machines, air conditioners, etc.) - Detection of defects such as molding defects and wrinkles - Automated appearance inspection on production lines 【Benefits of Implementation】 - Stabilization and improvement of inspection quality - Reduction of labor and effort in the inspection process - Increased productivity through shortened takt time - Establishment of a quality assurance system strengthened by traceability

In the furniture industry, surface evaluation that does not overlook defects such as surface uniformity, scratches, and dirt is required to maintain the aesthetic and quality of products. Particularly for furniture components with curves and R surfaces, traditional inspection methods may struggle to address these challenges. Our curved surface appearance inspection robot achieves high-precision inspection for such issues. 【Application Scenarios】 - Evaluation of scratches and unevenness on curved surfaces of tables and shelves - Quality maintenance through automated inspection on the manufacturing line 【Benefits of Implementation】 - Stabilization and improvement of inspection quality - Reduction of labor and effort in the inspection process - Increased productivity through shortened takt time

In the building materials industry, the inspection of exterior finishes emphasizes the importance of maintaining product quality and ensuring aesthetic appeal. Particularly for large, curved products such as roofing and exterior wall materials, inspections must ensure uniform surface conditions and not overlook defects like scratches. In response to these challenges, our curved surface appearance inspection robot combines Yaskawa Electric's collaborative robot with Dexis cameras to achieve scanning inspections of large, curved workpieces. 【Usage Scenarios】 - Inspection of roofing materials - Surface inspection of exterior wall materials - Other appearance inspections of building materials with curved shapes 【Benefits of Implementation】 - Improved inspection quality - Reduction of labor and effort in the inspection process - Shortened takt time

In the food manufacturing industry, it is essential to accurately detect foreign matter contamination and abnormalities in appearance to maintain product quality and ensure safety. In particular, variations in plating and fine foreign matter derived from raw materials are difficult to confirm visually, posing a risk of oversight. These challenges can lead to a decline in product quality and a loss of consumer trust. AI appearance inspection solutions accelerate the use of AI in the inspection process by generating pseudo-defective images, allowing for the rapid construction of high-precision AI models from a small amount of defective product data. 【Application Scenarios】 - Inspection of salad plating - Inspection of fish bloodlines - Detection of foreign matter contamination 【Benefits of Implementation】 - Improved inspection accuracy - Reduction in inspection labor - Strengthened quality control

In the beverage manufacturing industry, it is essential to reliably detect foreign substances to maintain product quality and ensure safety. In particular, detecting fine foreign substances that are easily overlooked by visual inspection, as well as foreign substances with inconsistent shapes and positions, is a significant challenge in quality control. AI visual inspection solutions address these challenges and support improvements in the accuracy and efficiency of the inspection process. 【Use Cases】 - Detection of foreign substances in the manufacturing line - Detection of abnormalities in containers and packaging - Quality checks of products 【Benefits of Implementation】 - Improved inspection accuracy - Reduction in inspection labor - Strengthening of quality control systems

In the pharmaceutical industry, quality control requires the accurate detection of minute foreign substances and abnormalities in appearance to ensure product safety and uniformity. Particularly, the quality of pharmaceuticals is directly linked to people's health, making a strict inspection system that does not overlook even minor defects essential. However, traditional visual inspections can face challenges such as inspector fatigue, variability due to subjectivity, and a lack of defective product data. AI appearance inspection solutions accelerate the utilization of AI in the inspection process by generating pseudo-defective images, allowing for the creation of high-precision appearance inspection AI from a small amount of defective product data. This reduces the time and effort required to acquire defective product images and supports the rapid establishment of high-precision appearance inspection AI models and the swift implementation of inspection systems. 【Application Scenarios】 - Inspection of foreign substances, chips, and discoloration in pharmaceutical tablets and capsules - Inspection of printing defects, damage, and foreign substance adherence in packaging materials 【Benefits of Implementation】 - Improved inspection accuracy and reduced burden on inspectors - Strengthened quality assurance by preventing the outflow of defective products - Increased productivity through the automation of inspection processes - Rapid implementation of AI inspection systems

In the cosmetics manufacturing industry, it is essential to accurately detect appearance defects such as scratches on containers, foreign matter contamination, and printing errors to maintain product quality and enhance brand image. In particular, fine defects that do not have a consistent shape or position can easily be overlooked during visual inspections, becoming a burden for quality control. AI appearance inspection solutions address these challenges by achieving high-precision defect detection using AI. 【Application Scenarios】 - Detection of scratches and dirt on containers - Verification of cap tightness - Inspection of printing content and position - Detection of foreign matter contamination 【Benefits of Implementation】 - Improved inspection accuracy and reduction of oversights - Labor savings through automation of inspection processes - Maintenance of quality by preventing the outflow of defective products - Reduction of effort in collecting defect data

In the electronic component manufacturing industry, it is essential to accurately and efficiently detect minute defects to maintain product quality and improve yield. In particular, detecting defects such as scratches and dirt that are often overlooked during visual inspections—especially those with undefined shapes or positions—is crucial for ensuring product reliability. However, collecting data on defective products and training AI models can require significant time and effort. AI visual inspection solutions accelerate the utilization of AI in the inspection process by generating pseudo-defective images, allowing for the rapid development of high-precision visual inspection AI from a small amount of defective product data. 【Use Cases】 - Detection of scratches, dirt, and foreign matter on the surface of electronic components - Detection of chips, cracks, and deformations in parts - Detection of assembly defects 【Benefits of Implementation】 - Improved inspection accuracy and reduced human errors - Reduced inspection labor and increased productivity - Quality improvement through early detection of defective products

In the field of the chemical industry, preventing foreign matter contamination is extremely important for maintaining product quality and ensuring safety. In particular, detecting fine foreign substances or those with inconsistent shapes and positions has limitations with traditional visual inspections, which carry the risk of oversight. This can lead to a decline in product quality and, in some cases, serious accidents. AI appearance inspection solutions utilize deep learning to generate pseudo-defective images, allowing for the rapid construction of high-precision appearance inspection AI models from a small amount of defective product data. This supports the early detection and prevention of foreign matter contamination and contributes to the stable operation of production lines. [Usage Scenarios] - Detection of foreign matter contamination on manufacturing lines - Identification of abnormal areas in product quality management - Incoming inspection of raw materials [Benefits of Implementation] - Reduction of product defects due to foreign matter contamination - Improvement in inspection accuracy and reduction in inspection labor - Strengthening of quality management systems

In the printing and binding industry, detecting printing errors and dirt is crucial for maintaining product quality. In particular, ink splatter, foreign objects, and scratches can potentially diminish the value of the product. Relying solely on human eyes to identify these defects requires time and effort, and carries the risk of oversight. AI visual inspection solutions address these challenges by utilizing AI to achieve rapid and highly accurate inspections. Usage Scenarios: * Detection of defects in seal and label application * Detection of dirt and foreign object contamination Benefits of Implementation: * Improved inspection accuracy and reduction of human errors * Increased efficiency through automation of the inspection process * Shortened development time and cost reduction * Standardization of quality criteria

In the textile and apparel industry, detecting pattern defects such as misalignment and color inconsistencies is essential to maintain product quality. Particularly, complex patterns are prone to being overlooked during visual inspections, which adds to the burden of quality control. In response to these challenges, AI visual inspection solutions efficiently detect pattern defects by leveraging AI technology. 【Application Scenarios】 - Detection of fabric pattern defects - Confirmation of pattern alignment after sewing - Detection of defects in printed product patterns 【Benefits of Implementation】 - Improved accuracy in detecting pattern defects - Increased efficiency through automation of the inspection process - Reduction in quality control costs

In the building materials manufacturing industry, it is essential to accurately detect anomalies such as surface scratches and dirt to maintain product quality and ensure reliability. Particularly in visual inspections, oversights are common, and depending on the occurrence of defective products, it may take time to collect the defective product data necessary for AI training. In response to these challenges, AI appearance inspection solutions accelerate the utilization of AI in inspection processes by generating pseudo-defective images, allowing for the rapid construction of high-precision appearance inspection AI models from a small amount of defective product data. 【Usage Scenarios】 - Detection of surface scratches on building materials - Detection of dirt on building materials - Detection of foreign substance adhesion on building materials 【Benefits of Implementation】 - Improved inspection accuracy - Reduction of inspection man-hours - Prevention of defective products from being released

In the semiconductor manufacturing industry, particularly in wafer inspection, the accuracy of detecting minute defects and foreign substances is directly linked to product quality. Building a high-precision inspection AI from limited defective data in a short period is a crucial challenge for improving inspection process efficiency and quality. AI visual inspection solutions generate pseudo-defective images, allowing for the creation of high-precision appearance inspection AI models even from a small amount of defective product data. This significantly reduces the time and effort required to collect defective product images and supports the rapid establishment of visual inspection systems. 【Use Cases】 - Detection of fine scratches, dirt, and foreign substances on wafers - Detection of misalignment and shape variations - Automation and efficiency improvement of the inspection process 【Benefits of Implementation】 - Improved inspection accuracy and prevention of defective products from being released - Reduction of inspection labor and cost savings - Quick implementation of AI inspection systems

In the automotive parts industry, it is essential to accurately detect defects such as scratches and dirt on the appearance of products to maintain quality. In particular, scratches that could affect the functionality or safety of parts must be identified without being overlooked. However, traditional visual inspections have faced challenges such as variability due to inspector fatigue and subjectivity, as well as difficulties in collecting data on defective products. AI appearance inspection solutions support the efficiency and accuracy of the appearance inspection process by generating pseudo-defective images, allowing for the rapid development of high-precision AI models from a small amount of defective product data. 【Use Cases】 - Scratch detection on automotive parts - Detection of foreign matter contamination - Detection of surface dirt and discoloration 【Benefits of Implementation】 - Improved inspection accuracy - Reduction in inspection labor - Prevention of defective products from being released

In the packaging materials manufacturing industry, uniformity and accuracy of printed materials are required to maintain product quality and improve customer satisfaction. In particular, printing defects such as ink smudging, color unevenness, and misalignment can damage the appearance of products and potentially affect brand image. Detecting these defects with the human eye requires a significant amount of time and effort. AI visual inspection solutions support the efficiency of the visual inspection process by generating pseudo-defective images, allowing for the rapid construction of high-precision AI from a small amount of defective product data. 【Use Cases】 - Detection of color unevenness, smudging, and misalignment in printed materials - Detection of printing errors on packaging materials - Detection of foreign matter contamination in films and sheets 【Benefits of Implementation】 - Improved inspection accuracy and reduction of human errors - Increased productivity through automation of the inspection process - Maintenance of quality by preventing the outflow of defective products