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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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Massive Digital Engineering AOI Intelligent Robotic Arm Inspection System Significantly Improves Defect Detection Accuracy

Taiwan is known as a manufacturing powerhouse, yet quality defect detection has always been a chronic sore point in production lines While AOI equipment is available to assist, most use fixed machinery which are limited by angles, resulting in less precise diagnostics and high false positive rates Massive Digital Engineering introduced an AOI intelligent robotic arm detection system that effectively reduces false positives and increases the accuracy of defect detection Generally, the yield rate of products affects the costs for enterprises and the return rate for customers The quality defect detection process in the manufacturing industry often necessitates a substantial amount of quality inspection labor Although there is AOI equipment to assist, these tools are mostly fixed detection machines Fixed cameras are easily limited by angles, resulting in less precise diagnostics and high false positive rates Thus, personnel need to re-screen and inspect afterwards, often manually visual inspection misses defects on average about 5, and can be as high as 20 Three major pain points in manufacturing quality detection Robotic Arm AOI with dynamic multi-angle inspection helps to solve these issues According to the practical understanding by Massive Digital Engineering, there are three main pain points in detecting product quality within the manufacturing industry Pain point one, manual inspection of product quality is prone to errors Currently, the manufacturing industry largely relies on human labor to inspect product appearance, but human judgment often entails errors, such as surface scratches, color differences, solder appearance, etc The error rate in defect judgment is high, and can only be inspected at the finished product stage, often leading to whole batch rejections and high costs in labor and production Pain point two, inability to quantify and record data from quality inspections Traditional manual inspections do not maintain inspection data, which makes it difficult to assign responsibility when quality disputes occur Moreover, high-end contract manufacturing orders from overseas brands often require traceability and corresponding defect records, which traditional human inspection methods struggle to meet Pain point three, limitations of traditional AOI visual inspection systems Current manufacturing uses AOI visual inspection systems, which due to the limitations of visual software technology, employ fixed cameras, fixed lighting, and single-angle operations This method may handle flat or linear-shaped products like rectangular or square items at a single inspection point However, it is more challenging to implement for products with complex shapes eg, irregular automotive parts, requiring multi-point and multi-degree inspections Massive Digital Engineering developed an AOI intelligent robotic arm detection system, effectively improving the accuracy of defect detection To address the pain points in quality inspection in manufacturing, Massive Digital Engineering initiated the concept of developing a multi-angle, movable inspection device, starting with the combination of two representative technologies in factory automation - 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[2023 Case Study] AI Steps into Philanthropy: Stylish Tech at Food Banks

Taiwan Food Bank AssociationHereinafter referred to as 'the Association'With the mission of providing food aid, poverty relief, reducing food waste, and building a hunger-free network, there are locations across Taiwan that gather donations from wholesalers, intermediaries, retailers, manufacturers, and even generous individuals These sites also rescue food that would otherwise be discarded, properly allocate and distribute it to needy households, thus aiding local vulnerable families55Food banks at various locations collect daily donations from wholesale stores, intermediaries, retailers, manufacturers, and even benevolent individuals from all over Taiwan These places also rescue about-to-be-discarded edible materials, properly sort them, and distribute to needy households, assisting local vulnerable populations However, each location requires significant human and volunteer resources to manage daily operations using traditional methods of communication with non-profit organizations and donors After receiving donations, these resources are then allocated to needy families or individuals There is a potential issue of uneven distribution of resources due to a lack of digitalization and integrated information management in these processes Warehouse and Transportation Centers and Mini Food Banks Distributing Resources to the Disadvantaged The location under validation by the Kaohsiung Charitable Organizations Association,Hereinafter referred to as 'Kaohsiung Charity' In109year6month24Officially inaugurated Taiwan's first 'Food Bank-Warehouse and Transportation Center' at a location measuring200square meters, enhancing the efficiency of food resource redistribution, proper storage, and management So far, nearly two hundred tons of vegetables and fruits have been saved, serving over a hundred organizations and benefiting over5thousand vulnerable households, and continues to serve19mini food banks, with planned completion across multiple districts in Kaohsiung, distributing food resources to over10ten thousand vulnerable families Kaohsiung Charity 'Food Bank-Warehouse and Transportation Center' in the Dasha Community Photo Source Kaohsiung Charitable Organizations Association Challenges in Labor and Food Resource Management Facing the needs of a large number of economically disadvantaged families, the management of the 'Food Bank-Warehouse and Transportation Center' is particularly critical During procurement, tasks such as sorting, purging, and bookkeeping must be performed, while during shipment, food resource needs suggested by social workers must be followed These activities rely on manual judgment and accumulated experience Many volunteers involved are elderly and have limited physical strength, making warehouse tasks physically demanding and recruitment challenging If a large batch of food resources arrives, space and manpower are consumed in sorting and inventory management, raising concerns about the effective use of resources and turnover rate This highlights the challenge of scaling up food bank services while lacking corresponding labor and material management systems At the same time, food bank resources come from various donations, thus they vary greatly in type, shelf life, standards, and quantity Volunteers at mini food banks, mostly also elderly, must handle multiple responsibilities such as case services, food resource management,resource allocation, and resource development Sometimes they must also explain and accept immediate, large quantities of specific resources, such as adults receiving baby formula 'Food Bank-Warehouse and Transportation Center' Resource Inventory Relies Entirely on Manual Labor Mini Food Bank Volunteers Handle Multiple Responsibilities Photo Source Taiwan Food Bank Association Reducing Scrap Resources60 Increasing Speed of Resource Transfer80 To enhance resource management and ensure effective use of materials, and to address personnel shortages, this field validation case has 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Testing Seat Contact Components AI Intelligent Flaw Detection

With rapid development in 5G, AIOT, automotive electronics, and other downstream sectors, the entire supply chain is expected to benefit from this consumer market As product demand momentum gradually increases, increasing production efficiency and reducing operational costs become the most important issues In order to meet the needs of customers for various packaging types, Yingwei Technology has been committed to developing highly customized test seats However, a resulting pain point is the inability to mass-produce and fully automate operations with machines some tasks still rely on manual execution In this project, the probe part of the test seat was outsourced in 2021, and under current and future large-scale demands, work hours, costs, supply, and quality are issues Yingwei faces The company achieves a defect detection rate of 9995, which seems high, but with an average inspector able to inspect 10,000 needles per day, there would still be 5 defective needles On a test seat that is only 3 cm wide with approximately 1,000 needles, just one defective needle could potentially lead to faulty testing at the customer end As the current operational mode relies on manual visual inspection, external factors such as fatigue or oversight of personnel, and subjective judgment by inspectors may lead to the outflow of defective products, which necessitates strict quality control of contact components We once sought to utilize optical inspections Rule-based for controlling the quality of appearances, but the metallic material of the contact components leads to light scattering, background noise interference, background scratches, and material issues that could result in misjudgments Therefore, we decided to look for AI technology service providers to solve our detection difficulties Developments of Dedicated AOI Line Scan Equipment To meet the needs for inspecting thousands to tens of thousands of probes within our company's IC test seats, traditional surface imaging and individual needle imaging would be too slow to achieve rapid inspection and labor-saving goals In response, the service provider proposed a trial with an AOI dedicated line scan module solution Utilizing a width of 63mm on the X-axis for reciprocal scanning of all probes on the test seat, the tests allowed for the simultaneous scanning of 8-9 probes, significantly enhancing the future detection efficiency of AOI machines This project will proceed with the aforementioned innovative Proof of Concept POC, focusing on the development of the line scanning equipment and performing imaging, learning, and training on both normal and abnormal probes provided by our company, with initial AI model training aimed at preliminary approval This project's customized line-scan imaging module Ideal future imaging result illustration A Single AI Technology Solution for MeasurementDetection Needs Unified use of AI DL CNN learning methods, instead of the current Rule-based system which necessitates defining each defect individually, to meet the needs for abrasion measurement and appearance defect detection of malfunctionsforeign objects When the same machine uses both measurement and detection technologies, not only does it increase costs, but it also affects the detection speed Hence, the service provider recommends the use of a line scan device for imaging Its resolution is sufficient for AI to simultaneously determine appearance defects and assess the condition of needle tip abrasion, as detailed below Line scan pixel imaging displaying needle tip abrasion conditions This AI detection technology meets both measurement and inspection needs for Yingwei, not only bringing more benefits to future probe testing but also introducing an innovative axis in AI technology Change the method of human inspection, enhance work efficiency and product quality After combining both hardware line scan and software AI model training approaches, we successfully ventured into new AOI detection applications Following the AI implementation POC, including the development and validation of a customized line scan module and an initial AI model, the plan is to officially develop the AOI machine next year and integrate it into the IC test seat production line Future Prospects Probe manufacturers upstream and downstream IC factory users both have needs for the AOI inspection machine upstream can ensure probe quality before leaving the factory, while downstream users can use this machine to regularly inspect the condition of numerous IC test seats in hand Given the future demands, the AOI machine is poised to have a significant positive impact on the IC testing industry in the foreseeable future 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」