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【2021 Solutions】 Industry-university cooperation shows results. National Taiwan University of Science and Technology’s Artificial Intelligence Operations Management Research Center uses AI to assist enterprises in digital transformation.

In intelligent systems, AI plays a key role. In addition to cultivating professional AI talents, the Artificial Intelligence Operations Management Research Center of the National Taiwan University of Science and Technology also actively conducts various project studies with enterprises to accelerate the implementation of industrial intelligence in Taiwan. One of the cases uses artificial intelligence and machine learning methods to use quality information for maintenance prediction and planning, which greatly improves equipment reliability and product quality. Using quality defect detection AOI technology can reduce the rate of missed defects.

Yu Wenhuang, director of the Artificial Intelligence Operations Management Research Center of the National Taiwan University of Science and Technology, observed that industry demand for AI is increasingly strong, and electronic manufacturing, finance, medical and other fields have greater development potential. On the one hand, the above-mentioned industries have a high degree of informatization, and automation products Online technology and the digital environment are mature and have the conditions for the development of AI technology; on the other hand, because the data required by the industrial environment has been retained, managed and used, it is easier to promote the application and solutions of AI technology when the concepts and data are available plan.

Quality defect detection AOI technology effectively reduces the wrong kill rate

For example, in the field of smart manufacturing, the team of the National Taiwan University of Science and Technology's Artificial Intelligence Operations Management Research Center assisted Taiwan's major electronics manufacturers in constructing a production line equipment diagnostic system and building a sensing network architecture in the production line equipment at the manufacturing site to detect Measure and record the operating status of the machine. Through big data analysis, a warning can be issued when an abnormality occurs on the machine to remind the manager to schedule maintenance. We use the AOI quality defect detection process, combined with machine vision and deep learning technology, to detect defects in electronic parts and perform real-time control and monitoring to assist companies in developing automated optical inspection stations, surface defect algorithms, and management application functional services.

In the flexible printed circuit board (FPC) industry, quality defect detection technology is used for image identification, mainly for re-inspection after the initial inspection, and the original inspection results are designed to be re-inspected. When doing defect detection, ordinary factories often believe that "they would rather kill a hundred by mistake than let one go" and adopt the most stringent testing standards. With the current testing technology and process, it may cause excessive detection and waste the cost of good products. .

National Taiwan University of Science and Technology Artificial Intelligence Operations Management Research Center focuses on intelligence Manufacturing Solutions

▲The Artificial Intelligence Operations Management Research Center of National Taiwan University of Science and Technology focuses on smart manufacturing solutions.

Lecturer Professor Cao Yuzhong, director of the National Taiwan University of Science and Technology Artificial Intelligence Operations Management Research Center, said that the current flaw detection, AI model and algorithm construction and training of the National Taiwan University of Science and Technology Artificial Intelligence Operations Management Research Center have achieved preliminary results. The center hopes to use images to The results of the identification can help companies quickly identify defects and quality status of products during the production process. After that, the next stage can start from the source, how to optimize parameters, improve behavior in the production process, and assist the factory to optimize the process. Better. During the product production process, the parameters of the machine equipment can be used to analyze machine data abnormalities and summarize different patterns for future maintenance and quality management to provide reference for enterprises in the application field.

The epidemic is the biggest catalyst for digital transformation for enterprises. Director Cao pointed out that the introduction of AI to promote digital transformation of enterprises is not necessarily just based on reducing costs or improving production efficiency, but must be based on the fundamental development goals and the essence of the problem. Process analysis, thinking about how to use AI or ICT technology to serve and meet process and customer needs. This process often requires breaking out of the existing framework to help companies reshape new operations and management models to effectively improve corporate performance.

Chair Professor Cao Yuzhong, Director of the Artificial Intelligence Operations Management Research Center of National Taiwan University of Science and Technology
▲Chair Professor Cao Yuzhong, Director of the Artificial Intelligence Operations Management Research Center of National Taiwan University of Science and Technology

The biggest challenge for enterprises introducing AI: improving customer trust

In the process of expanding AI industry-university cooperation, Yu Wenhuang believes that the biggest challenge is to enhance the trust of enterprises in you. For customers, a certain degree of trust is required before the Know-How of the production line can be communicated to you. Share and tell you where the focus of business is. In the absence of business trust, it is difficult for AI industry players to analyze the availability of processes and data. Enterprises usually consider two key points when choosing AI cooperation partners:

1. When cooperating with you, will the data and results be sold to others?

2. Will the cost of customization be too high? Although companies are less wary of academia, Director Yu still believes that gaining customer trust and jointly establishing sustainable AI innovation application capabilities and development goals are the key to The key factors for all ICT companies to face industrial customers and their ability to provide AI solutions.

Regarding the cultivation of AI talents, Yu Wenhuang also has his own unique insights. He observed that the education system from junior high schools, high schools to universities has driven the trend of AI. However, AI technology itself has many theoretical foundations and industrial knowledge that must be integrated. When talking about AI talent cultivation, we should first define how to construct a talent development system or route in the AI ​​field, what types of people are needed to introduce AI into the economic system, systematize talent positioning and characteristics, and let talents who are interested in investing in the AI ​​industry understand how to use their own The goal measures the types of AI skills and jobs that can be developed.

Secondly, it is to help companies that want to promote AI in a systematic way to understand, whether it is developing applications or building technical teams, how to measure the talent needs and technical blueprint corresponding to business goals. It not only plays the role of problem-solving, because AI It is just one of the ways to solve the problem. Only by assisting enterprises to establish innovative consciousness with AI R&D thinking can we truly implement industrial development, strengthen demand and promote both supply and demand at the same time, and accelerate the implementation of AI applications and talent cultivation.

NTUST Artificial Intelligence Operations Management Center provides a number of smart manufacturing solutions

Regarding smart manufacturing solutions, the solutions provided by the Artificial Intelligence Operations Management Center of National Taiwan University of Science and Technology are as follows:

. Intelligent predictive maintenance

Adopting artificial intelligence and machine learning methods, using quality information for maintenance prediction and planning, greatly improving equipment reliability and product quality, establishing failure modes and reliability analysis based on different equipment operating characteristics, and using process control analysis to trace products Quality history helps on-site personnel eliminate operational abnormalities in a timely manner.

. Smart dispatch and scheduling planning

According to the characteristics of the industry, develop intelligent labor dispatch and scheduling algorithms to effectively shorten setup time and total working hours. For example, for a variety of workpieces, the production schedule must meet conditions such as combined material preparation, group production, and specific process sequences. From the group production of workpieces, the assignment of adapted production lines, to the multi-parallel single-machine scheduling that adjusts the production sequence of each production line under grouping, the optimization algorithm is introduced to design a complete smart schedule. system.

. Deep learning and automatic optical inspection

Improve quality defect detection AOI technology, using machine vision and deep learning, which can detect flat and curved surfaces of metal electronic parts, and perform real-time control and monitoring, including automated optical inspection stations, metal AOI defect algorithms, and modular design and other application technologies.

The design elements of this algorithm: 1. Automated optical inspection station 2. Metal AOI defect algorithm 3. Modular design

. Smart Situation Room

Combined with high-end graphics card flexible assembly units, including processing machines, industrial robot arms, collaborative robot arms, engineering inspection stations and conveyor belts, a smart war room with digital twin technology is established. The technical features include real-time monitoring, data integration, data Transparency and data visibility.

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

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【解決方案】小柿智檢 以「AOIAI」雙劍合璧,軟加硬體千錘百鍊 打通外觀瑕疵檢測任督二脈
Xiaoshi Intelligent Inspection uses the two swords of "AOI + AI" to combine software and hardware to open up the two channels of appearance defect detection and supervision.

Quality inspection, like a double-edged sword, has always been a favorite and painful subject for Taiwanese manufacturers When AI deep learning enters the industrial visual inspection of traditional manufacturing industries, it can not only save inspection manpower investment, solve the problem of inconsistent manual visual standards, overcome the limited visual recognition and defect detection blind spots of traditional automatic optical inspection AOI, and also enable real-time traceability Causes of quality problems The overall AIAOI visual inspection solution developed by Xiaoshi Intelligent Inspection integrates software and hardware to create efficient appearance defect detection capabilities, helping electronics OEM customers create high-efficiency products with a miss detection rate of less than 1 and an overkill rate of less than 3 Check the level Xiaoshi Intelligent Inspection was established in 2020 Although it is a new venture two years ago, it did not start from scratch Founder and CEO Hong Peijun and the core team have been deeply involved in Foxconn factories for many years and participated in countless smart factory-related solutions and process improvements , has profound AI deep learning development capabilities, and accumulated rich experience in world-class AI application implementation Seeing that AI industrial inspection must be the last mile for the manufacturing industry to move towards Industry 40, Hong Peijun resolutely decided to implement AI deep learning technology in the field of smart manufacturing with high output value, and specialized in the development of AI industrial visual inspection For the manufacturing industry, product inspection is the most important part of all quality control, but traditional industrial inspection faces two major pain points 1 Manual visual inspection Today, more than 95 of the entire manufacturing industry still relies on manual visual inspection Inspection makes it difficult for manual visual quality inspection standards to be consistent, and visual inspection of fine objects, such as passive components or highly reflective components, will cause long-term vision damage 2 Traditional AOI automatic optical inspection The product has limited visual recognition capabilities and blind spots in defect detection Among them, the detection of appearance defects such as scratches, oil stains, dirt or hair and other unexpected subtle defects has always been a problem in AOI applications Insurmountable difficulties AIAOI visual inspection overall solution is a great boon for appearance defect detection When designing the product roadmap of Xiaoshi Zhikan, customer group positioning and strengthening customer product services and value were important indicators Moreover, appearance defect detection has always been an unresolved pain in the manufacturing industry, Hong Peijun said With industrial quality inspection AI software as the core, Xiaoshi Intelligent Inspection provides an overall solution for AIAOI visual inspection It mainly promotes three major products, including "QVI-T AI deep learning inspection modeling platform software" and "AI six-sided defect inspection and screening machine" ” and “AI Industrial Quality Inspection Platform” The main customer groups served are semiconductor packaging and testing, EMS electronics foundry, small metal parts processing and other industries with high production capacity and high gross profit margin In response to customer needs, Xiaoshi Intelligent Inspection provides corresponding software and hardware services, combining self-developed AI deep learning software and hardware quality inspection equipment to reduce the manual visual burden on the production line and effectively improve the production quality of the factory In order to help equipment manufacturers and technical engineers with development capabilities accurately grasp product appearance defect detection, Xiaoshi Intelligent Inspection independently developed QVI-T deep learning detection software, which can provide customers with defect location, defect classification, defect segmentation, anomaly detection and text recognition Key functions such as this are different from the fixed detection methods of traditional software Algorithms can be refined based on different industrial detection methods and different APIs can be developed to connect devices with different lenses The software design of this platform is very lightweight It is a SaaS software built on public cloudprivate cloud It mainly involves simple image uploading, labeling, training modeling, and verification testing After completion, users can download models, SDKs, APIs, and reports Effectively help customers achieve AI inference functions Currently, most of the industrial inspection services on the market are traditional AOI software industrial inspection machines, which can only measure product contours such as the head and length of fasteners, etc, and cannot truly provide detection of subtle product surface defects such as screw head cracks and tooth damage There is a lack of such high-precision defect detection companies in the market, Hong Peijun observed Xiaoshi Intelligent Inspection developed and independently built the "AI six-sided defect detection and screening machine" from customized services in the past to providing standardized services for customers at the current stage It provides standardized testing services for fasteners in measurement and surface defects, as well as passive components High-speed surface defect detection of similar products This professional machine uses the AI deep learning AOI composite algorithm technology independently developed by Xiaoshi Intelligent Inspection Through parallel computing technology, it can achieve model inference up to 3 milliseconds per picture, and realize multiple complex defect detection on the electrodes and body of passive components This professional machine is mainly used for the inspection of fasteners, small metal parts and passive components In terms of competitiveness in the industry, the software hardware integration provided by the AI six-sided defect inspection and screening professional machine is an important core competitive advantage of Xiaoshi Intelligent Inspection It is not as simple as it sounds Hong Peijun said with emotion that this special machine is very important in the industrial inspection industry Commonly known as the highly integrated integration of optical mechanisms, electronic controls, software and algorithms, the process requires continuous optimization and iteration, and requires multiple client verifications and modifications After a long period of hard work, the technical threshold has also been raised The AI six-sided defect detection and screening professional machine will be the main product promotion direction of Xiaoshi Intelligent Inspection in the next 3-5 years It is believed that AI combined with measurement technology and surface defect detection will be an important source of core competitiveness of Xiaoshi Intelligent Inspection, Hong Peijun said AI six-sided defect detection and screening professional machine will be the main product promotion direction of Xiaoshi Intelligent Inspection in the next 3-5 years Faced with the booming development of Industry 40 in smart factories, customers often ask "Does quality inspection data have secondary use value" Hong Peijun said that the "AI Industrial Quality Inspection Platform" launched by Xiaoshi Intelligent Inspection has a machine learning mechanism , which can be used for secondary use of quality inspection data to provide customers with multiple functions including real-time monitoring and early warning of production quality, quality traceability analysis, quality factor assessment, process parameter prediction and recommendation Taking the successful introduction into the automotive parts factory as an example, through the prediction and recommendation of process parameters provided by the AI industrial quality inspection platform, when we know the product defects, we build a set of models based on the experience of past masters, coupled with the network connection data from the previous stage, After integration, we have process data, incoming material data, and quality inspection data We can predict whether these machine parameters have run out, and we can recommend whether the process parameters of certain sections should be adjusted up or down Through the AI industrial quality inspection platform, Xiaoshi Intelligent Inspection can help customers connect visual quality inspection results, process data and acceptance standards with the existing MES system of the customer's factory to improve production quality, improve efficiency and reduce costs In terms of business model, Xiaoshi Zhiqian also provides a software subscription system for the deep learning detection modeling platform software It provides public cloud customers with traffic subscription and charges based on the amount of image uploads, while private cloud customers adopt an annual license fee license charging mechanism In addition, the company also provides customers with a buyout charging mechanism for the overall solution equipment, and provides a one-year warranty, after which consumables and software update maintenance fees are charged annually Going in the opposite direction, using both hard and soft methods, with a missed detection rate of less than 1 and rapid modeling in 15 minutes Faced with various small-volume and multi-sample inspection needs in the manufacturing industry, general AI deep learning visual inspection usually requires customers to collect a large number of photos of defective products, which is time-consuming to label, and also causes customers to have difficulty in importing AI, and defective products cannot be collected The introduction cycle is long and implementation is full of risks If there are not enough bad samples, the model will be inaccurate Kosaki Chikan goes in the opposite direction and uses its product "AI Visual Inspection Model Development Tool" to train models through pictures of good products provided by customers It is relatively easy for AI to learn good products, no labeling is required, and the time can be quickly compressed to complete the modeling Take the implementation of IPC electronics industry - AAEON Technology as an example In order to reduce the manpower input of the quality inspection station in the PCBA production line and have standardized quality inspection, Xiaoshi Intelligent Inspection provides an overall solution for PCBA AI visual inspection software and hardware services, and conduct in-line inspection on the factory's highly automated assembly line, effectively saving inspection manpower investment, improving the standardization of quality inspection rates, and improving the problem of inconsistent standards caused by manual visual inspection Through the introduction of AI visual inspection software and hardware integrated solutions, we have effectively helped customers maintain an overkill rate of less than 3 in the past two years, and achieved high-efficiency performance with a missed detection rate of less than 1 In addition, this solution allows practitioners who do not understand AI to quickly operate modeling By installing the modeling tool on the device, when the customer has a new product number and needs to create a model, he only needs to provide 10 pictures of good products to scan under the device It only takes 15 minutes to quickly train the model In terms of product core strategic layout, compared with market competitors who rely solely on general software services to seize all manufacturing markets, it is not feasible to apply it to industrial inspection Hong Peijun has observed over the past 10 years and believes that only software hardware can With technical thresholds and focusing on one industry and field, only by adopting a standardized company's AI six-sided defect detection and screening special machine can it be replicated and scaled up, and the company can truly continue to move towards optimization and create product competitiveness, even if there are other competing products It’s not easy to compete for this pie, Hong Peijun said Xiaoshi Intelligent Inspection’s overall AIAOI visual inspection solution creates rapid modeling and excellent results for customers with a missed detection rate of less than 1 The most competitive AIAOI overall solution provider with global presence For new entrepreneurs, facing business expansion is a challenge every day Hong Peijun said that small companies are easily snatched away by large companies, company talents are poached by high salaries, lack of deep customer relationships, and the business team is not large enough, etc How to overcome this Hong Peijun believes that the key to success and competitiveness of a new start-up company is to be diligent in making up for mistakes, provide better services, provide more immediate feedback, and create more professional solutions to convince customers Since its establishment in 2020, Xiaoshi Intelligent Inspection has always gone against the grain in terms of product core strategic layout, surpassing the competitive market among its peers, and actively taking root in the overall solution of AI visual inspection software and hardware Hong Peijun hopes that Xiaoshi Intelligent Inspection will become the world's most competitive AIAOI overall solution provider for the electronics and semiconductor industries in the future, and provide the top AIAOI professional machines and equipment to the electronics and semiconductor industry customer base Hong Peijun said that the technical capabilities of the company's AI six-sided defect detection and screening professional machine have reached the top domestic level In order to speed up the research and development of professional machines to become more standardized and sell them to overseas markets, the company will conduct a fundraising plan at this stage, hoping to use legal persons such as the Capital Strategy Council to assist in more business connections and fundraising channels For the medium and long-term goals, Xiaoshi Intelligent Inspection will lay out the global market including mainland China and Southeast Asian countries At the same time, it will follow the international footsteps of major OEMs in global layout Under the target inspection project, it will continue to develop specialty products and spread towards the international field 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【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

Compared to Japan, where 90 of golf courses operate without caddies and use an automated service model, golf course management in Taiwan still heavily relies on human labor Facing a labor shortage of up to 70, adopting a site and membership management platform to provide intelligent golf services may be a transformation worth considering for golf course operators 'Taiwan's software technology is comparable to international standards and definitely has the capability to compete in the global market,' says Tsung-Che Liao, co-founder and CEO of Golface, established in 2014 with the vision to leverage technology at its core, aiming to create Taiwan's first golf entertainment platform With over 9 years vested in cultivating intelligent golf services, Liao is well-versed in the nuances of golf course services He has considerable domain knowledge and has launched a comprehensive intelligent golf solution The world's first networked smart golf cart hits the road automation of golf courses is no longer just a dream In mid-May, Golface's newly developed ARES Smart Golf Cat, the world's first networked smart golf cart, officially became operational Equipped with a dedicated vehicle computer mainframe, dual network systems, AI-based visual recognition cameras, and high-precision GPS tracking, golf courses can now confidently allow golfers to drive themselves The system enables real-time monitoring of any driving violations, and the presence of digital consumption traces allows for insurance coverage The procedure is as follows golfers book the cart via a reservation platform, receive a QR code, pay through the platform, and unlock the cart with the QR code at the golf course The golf cart can then be driven onto the course The course management platform can monitor and restrict the areas through which the cart can travel, ensuring it does not leave the paths Upon completion, the cart is returned through a tablet in the cart In instances of any infractions, penalties are applied directly through the user's account, and for severe violations, future access to the carts may be prohibited This achieves the goal of 'automation' ARES Smart Golf Cat is the world's first networked smart golf cart, officially in service since May 2022 'As labor costs continue to rise, recruiting and training caddies are becoming common pain points in the market While Taiwan's courses still employ caddies, there's a 70 labor shortage,' Tsung-Che Liao added This smart golf cart tablet, combined with a mobile app, has become the ultimate smart caddy Golface is striving to complete the last piece of the 'automated golf course' puzzle Amassing digital consumption trails for advanced client segmentation services Starting with consumer needs, Golface has sequentially launched services like the golf cart tablet, mobile app, golf reservation platform, instructional videos Golface TV, golf tourism, and smart carts The smart cart has been operational since May 2022, currently featuring four units with plans for mass production in the latter half of 2022 Although the cart currently requires manual operation by golfers, remote operation is anticipated early in 2023, with autonomous driving expected in the third phase Via the cart tablet and management system, staff can understand the status of the course through on-screen visual representations, showing each cart's real-time and relative location, departure times, and duration of service per hole, which aids course managers in monitoring on-course consumption effectively, thus reducing traffic jams and customer complaints 'Previously, we relied on staff's mental imagery now, we can employ imagery to visualize real-time situations on the course This makes it possible for those who don't understand golf to work in this field,' emphasized Tsung-Che Liao While course control has traditionally been handled by experienced professional players, the shortage of skilled professionals makes hiring even more challenging Therefore, replacing manpower with digital tools yields twice the result with half the effort The golf cart tablet has entered the Japanese golf market, installed at Fukuoka Century Golf Club Golface's golf cart tablet has been introduced to 14 domestic courses, and has now officially entered the Japanese market, favored by Fukuoka Century Golf Club, where tablets have been installed in carts providing automatic voice announcements for hitting strategies, distance measurements, and visual charts displaying hitting data During the COVID-19 pandemic, with borders closed, Golface utilized OTA technology to provide software updates and troubleshooting, ensuring uninterrupted services, which was highly appreciated by the Japanese golf courses Tsung-Che Liao remarks that Taiwan's software technology is not inferior to other countries like Japan, but more support from golf courses is needed to help transform the industry intelligently 'To assist in the transformation of golf courses, the first step is digitalization,' Liao pointed out By helping courses accumulate data and understand customer service cycles and hitting rhythms, it enables courses to avoid congestion and serve more customers To date, Golface has accumulated data on over 20,000 teams, 35 million scorecards, and over 10 million records This data helps enhance management performance, segment customer layers, reduce complaints, and plan marketing strategies for off-peak periods Golface co-founder and CEO Tsung-Che Liao has spent 9 years deepening intelligent golf services, aiming to build Taiwan's first golf entertainment platform「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」