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【2020 Application Example】 AI Can Sealing Film Inspection System Improves Product Shipment Yield and Ensures Food Safety

Traditional manufacturing quality control relies on visual inspection, which damages both quality and goodwill

According to research of the International Data Corporation (IDC), 25% of Taiwan's manufacturing companies adopted artificial intelligence (AI) in 2018. The companies mainly focus on two needs, one is quality testing, and the other is predictive maintenance of equipment.

However, in many traditional manufacturing industries, finished products from the production line are still manually inspected. The problem with manual inspection is that long working hours and eye fatigue often result in inconsistent quality, and the shipment of defective products with miniscule defects that cannot be identified with the naked eye results in compensation of damages and damage to goodwill.

Poor sealing film can have a massive impact

For a domestic coconut jelly product manufacturer, in the coconut jelly product manufacturing process, sampling inspection of the integrity of product sealing film is conducted manually, but the coverage of sampling inspections is 2.5% due to human resource arrangements and fast production line speeds. If a product with poor sealing film is shipped, it will not only cause damage to the single can of product, but also contaminate products in the same box and transportation vehicle, and attract mosquitoes and flies, causing overall hazards and affecting goodwill. In addition, since the product is a highly concentrated processed food, if products with poor sealing film are not detected and the buyer does not inspect the products after shipment, it might cause a food safety crisis with huge consequences!

Therefore, the "AI quality control inspection solution" not only improves inspection coverage, but also hopes that the AI system can accurately pick out products with defective seals, reducing the chance of defective products being shipped and subsequent food safety issues.

Smart sealing yield inspection, comprehensive review

Seal recognition system diagram

▲Schematic diagram of sealing film recognition system

ZeroDimension Tech Co., Ltd. combined its know-how in image-related AI systems with the system integration know-how of another well-known system integrator in the industry to jointly develop a "smart factory sealing yield inspection system," which was integrated and implemented in the process of coconut jelly manufacturers, increasing the coverage of product seal inspection.

Before utilizing the capabilities of AI, the original production line produced 100 boxes (about 600 cans) with a yield of 95%, meaning that there are about 30 defective cans. However, since the inspection coverage was only 2.5%, only 1 defective can was detected. However, after utilizing AI for inspection, the inspection coverage rate increased to 96%, meaning that about 28 defective cans can be detected, greatly increasing the detection rate of defective products, thereby reducing potential losses in the future.

Whether it adds value as an add-on or is built-in, it can provide solutions for the industry

Inspection service process diagram

▲Schematic diagram of inspection service process

This sealing film inspection system service framework can be implemented into the quality control inspection of other similar inspection processes in the form of an add-on in the future, such as: integrated into the film sealing production process of beverage factories and other canned products. It can also integrate software and hardware with sealing machine hardware manufacturers to add value to sealing machines using the build-in model, providing the industry with total solutions.

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【導入案例】防患於未然 麗臺科技研發心臟衰竭AI辨識技術可及早發現病徵
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The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the 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constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed 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actual application in yachts The image inspected is an ultrasound image The image displays different colors based on the ultrasonic feedback signal An AI model that automatically identifies defective parts is established through the YOLO algorithm If the amount of abnormal data collected is insufficient for training, the CNN-based Autoencoder algorithm is used to collect normal image data for training and construct an AI model for abnormality detection The object detection YOLO model is trained by inputting image data marked as having defects, while the abnormality detection model is trained by inputting image data without defects Simulated defective specimen corresponding to PAUT results Defect detection by and AI system can shorten the construction period by 15 months and speed up determination by 50 After the development of this AI system is completed, it will be validated on actual 54-foot yachts of Kha Shing Enterprise, and can effectively resolve issues with defects The application of AI technology in ultrasonic inspection for intelligent determination is expected to accelerate determination by approximately 50, and will also shortens the construction period by 15 months, effectively improving the speed and quality of the yacht manufacturing process As Taiwan develops larger and more refined yachts, it will create opportunities for industry optimization and transformation, as well as opportunities for the development of key technologies The application of an AI ultrasonic inspection solution for composite materials is the first of its kind in the yacht industry, and is expected to attract more yacht manufacturers with inspection needs The AI ultrasonic inspection solution for composite materials has three major competitive advantages 1 Professional inspection experience and digital database to facilitate process management and analysis 2 Automatic AI determination and identification quickly identifies defects and provides immediate feedback to process engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market