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【2020 Application Example】 Full Inspection AI Implementation in AOI Sealing Process, Reducing Screening Volume by 50%

Miniaturization of products, client demands full inspection

A listed electronic component manufacturer in Taichung, responding to the 5G era injecting new growth momentum into the quartz component industry, especially under the explosion of 5G opportunities, the importance of quartz components will play a more crucial role than in the past in consumer products.

As frequency components move towards miniaturization and at the same time demand high precision, the manufacturing processes are more susceptible to subtle factors, necessitating manufacturers to manage comprehensive data across all aspects including human, machine, material, method, and environment to quickly identify key defective factors in complex production environments.

Differing perceptions of defects, difficulty in enhancing quality consistency

With the trend of miniaturization and complexity of electronic components, visual inspection on the production line has four main functions including measurement, identification, positioning, and inspection, with inspection being the most challenging part as most electronics manufacturers still rely on traditional manual visual inspection.

Taking the PCB industry, where Automated Optical Inspection (AOI) technology has the highest penetration rate, as an example, a research institution once investigated and found that when two individuals inspect the same PCBA board four times, their mutual agreement rate was less than 28%, and the self-agreement rate was only about 44%. Due to differing perceptions of defects among on-site personnel, even automated machine vision can still lead to inconsistencies in product quality due to system settings or differences among quality control staff.

偲捷科技檢測AI化,降低過篩率20%~30%

With the support of the advisory team, collaboration with Sijie Technology aimed at the defects in the sealing process. Based on CNN (Convolutional Neural Network), the integration of multiple models introduced an AI recognition module to aid in the optimization of subsequent AOI tests, aiming to improve the accuracy of inspection equipment.

It is estimated that after introducing AI visual recognition, the over-screening rate could be effectively reduced to 20~30%. Thus, the industry, needing smarter inspection systems, has started applying AI technology to assist AOI equipment in optimizing subsequent screening tests.

AI-powered AOI Inspection Solution Cross-Model Design Concept

▲AI-powered AOI Inspection Solution Cross-Model Design Concept

Sealing AOI Inspection Trial Results

▲Sealing AOI Inspection Trial Results

Reducing false rejects, cutting manual screening workload by 50%

The project, through a deep learning network architecture, reclassifies defects detected including true and false defects, and further classifies them to reduce the false reject rate of the traditional AOI solution. This is anticipated to further aid manual inspectors in reducing more than 50% of the inspection screening volume, addressing current production line issues of relying heavily on manual re-inspection and low efficiency.

Future goals include integrating robotic arms for automatic loading and unloading, and analyzing defect causes to optimize production process parameters.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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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」

這是一張圖片。 This is a picture.
Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

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demand convenient life, food consumption patterns value dining experiencesimple and fast, and are equipped with mobile phone-connected ordering models, and hot drinks and Fresh food delivery is the focus of two major trends The location, items sold, consumption methods and multiple payment methods are the focus of market growth for smart vending machines In terms of convenience, Taiwanese consumers still prefer to purchase vending machine food near stations, airports, schools, and businesses in business districts Various payment methods are also gaining more support from consumers, indicating that in the future, automatic Vending machines can be developed in two directions diversified items and diversified payment methods AI sales forecast technology integrates back-end management to achieve precise marketing purposesDue to the wide variety of products, it is difficult to know the performance of products under different factors such as season, market conditions , promotional activities, etc, it is easy to cause out-of-stock or over-inventory situations Magpie Life has specially developed "AI sales forecasting technology" and integrated it into the back-end management system, hoping to lock in customer purchasing preferences and intentions through data analysis In order to achieve the purpose of precise marketing, make accurate business decisions and effectively allocate limited resources The introduction of AI systems can achieve the three major goals of precise marketing, inventory management and supply chain management This system is a replenishment decision-making aid designed specifically for supply chain managers It uses AI to predict future sales demand, helping companies effectively optimize production capacity, inventory and distribution strategies Its overall system architecture includes1 Data exploratory analysis function Provides automatic value filling, automatic coding and automatic feature screening functions for missing values in the data 2 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rent and sell them at a lower cost than the store rent Cost of running a retail business Two cooperation models, machine sales and leasing, are provided, and the choice is based on the evaluation of the industry 2 Various types of products are put on the shelves Products are sold anytime and anywhere 24 hours a day Up to 60 kinds of diversified products can be put on the shelves Large transparent windows enhance the visibility of products Regular replenishment and tracking of product sales status are available, and product types can be adjusted according to needs Recently, the line between the Internet and the physical world has blurred, the way customers interact has changed significantly, and consumer demand is changing and personalized The retail industry is facing unprecedented challenges and uncertainties, and mastering data has become key AI data analysis solutions can help the retail industry quickly activate large amounts of data, create seamless personalized experiences, optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】AI嘛會煮咖啡 無人烘豆機靠AI 精準設點與培養忠實客群
AI Can Make Coffee! Autonomous Coffee Roasters Relying on AI for Precise Location Setting and Cultivating Loyal Customers

Have you had your morning coffee yet Over the past decade, Taiwan has gradually formed a coffee drinking culture With the advancement of AI technology, autonomous coffee roasters can now rely on AI for precise location setting while also cultivating a loyal customer base Let's see how this is done According to the International Coffee Organization ICO, Taiwanese consume approximately 285 billion cups of coffee annually, with the coffee market in Taiwan estimated at 80 billion TWD, growing about 20 each year In recent years, the 'drinking coffee' culture in Taiwan has become synonymous with popularity, with coffee being the most frequently chosen daily beverage by 65 of the population Coffee enthusiasts, particularly the more avid ones, are willing to pay more for coffee beans that suit their tastes An increasing number of unmanned drink kiosks have also begun to appear in the Taiwanese beverage market Unmanned coffee beverage shops face difficulties in expanding quickly, primarily due 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persistently rising rent and personnel costs However, the initial assessment of store locations still requires hourly labor expenses for manual estimation of customer flow, leading to possible miscalculations of both on-site consumers and passerby traffic These inaccuracies may prevent precise real-time analysis of customer flow, or even misguided estimations of operational efficacy after a trial run, thus missing the optimal timing for loss-preventing location retraction Raysharp Electronics introduces autonomous coffee roasters equipped with AI-based people counting analysis and facial recognition Raysharp Electronics combines AI people counting analysis and facial recognition with the coffee trend known as 'black gold', addressing the preferences of numerous coffee connoisseurs in Taiwan who enjoy personally selecting coffee beans at bulk stores and frequenting high-quality grinding cafes or chain coffee shops A new concept for the first autonomous coffee roaster offering choices based on the origin, variety, and roasting methods of coffee beans has emerged AI coffee roasters enhance customer loyalty and materials management efficiency by 20 For the advanced development of autonomous coffee roasters, Raysharp Electronics engineers have equipped the AI NVIDIA development platform on the basis of TCNNFacenet Through AI, tens of thousands of images related to gender and age are used for sample training, allowing even first-time coffee roasting customers to be easily classified using unfamiliar face recognition This gains consumer trust, enhances willingness to use, and allows for recording purchase information and future product recommendations, leading to consumer purchase behavior analysis This information helps owners tailor future material preparation based on consumer preferences for different coffee beans, reducing raw material transportation and storage issues, and improving material management efficiency by 20 Moreover, by placing these autonomous coffee roasters in high-traffic areas, owners can use cameras to capture the crowd and assess whether the machine location has an adequate customer base, quickly analyzing whether to reposition the machines, and more easily targeting the best locations for middle and high-end coffee lovers The unmanned coffee roaster features a professional roasting mode interface, providing options based on the origin and variety of coffee beans, their roasting methods light, medium, deep, and related temperature, wind speed, and timing settings If improvement needs arise during the process, engineers can adjust firmware parameters and also assist in integration with the owner's ordering system Staff members briefly describe the operation of the autonomous coffee roaster 'Black Gold' penetrates deeper into coffee shops, science parks, and commercial buildings through AI This autonomous coffee roaster targets coffee connoisseurs and can be placed in middle to high-end coffee shops to roast more customized coffee beans than those available in bulk stores Upon completing a batch of coffee beans, it immediately provides them to professional technicians within the coffee shops for grinding and manual brewing The remaining roasted beans can also be taken home for brewing and enjoyment It also adds value to coffee shops by better understanding consumer preferences for coffee beans and launching more customer-attracting drink promotions and appropriate inventory management In addition to coffee shops, the autonomous coffee roaster can also utilize AI-based people counting analysis to precisely set up near scientific parks and commercial buildings, offering high-quality coffee beans for office brewing to internal employees with high coffee consumption needs Furthermore, implementing a physical membership system can initiate coffee bean purchase promotions or periodic payment incentives, thus attracting new clients and cultivating existing customer loyalty and retention The operation interface of the smart autonomous coffee roaster「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」