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【2020 Solutions】 ASUS AI Deep Learning Image Recognition Makes Defect Detection Easier

For the manufacturing industry, replacing manual visual inspection with automated optical inspection is common, especially when the yield of 3C or semiconductor products is high. General automated optical inspections often face the bottlenecks of insufficient defect samples and difficulties in qualitative and quantitative recognition. Using AI deep learning for image defect detection has become increasingly significant!

AI detects minute defects, ASUS makes smart manufacturing 'visible'

'Initially, we hoped to promote upgrading with our 3C supply chain partners steadily, assisting the industry to enhance and face international competition,' said Chang Quande, ASUS Global Vice President and Co-General Manager of the Smart IoT Business Group. ASUS Smart Solutions Business Unit uses AI deep learning to perform various workpiece defect detections, and layout after accumulating experiences is a priority task.

ASUS Global Vice President and Co-General Manager of the Smart IoT Business Group, Chang Quande

▲華碩全球副總裁暨智慧物聯網事業群共同總經理張權德

For metal component manufacturers, detecting defects on surfaces is relatively difficult due to the reflection of light, which often causes actual defects to be overlooked. Mastery of optical properties and the specifics of component surfaces is crucial. The ASUS Smart Solutions Business Unit not only has AI experts but also a digital imaging technology team with unique post-processing skills and strong augmentation capabilities. They can achieve correct defect data collection and train AI models efficiently even with a very small number of defect samples. 'General optical inspection accuracy is about 85-90%, and high precision-seeking manufacturers would not use it, as it implies a +/-10% defect misjudgment,' said Chang Quande. Whereas manual visual inspection has an accuracy rate of about 93%, it is labor-intensive and carries occupational hazard risks. ASUS has now enabled AI to achieve 98% accuracy, fully capable of replacing manual inspections and certain traditional optical inspections.

Previously, it took three people to manage quality control across three production lines, now only one is needed

In recent years, many manufacturing industries have been returning to invest in Taiwan. Major metal structure stamping plants have also committed to establishing new factories. ASUS has designed their three-in-one defect detection stations, capturing images through edge computing, uniformly training an AI model, and utilizing the same AI inference workstation to perform defect detection calculations. Quality control stations across various production lines now monitor these processes in real-time. Previously, three production lines required three people for quality control; now, only one is sufficient, increasing the detection rate from 93% to 98% and reducing costs by 5%. Accompanied by the reallocation of human resources, the stamping plant has achieved smart manufacturing and has broken the curse of increased production costs due to returning investments.

ASUS IoT Applications Industry

▲ASUS IoT 應用產業

除了金屬機構件之外,塑膠成型件、印刷電路板等電腦周邊元件生產業及系統組裝業都能運用AI 深度學習影像瑕疵檢測做高精度品管,目前也有半導體業正在優化導入華碩AI 深度學習影像瑕疵檢測,以補足自動光學檢測在晶圓層所抓不到的瑕疵,盼藉由AI的助力突破良率瓶頸,降低人工目測或自動光學檢測已知的誤判所造成的損失,更能利用人工智慧大數據針對品質瑕疵種類做統計分類以歸納出瑕疵形成原因,從源頭改善進而減少製程瑕疵。

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

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【解決方案】AI電眼取代人眼 慧演智能運用AI幫製造業做品管
Using AI vision to replace human vision, Claireye Intelligence uses AI to help the manufacturing industry with quality control

In response to customer demand on a wide variety of products in small quantities in the manufacturing industry, there is an urgent need to find AI solutions from the cloud to terminals Claireye Intelligence provides a solution that integrates software and hardware - BailAI image inspection solution to assist traditional manufacturing industries in improving process efficiency and product quality, thereby achieving the initial goal of transformation After the government declared 2017 to be Taiwan's "First Year of AI," AI startups have sprung up in Taiwan Established in 2018, Claireye Intelligence targets smart manufacturing and provides a platform for AI image analysis and process optimization, using the power of deep learning to detect product defects and abnormalities in the assembly process It assists companies in building infrastructure from terminals to the cloud, which enables automated monitoring of factory production to improve process efficiency and quality Focusing on AI image inspection based on its familiarity with the production line quality control process Shirley Liu, founder and CEO of Claireye Intelligence, is a young entrepreneur She entered the manufacturing industry after graduating from college and held a quality control position in the plastic injection process of hard disk parts "She was already on the production line at the time, and is familiar with the production line process of production machinery" She later switched career paths to marketing and planning, and then worked as an AI product manager When the time came, Shirley Liu decided to start a business, focusing on AI image recognition in the manufacturing industry "The difficulty for enterprises is the lack of an AI development team Even if an enterprise has an AI team, development projects will take a lot of time, at least 6-12 months" said Shirley Liu, who is well versed in the market's pain points The problem that needs to be solved by platforms is to provide services that allow 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on to say that it is impossible for traditional manufacturing industries to maintain a technical team that includes AI engineers, data engineers, cloud architects, and terminal architects Claireye Intelligence specializes in software and hardware integration Enterprises can use the BailAI image inspection platform to easily solve inspection problems on the production line In other words, customers only need to provide images or samples for Claireye Intelligence to carry out model training, model deployment, and system integration, and they can easily use AI technology to optimize and monitor production line processes Participated in the AI New Talent Selection and achieved a recognition rate of over 90 in assembly behavioral image recognition For example, a certain connector manufacturer only has 1-2 AI engineers in its technical team The main problem that needs to be solved is that most operators are on the production line, while quality control and senior managers are not on site, 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【解決方案】台灣軟體科技實力媲美國際 Golface智慧服務促高球轉型
Taiwan's Software Technology on Par with International Standards: Golface's Intelligent Services Transform Golf

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這是一張圖片。 This is a picture.
Smart Construction Site Security Platform

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