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【2020 Application Example】 AI Helps Establish the Best AOI Parameter Model for Passive Components, Reducing Production Costs of Over-Screened Components, Saving NT$2.5 million Annually

Traditional AOI uses limited sample images for inspection, facing the problem of high over-screening rates

In the electronic component manufacturing industry, AOI (Automated Optical Inspection) equipment is often used to measure defects in product appearance. For a long time, AOI measurement equipment has used limited sample images in image processing to compare the appearance of products from different external light sources and angles.

This comparison method can automatically screen for defects in product appearance. However, due to current technical limitations, there are often problems with light source parameter adjustment between product batches. If an inexperienced technician handles these adjustments, it will lead to a decrease in machine utilization rate and high over-screening rates.

The maturity of AI image machine learning has brought new opportunities for the AOI process

In terms of Taiwan's passive components, chip resistors and MLCC currently rank in the top two worldwide in terms of market share in 2019. In the long term, various car manufacturers have launched electric vehicles and smart vehicles, and various countries have also developed 5G-related equipment, which will further increase future shipments of passive components. Therefore, besides expanding new product lines, how to help existing products enhance their competitiveness will be the key to the industry's future international competition.

AOI inspection is one of the common stations in the passive component process, limited sample images are used in the current stage to compare the appearance. However, when switching between product batches, there are often problems with light source parameter adjustment, and the condition of these adjustments will affect the over-screening (mis-screening) of good products in each batch. In each batch of defective products in the industry, on average over-screening (mis-screening) occurs 20% of the time.

Relying on the guidance capabilities of the Southern Taiwan Industry Promotion Center, which has been deeply involved in Southern Taiwan for more than a decade, the company was matched with the AI image recognition technology unit of the Industrial Technology Research Institute (ITRI) to address the pain points of the passive component industry, reducing over-screening in the AOI process and also reduce errors caused by manual adjustment.

Using image recognition technology to reduce the occurrence of AOI over-screening

The technical unit of the ITRI that participated this time used image recognition technology to develop AOI technology for passive component processes in the establishment of AI modules.

In the development process, the company in this case first provided product appearance images and corresponding adjustment parameters, and then used the adjustment logic of current production line personnel to construct a product data set and further establish an AI model. When planning the production line test, the first priority is image recognition rate. Image detection and tag search are combined with comparison by an AI module to output AOI adjustment parameters for reference by online personnel.

Image analysis diagram

▲Image analysis diagram

In the future, we also hope to use the help of machine learning to complete the AI learning curve for machine parameter adjustment, further reduce the over-screening rate of product appearance defect detection, simultaneously solve the gap in on-site professional and technical talent, and increase product yield.

Before and after scenario of incorporating machine learning

▲ Scenarios before and after implementing machine learning

Implementing AI applications in processes to lay the foundation for developing unmanned factories

In the future, we hope the guidance of AI HUB will accelerate the application of advanced process technology and establish AI indicators for each station of the passive component process, which will help domestic production of high quality passive component products and increase product yields and prices. It will use innovative thinking to increase the added value of the industry and continue to lead the passive component industry forward.

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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
Preventing Problems Before They Arise: Leadtek Research Develops AI Technology for Early Detection of Heart Failure Symptoms

With the increase in the elderly population, the incidence of various chronic diseases is rising daily Among these, heart failure is not only a silent killer it has a very long disease course with a high recurrence rate, leading to increased burden on healthcare personnel However, by using medically certified electrocardiography acoustics devices, coupled with AI predictive assessment of heart failure risk and remote care systems, diagnosis can be aided significantly, helping doctors make accurate diagnoses for subsequent patient medical care or referrals Heart failure has a lengthy course and medical expenditure is five times that of diabetes If you find yourself short of breath even with minimal movement, or if you wake up from sleep needing to sit up to feel comfortable, or if you have symptoms such as swollen lower limbs, anxiety, restlessness, fatigue, or a loss of appetite, be cautious These could be signs of heart failure According to statistics, there are about 60 million 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Leadtek, a major graphics card manufacturer, has been investing in the medical and healthcare sector since 2000 Following two heart attacks in 2011 and 2015 experienced by Chairman Lu Kunshan, Leadtek has focused on health big data, independently developing AI technology for heart failure recognition This AI application reads patients' electrocardiograms and phonocardiograms to perform anomaly detection and model prediction of heart failure risk, enabling early detection of disease symptoms Leadtek independently developed heart failure AI recognition technology to predict medical history and risk Leadtek's independently developed heart failure AI recognition technology has the following three judgment functions 1 Prediction of heart failure history Classifies electrocardiogram and phonocardiogram data into 'with hospitalization history of heart failure' and 'no history of heart failure' 2 Risk prediction of heart failure Provides a predictive risk value of heart failure occurrence 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mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and 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【導入案例】化身大型AIOT科技遊樂場 海科館華麗轉身好吸睛
Transforming into a Large-Scale AIoT Technology Playground: The Spectacular Makeover of the National Museum of Marine Science & Technology

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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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