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【2024 Solutions】 AI Defect Intelligent Detection - Energy Reduction Smart Monitoring Solutions

AIIntelligent Defect Detection-Smart Monitoring Solution to Reduce Process Energy Consumption

When there are over2ten thousand chip resistors on a ceramic substrate, how should one quickly detect defects? The answer is:UsingAIto detect

In the era of rapid technological development, Leike proudly announces significant advances in its laser processing technology, thanks to the innovative applications of artificial intelligence(AI)Leike is committed to integrating advancedAItechnology into laser processing machines, and in2019year, in collaboration with partners, developed the world's first laser machining system that integratesAItechnology, and on this basis further developed in2023year the first ceramic substrate inspection machine that integratesAOI+AI+LASERtechnology.

Smart Ceramic Substrate Inspection Machine
Smart Ceramic Substrate Inspection Machine

Through the introduction ofAIand machine learning, along with the accumulation of big data samples, the system becomes smarter, which has led to improved product yield within one year5%dramatically reducing the inspection time from originally2minutes/per piece to just20seconds/per piece, drastically lowering inspection costs, enabling efficient initial detection and post-laser marking to reduce waste in subsequent processes, diminishing overall carbon emissions of the site, allowing the automatic generation of detailed inspection reports for data analysis and optimization, which helps increase equipment capacity, reduce human error, enhancing the value of Leike's equipment, and strengthening the international competitiveness of the country's electromechanical industry.

Leike Corporation(Laser Tek)Founded in1988year, and officially listed as a publicly traded company in2002year. Since its establishment, it has become a leading global service provider and manufacturer of electronic packaging materials,SMDElectronic Packaging Materials,SMTinspection equipment, and laser systems.

Leike's general manager, with years of laser integration experience, observed that passive component customers can produce over20With many years of laser integration experience, he observed that the production capacity of passive component customers can exceed10billionSMDcomponents every month, but withSMDcomponents per month. However, as component sizes continue to miniaturize, defect detection during production becomes increasingly challenging. With thousands to millions of components on a single ceramic substrate, and as component sizes decrease and their laser processing positions become smaller, the difficulty of detection increases, making production inspection a critical process.

R-SMD Production Inspection Process
R-SMD Production Inspection Process

AOIproblems of yield overkill relying onAIfor oversight,

Yet,AOIthe inspection machine is a widespread and mature type, but the high accuracy on the marketAOIuses a technique that captures small images in a single shot and stitches them into a larger image. Although accurate, this method requires more time for small-sizedSMDcomponents, which are more likely to be influenced by environmental factors like lighting and vibration that can cause misjudgments; as a result,AOIyield rate can only be estimated by sampling, and components with poor sampling yield are not removed individually but discarded together with good ones; manual re-inspection not only increases costs, but the lack of unified inspection standards ultimately results in about2%-5%products that are not detected as defective enter the subsequent manufacturing process monthly at least2,000thousands of such defective componentsSMDthat were not initially detected causing ongoing printing and machining inspections in subsequent processes. Regardless of the waste of ink materials and energy, which increases the cost burden, this also accelerates equipment wear and shortens operational life. Each stage of waste increases the site's carbon emissions, unfavorably impacting the company's carbon footprint.

Post-Adjustment Sample Photo Example 0402
Post-Adjustment Sample Photo Example 0402

TraditionalAOI High false positive rates in Automatic Optical Inspection (AOI) are a major production issue for manufacturers, particularly in the passive components industry where 'it's better to mistakenly reject a hundred than miss one'—a high standard, often leading to AOI setting extremely high parameters which makes devices overly sensitive. Excessive stringency in data parameter settings can lead to high false positive rates. For instance, if the dirt contamination on passive components resembles the color of the printing layers,AOI the misjudgment rate could reach 7 percent.

Contamination and Print Layer Color Similarity Make AOI Prone to Errors

Contamination Dirt

Contamination Dirt and Print Layer Color SimilarityAOIProne to Misjudgment

Raytek stands apart from otherAOIsuppliers by discarding the stitching of small images or line scanning, effectively preventing data loss and discrepancies caused by hardware or environmental conditions during image processing. It employs a large-array photodetector coupled with custom high-resolution lenses, using specialized imaging for composite processing. Throughout this process, each pixel of the photodetector contains light information captured from various positions. By combining this data, the image resolution and detail are enhanced, reaching a resolution of millions, and with multiple automatic light adjustments, a single shot can manage70*70mmachieving an image resolution up to5umobtaining clear images, then throughSmart-AItechniques for analysis and selection.

Three Innovative Methods to Achieve Rapid InspectionSmart -AI

Raytek's General Manager shares, rapidly implementingAItechnology and reducing inspection computation time, further developingSmart-AIthree major approaches:

Method one, initially useAOIto quickly separate good products from those with controversial defects, focusing the detection on the minority of defective identifications.

Method two, an automated labeling platform simplifies the training issue: by using cameras to collect data from machines, automatic labeling replaces manual labeling, progressively training to improve accuracy. The simpler the problem, the less data needed for training.

Method three,AOIandAIDual-track Advancement: In the smart manufacturing process, relying solely onAOIorAIis not enough to accomplish the task alone, it must be preceded byAOIfirst marking the characteristics, distinguishing between good and defective parts, then usingAIa method for labeling and training. Subsequently, by utilizing a repeating cascade effect, the detection benefits are greater as more training data accumulates,AOIreducing the ratio of errors,AIand gradually increasing the accuracy ratio.

Post Adjustment Object Detection and Training
Post Adjustment Object Detection and Training

Through three major methods gradually building system reliability, and categorizing data for defect sorting, ultimatelyAIreturning the judgement results to the main system, utilizing laser machining to control truly defective products at the front end of the process, reducing the inflow of defective products into other stations, thus minimizing losses due to repeated tests or reprocessing.

Leading in smart laser equipment, chooseLASERTEKthe right one!

Continuously developed by the Taiwanese brand Raytek, combiningAIsmart detection and laser processing equipment to progressively build a smart monitoring solution stack from raw materials, products, testing, laser equipment, etc., aiming at reducing the energy consumption of the production process, implementing semiconductor advancements, /substrates and component processing among other fields, producing equipment products capable of meeting the end-user demands under low carbon conditions, rapidly and with quality products and services expanding both domestic and international markets, enhancing the global competitiveness of localMade in Taiwan(MIT)equipment.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-12-12」

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這是一張圖片。 This is a picture.
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【解決方案】讓硬碟裡的音樂重生 愛飛媒平運用AI為影像找到最佳拍檔
Rebirth the music on the hard drive. Aifei Matchmaking uses AI to find the best partner for the image.

A young girl, alone in Los Angeles, USA, is looking for a dream, a dream that allows the music creator to find her soulmate again with the music she buried deep in the hard drive Li Zihui, the founder of iFeiMedia, has a background in science and engineering, but she has a strong gene for musicians In order to help global musicians create music and find the "best partner" who can successfully match them, she founded iFeiMedia Ping Company provides a one-stop AI video and music matching platform AV Mapping to help video creators quickly find copyrighted original music One-stop AI image music matching solution to find innovative business opportunities for music creators Generally speaking, in the past, video creators had to work on video music, including composing lyrics, music, and finding copyrights It usually took two weeks Through the AV Mapping video and music matching platform, it can be instantly matched to a suitable video in 10 seconds Music, musicians can also remarket their creations to gain profit sharing, creating a win-win situation This new, decentralized operating model is also favored by the descendants of the late Taiwanese music master Li Taixiang On the platform, you can relive a time when music creation was free to fly Li Zihui has practiced piano since she was a child, participated in choirs and wind bands, and composed her own music Although she studied science and engineering in college - the Department of Surveying and Spatial Information at Cheng Kung University, she joined the imaging team to work on soundtracks from her junior year , and went to the Applied Music Department of Nanyit University to audit After graduating from college, Li Zihui decided to obey the voice in her heart and become a music dreamer Aifei Matching provides a one-stop AI image and music matching solution Aifei Matching provides a one-stop AI image and music matching solution, which mainly uses artificial intelligence image recognition and music analysis Image creators can search and match suitable music by themselves on the platform, and use the system to It can shorten the duration of the soundtrack from 8 hours to a few seconds, a significant reduction of nearly 2,000 times Li Zihui said that in addition to creating suitable soundtracks, traditional video scoring projects also require a lot of time and cost in communication and search, including subsequent post-production processing such as arrangement and recording, and music licensing, which are even more time-consuming and labor-intensive With the assistance of AI, creators can focus all their efforts on creation without worrying about finding suitable music or having their music copyright stolen Integrated virtual and real marketing, from transaction to contract signing with one click At present, AifeiMeiping's music database has a total of 60,000 tracks in more than 60 categories, covering music from Europe, America, Asia and other parts of the world, including pop, EDM, rock, Irish music, etc The original decentralized concept of iFlyMediaPing further protects the rights and interests of musicians Musicians on the platform can set their own prices and track the transaction process, achieving open, transparent and decentralized features There are currently more than 7,000 video and music creators on the platform Music creators who successfully trade on the platform can share profits of more than 40, up to 50 The two parties transact and complete the contract on the platform, and the procedure is very simple AVMapping has a total of 14 AI models, making it easy to find speed dating music Li Zihui said that the AI image music matching solution has a total of 14 AI models The method is to disassemble all elements, conduct music analysis through image recognition and text recognition, and then use machine learning algorithms to train extensively The characteristics of images and music are listed, which can quickly match the soundtrack that suits the image situation, atmosphere, and rhythm In addition to online matchmaking transactions, Aifei Matchmaking also holds physical concert events, inviting music and video creators to participate The content of the event revolves around the display of AI video soundtracks, and a video directed by the director is used on-site to allow music creators to participate PK soundtrack or take out a demonstration video and let AI match it It only takes 10 seconds The images and music matched by AI are very accurate in terms of mood and atmosphere, which amazed the participants Three years of research and development won the Red Dot Design Award, using technology to support the development of music and art Aifei MatchPing spent three years of research and development, and the platform was officially launched in August 2021 In January 2022, it participated in the CES event in Las Vegas, USA, which attracted great attention from reporters present and received more than 100 awards in total media reports, the number of 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token is currently very popular in the art and cultural market What is the possibility of introducing it into the field of video and music Li Zihui said that the current transaction fees gas fees of Ethereum remain high, and coupled with the conclusions she obtained from attending many gatherings in Los Angeles, the acceptance of NFT is still in the process of brewing However, Aifei Matching is still optimistic about the future of NFT Trend, in the foreseeable future, relevant technologies will still be introduced into the AV Mapping platform to provide more diversified trading methods In order to rapidly expand overseas markets, Li Zihui continues to seek funding from international strategic investors in San Francisco At the same time, due to the appropriate control of the epidemic in Los Angeles, the industry is gradually recovering, and Li Zihui also participates in many offline creative gatherings Aifei Media hopes to become a bridge connecting images and music, introduce well-known user cases in the international market, and let more creators see the power of the platform Aifei Media Ping also frequently reports good news After winning the DSA Digital Advertising Singularity Silver Award and the AWE Female Entrepreneurship Best Potential Award co-organized by the American Institute in Taiwan and META, the one-stop AI video and music matchmaking company founded by Li Zihui The platform AV Mapping also won the Best of the best in the Design Concept of the German Red Dot Award in 2020 We hope to continue to be based on technology and nourished by art Support music creators to create better works Li Zihui, the founder of Aifei Matchping, has won many international awards and is a female entrepreneur with great potential「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【解決方案】2秒鐘完成結帳動作 Viscovery AI影像辨識助攻智慧零售
Complete checkout in 1 second, Viscovery AI image recognition assists smart retail

Artificial intelligence AI has gradually changed the way various industries operate in recent years However, most of the work is still done by humans, with AI playing a supporting role This has led to emergence of the term "AI Copilot," which stands for "AI-driven tools or assistants" that aim to assist users in completing various tasks and improve productivity and efficiency The concept of AI Copilot comes from the role of "co-pilot" During flight, the co-pilot assists the main pilot in completing various tasks to ensure flight safety and efficiency In fact, there have been signs of various "machines" beginning to play the role of "copilot" in different fields since the Industrial Revolution, assisting humans in completing heavy physical and repetitive tasks, greatly improving factory production efficiency, and driving rapid economic development Following the advancement of computing equipment and breakthroughs in machine learning, deep learning, and image recognition technologies, the concept of AI Copilot has gradually taken shape The development of AI Copilot marks the transition from "machine-assisted to AI-assisted" Early robots could only complete preset repetitive tasks, but today's AI copilot can learn and adapt to new environments and tasks, and continuously optimize its performance in practical applications This transformation not only changes human-machine interactions, but also has a profound impact on various industries The application scope of AI copilot covers various industries, including finance, healthcare, manufacturing, education, retail, etc, and are everywhere to be seen Application of AI copilot in the retail industry AI image recognition checkout In the retail industry, the application of AI copilot has begun to show concrete results Take Viscovery's AI image recognition checkout system as an example This system is a type of AI copilot model that helps store clerks speed up checkout or assists consumers in simplifying the self-service checkout process The store clerk needs to scan the product barcodes one by one in the regular checkout method If a product does not have a barcode, such as bread and meals, the clerk needs to first visually confirm the items, and then input them into the POS checkout system one by one Based on actual measurements at a chain bakery, it takes 22 seconds for an experienced clerk from "visual recognition" to "entering product information of a plate of 6 items into the checkout system" New clerks may need even more time In addition, according to a Japanese bakery operator, it takes 1 to 2 months to train employees to become familiar with products Now with AI image recognition technology, store clerks let AI handle the "product recognition" step, and AI will play the role of copilot, quickly identifying items within 1 second, speeding up checkout to save 50 of checkout time, and optimizing customers'shopping experience The time cost of training employees to identify bread can also be effectively shortened Even for products with barcodes, AI can quickly identify multiple items in one second, which is more efficient than scanning barcodes one by one The self-checkout system "assisted" by AI image recognition allows consumers to successfully complete shopping without the help of store clerks, eliminating the trouble of swiping barcodes or searching for items on the screen, which improves the shopping experience In a time when store clerks are hard to hire due to labor shortage, this also helps stores reduce operating costs AI quickly identifies multiple checkout items in just one second Source of image Viscovery Recently, startups dedicated to developing AI image recognition checkout solutions have emerged in various countries The most lightweight solution currently known is in Taiwan It can be immediately used by installing a Viscovery lens and a tablet installed with Viscovery AI image recognition software at the checkout counter to connect to the store's existing POS checkout system There are various integration methods, including plug-and-play and API solutions integrated with the store's POS system Viscovery AI image recognition system can be painlessly integrated with the store's existing POS system Source of image Viscovery Example of AI image recognition checkout Currently, the Viscovery AI image recognition system is being used in bakery chains in Taiwan, Chinese noodle shops in Singapore, micromarkets in department stores in Sendai, Japan, and Japanese bakeries and cake shops Over 7 million transactions were completed through this AI system, which identified more than 40 million items These use cases demonstrate the extensive application of the Viscovery AI image recognition system in the retail industry In the future, the company will continue to explore the various possibilities of using Vision AI in retail and catering nbsp The Viscovery AI image recognition system is already being used in bakeries, cake shops, restaurants, and convenience stores in Japan, Singapore, and Taiwan Source of image Viscovery