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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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這是一張圖片。 This is a picture.
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With the introduction of theAOIAIWith the introduction of the system, we can improve product yield, reduce costs, and from a business perspective, increase customer trust and sales revenue Moreover, AIit has advantages that are difficult to imitate, unlike other equipment that can be bought with money, making it hard for our competitors to catch up with us Our company's current development We are committed toIOTsmart manufacturing our systems already include smart materials systems, environmental humidity control systems, anti-miscarriage systems, smart procurement computation systems, smart inventory systems, solder paste management systems, and production management systems We have asked other manufacturers about the possibility ofAIinspectingPCBAsurface defects, each hoping that we would purchase their equipment, but none were effective upon verification After discussing with IT service providers, we defined it asAOIAIa feasible operational model Tzuhong Technology has invested inAOIAIan inspection plan to checkSMTtext on components, solder joints, polarity, missing partsand usingAIto replace manual learningAOIand define the 'potentially defective' parts, enhancing productivity and reducing misjudgment rates Industry pain points Taiwan faces a severe labor shortage, especially those willing to perform visual inspections are few and typically older, increasing the frequency of missed inspections Thus, the most critical bottleneck in the pursuit of high-quality electronics has become post-production inspections Previous consumer products with undetected anomalies were acceptable within a certain ratio However, in the automotive industry today, undetected defects could lead to fatalities hence, the automotive industry has extremely high quality demands To survive in the automotive supply chain, we must address the issue of undetectable anomalies Moreover, as wages in Taiwan continue to rise, we can only endeavor toAIreplace traditional manpower with technology, otherwise, even if the anomaly leakage problem is resolved, the relatively high labor costs will still prevent competitiveness in this industry Application technology and explanation Initially,Figure 1,PCBUpon emerging,Reflowsystem, it will undergoAOIwill undergo inspection, dividing into 'suspected defective' and good products At this point, the 'suspected defective' portion accounts for20manual review for these20parts, further classifying the 'suspected defective' portion into good and defective products With We aim to leverageAItechnology, to shift from manual re-inspection of these20technology, we aim to replace manual review of 'suspected defective' products withAIand after review, the results still yield 'good' and 'suspected defective' products, but now 'suspected defective' comprises only3thus reducing the workload of Tzuhong's employees from20down to only3In theory, it isAOIIn theory, after inspection, it is further reviewed byAIbut it appears to go throughAOIonly, so we call this technologyA0IAIDetectionFigure 2。 The original AOI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOI information on defective products, then manually re-inspect one by one to determine if they are defective AOIAI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOIinformation on defective products after, then proceed byAIfirst performingAOIre-assessment of defective products, outputtingAIinformation on defective products afterward, then manually re-inspect one by one to determine if they are defective Process differences By introducing theAOIAIsystem, not only can we enhance the efficiency and yield of visual inspection personnel, we also have this timeAIexperience in system introduction, we will also incorporateAIthe use of big data into Tzuhong's existing smart manufacturing systems, further enhancing the performance of our smart manufacturing systems and reducing the pressure on employees Difference between pre and post-introduction Promotion strategy 1 Similar field diffusion allSMTmanufacturers face bottlenecks in inspections leading to shipment delays introducing this system can solve the severe labor shortage issue and enhance shipment speed and quality, allowing self-promotion to customers or through equipment dealers to cater to relevant needs 2 Cross-industry expansion plans negotiate withAOImanufacturers to directly integrateAIthe system intoAOItheir systems, enhancing their market competitiveness Profit strategy 1 In collaboration withAOImanufacturers, collect licensing fees 2 Direct sales toSMTthe manufacturing industryAIsystems 3 ProvideSMTmanufacturing industryAOIAIsystem subscription model「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-09」

【解決方案】連聯合國都買單 悠由數據應用運用農業數據搶攻全球商機
Even the United Nations is on board! Yoyo Data Application captures global business opportunities with agricultural data

Nearly 2,000 days in the fields have made Yoyo Data Application a top player in Taiwan’s agricultural data sector Their comprehensive grasp of crop yields, production periods, and prices has enabled them to collaborate with the United Nations The service area for agricultural land skyrocketed from 24 hectares to over 6,000 hectares in less than three years—a 250-fold increase For Wu Junxiao, founder and CEO of Yoyo Data Application, aligning with global environmental trends and becoming a data company at the intersection of climate technology and the green economy to serve the global market is his ultimate entrepreneurial goal Wu Junxiao, originally an engineer, joined the Industrial Technology Research Institute in 2010, where he honed his profound technical and data science analytic skills 'At that time, I was working in data analysis engineering, and almost all data-related materials would be directed to me Additionally, I worked on indoor cultivation boxes, planting vegetables and mushrooms, hence planting the seed of entrepreneurship by integrating agriculture with data analysis,' Wu recalls Since 2016, Wu Junxiao has been frequently visiting farms to 'embed' himself among farmers and agricultural researchers, chatting and sharing information systematically, which quickly established his agricultural know-how Solid data analysis capabilities have even convinced the United Nations In 2017, he left the Institute to start his own business and founded Yoyo Data Application in 2019 Today, many agricultural businesses are his clients, with service areas rapidly climbing from 24 hectares to over 6,000 hectares, expected to surpass 7,000 hectares in 2022 His clientele includes markets in Japan, Central America, and even entities under the United Nations like the World Farmers Organization, which utilizes the 'Yoyo Crop Algorithm System' supported by Yoyo Data How exactly does Yoyo Data Application manage to impress even UN agencies The 'Yoyo Crop Algorithm System' developed by Yoyo Data Application accurately predicts the production period, yield, and prices Firstly, due to Wu Junxiao's precise mastery over agricultural data, Yoyo Data Application's clients don't necessarily need sensors or other hardware devices 'Sensors are expensive and if you buy cheap devices, you just collect a lot of noise or flawed data, which is useless,' Wu explains He continues, 'Collecting data doesn't necessarily require sensors our data solutions can solve problems more directly and effectively' For instance, one of Yoyo Data Application's products, the Yoyo Money Report Agri-price Linebot, developed in collaboration with LINE in 2020, gathers data on origin, wholesale, and terminal prices spanning over 10 years, driven by Yoyo Data’s proprietary AI algorithms This enables the system to autonomously learn about agricultural product trading prices, using big data and AI to perform price prediction analysis, thereby helping buyers reduce transaction risks and expanding the data application to the entire agricultural supply chain Regarding banana prices, the accuracy of price predictions increased from the original 70 to 998 Wu Junxiao notes that both buyers and farmers are very sensitive to prices Now, through the Yoyo Money Report service, both buyers and farmers can precisely understand the fluctuations in agricultural product prices Yoyo Data can also provide customers with optimal decision-making advice based on predictive models for crop growth, yield, and price estimations Currently, price predictions cover 28 types of crops Precise estimates of production periods and price fluctuations allow Yoyo Data to provide differentiated services based on data analysis The 'Yoyo Crop Algorithm System' provided by Yoyo Data Application incorporates a 'Parameter Bank', usually collecting 200-300 parameters, not just straightforward data like temperature and humidity, but also data divided according to the physiological characteristics of the crops Through effective dynamic data algorithms, it can accurately calculate when crops will flower and when they can be harvested, what the yield will be, and so forth For instance, the prediction accuracy of the broccoli production period is 0-4 days, with the flowering period predicted this year to be precisely 0 days, perfectly matching the actual flowering time in the field In these dynamic calculations, a 7-day range is considered reasonable, and the average error value of Yoyo Data's predictions typically ranges from 2-4 days, with most crop production period accuracies above 80 Through effective dynamic data algorithms, over 120 global crops can have their production periods and yields accurately estimated Using these effective dynamic data algorithms can set estimates for production quantities, helping adjust at the production end Yoyo Data Application's clientele primarily includes exporters of fruit crops like pineapples, bananas, guavas, mangos, pomelos, sugar apples, Taiwan's agricultural production is highly homogenized, often leading to a rush to plant the same crops and resulting in price crashes Yoyo Data Application helps clients differentiate their offerings Thus, Wu Junxiao positions his company as a boutique digital consultant, carefully selecting clients for quality over quantity He notes that Taiwanese agricultural clients focus on how to improve yield rates, even categorizing yield rates by quality, aiming for high-quality, specialized export markets whereas international clients prioritize maximizing per-unit yields, showing different operational approaches in domestic and international markets In addition to agricultural fruit, Yoyo Data Application has also extended its services to the fisheries sector, including species like milkfish, sea bass, and white shrimp, all using the same system to establish various parameters related to the growth of fish and shrimp, such as when to feed and when to harvest, and the anticipated yield, 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【導入案例】海量數位工程AOI機器智能手臂檢測系統 大幅提高瑕疵檢測精準度
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 - robotic arms and machine vision By integrating robotic arms with AOI for dynamic multi-angle AI real-time quality inspection, the limitations of fixed inspection systems are addressed, and visual inspection techniques are enhanced by leveraging artificial intelligence, further elevating the sampling of images from flat to multi-dimensional and multi-angular Selected the automotive industry as the real-world testing ground to quickly respond to customer needs The AOI intelligent robotic arm detection system, utilizing AI technology including unsupervised learning, supervised learning, and semi-supervised learning, allows operators to use unsupervised deep learning techniques to learn about good products even when initial samples are incomplete or there are no defective samples, applying it in the visual inspection of automatic welding of car trusses This can solve issues of limited angles with fixed machinery before implementation, less precise diagnostics, and high false positive rates Automotive components are high in unit price and demand a stricter defect detection accuracy In industries that have adopted AI services, the automotive manufacturing sector was chosen as the real-world testing ground Massive Digital Engineering states that the automotive industry mainly consists of related component manufacturers and components typically have a higher unit price, hence requiring more in terms of quality inspection and yield rates, and demanding stricter accuracy Therefore, the automotive sector was chosen as the area for introduction By using a robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection, not only can the defect quality error rate of automotive components be improved, but the fixed-point AOI optical inspection can be enhanced to meet the measurement needs of most industries and finally, establishing a third-party system platform to build an integrated monitoring system platform, enabling immediate response and action when issues arise This system allows for recording and storing important data of products leaving the factory, serving as a basis for future digital production lines and virtual production At the same time, in the event of defects, it can immediately connect to Massive's MES monitoring system, quickly responding to the relevant manufacturing decision-making department, subsequently utilizing ERP systems for project management and reviews, effectively improving production efficiency and reducing production costs Helps to reduce communication costs and aims to become an industry standard In terms of industry integration, it provides a foundational standard for data continuity among upstream and downstream businesses, reducing communication costs within the supply chain Through certification of the contract manufacturers and brand owners, there is a chance to become the industry standard configuration Through the data database established by this project, operators can further optimize their supply chain management solutions using big data analysis Data Analysis, based on data, establish forecast planning, and utilizing technology to link upstream and downstream data of the supply chain, accurately controlling product quality In the future, when interfacing with European, American, and Japanese markets, which demand highly fine-tuned orders, operators can respond and integrate the industry supply chain Supply Chain more swiftly Ultimately, through the benchmark demonstration industry's field verification, such as with the automotive component manufacturing industry used as the benchmark demonstration field, by implementing the robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection system project, the supply chain connection between automotive contract manufacturers and OEMs can be optimized, becoming the industry standard Further seeking more AI teams to join the cross-industry development on the field collaboration platform, driving the overall ecosystem combining AI innovation with field application Self-driving vehicle developed by Massive Digital Engineering「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」