【2022 Solutions】 Defect identification rate reaches 100%, Nairi Technology is favored by major panel manufacturers
On the machine tool production line, there are some slight differences in the first step of assembly. Accumulated tolerances will cause the assembly work to be repeated, which is time-consuming and labor-intensive, resulting in shipment delays that will impact the company's reputation. Narili Technology Company focuses on the field of smart manufacturing and provides various AI solutions. It uses machine learning models to inherit the experience of old masters. In the CNC processing machine assembly and casting process, it uses AI to analyze production line data, accurately adjust various data, and improve Production accuracy is 25%.
This AI production line data analysis system is called "Master 4.0" by Huang Changding, chairman of Naruili Technology. It is the most evolved version of the master plus artificial intelligence. It has been used in machine tool processing factories with remarkable results. . In addition, Nairi Technology used AI defect detection technology to participate in the 2021 AI+ Rookie Selection Competition of the Industrial Bureau of the Ministry of Economic Affairs, assisting AUO in advanced panel image defect detection, with an accuracy rate of 100%, and won the award.
Assisted panel manufacturer AUO to solve problems with 100% accuracy in defect detection
Huang Changding further explained that during the production of general panels, edges and corners are There may be defects in the corners. Although the defects are visible to the naked eye, AOI is often difficult to identify, causing the detection error rate to often exceed 30%. Therefore, re-inspection must be carried out with manpower to improve the accuracy rate. However, in response to the demand for a small number of diverse products and insufficient manpower, using AI detection is indeed a good method.
Nairui Technology, founded in 2018, has been able to win the favor of major panel manufacturers with its AI technology in just three years. In fact, it has been honed in the field of CNC machine tools for a long time. Tang Guowei, general manager of Narili Technology, pointed out that the top three CNC machine tool factories in Taiwan hope to introduce AI into the two production lines of assembly and casting. Among them, on the assembly line, in order to maintain the accuracy of assembly, every part of the component is designed. Tolerances are designed. During assembly, each component is within the tolerance. However, the cumulative tolerance still fails the final quality inspection and must be dismantled and reassembled. This is not only time-consuming and labor-intensive, but also causes waste.
"After entering the production line, I realized that some masters have accumulated a lot of experience and are good at adjustment. After his adjustment, the accuracy rate has improved a lot and the speed is faster." On the contrary, the new engineers did not Based on experience, it takes a long time to adjust and may not pass the quality inspection.
The yield rate of Master 4.0 system has increased significantly from 70% to 95%
Tang Guowei then said that the original size data set by Master during assembly All were recorded on paper. After the information was written, it was stored in the warehouse and sealed. No one studied the relationship between the dimensions. Narili assists customers in designing the Fu 4.0 system. Through the human-machine panel, the master can directly input the measured dimensions and related data during assembly. After collecting data from different masters, AI algorithms are used to analyze the relationship between the data and create an AI model. The AI model automatically notifies the operator what size to adjust to, and the quality inspection will definitely pass. In this way, the yield rate will be improved. It has increased significantly from 70% to more than 95%.
▲Narili Technology Company focuses on the field of smart manufacturing and provides various AI solutions
Tang Guowei added, assembling the spindle of a CNC processing machine It took four hours. In the first step, the machine made measurement errors, including vibration, temperature, speed, etc. that were out of range. It had to be dismantled and reinstalled, which took another four hours. How to adjust after disassembly depends on the experience of the master. At first, the master may have done the best assembly method based on experience, but the error rate was also 30%, and the assembly took several days. With the assistance of AI masters, the assembly time only takes half a day, and the yield rate reaches over 95%, saving a lot of time and manpower.
"Use the AI model of machine learning to collect the experience of all the masters and provide it for AI learning. The first step is digitalization, and the second step is knowledgeization. This is the transformation of the enterprise. "An important key", Huang Changding believes that Narili Technology is an important partner in the transformation of traditional manufacturing from automated production to digital transformation.
In addition, another industry that Naili Technology focuses on is the smart car dispatching system of the leading brand of elevator manufacturers. The so-called car dispatch (referring to the elevator car) means that if there are more than two elevators, group management is required. In the past, car dispatching was based on fixed rules. If the elevator was closer to the requested car, that elevator would be automatically dispatched. On the one hand, it did not take into account that dispatching a car if the elevator was called too many times might make other people wait longer; The previous vehicle dispatching model did not take into account the usage characteristics of the building, resulting in a lot of waste. For example, in an office building, there are peak hours in the morning, lunch break, and afternoon after work. AI smart car dispatch can be flexibly adjusted according to off-peak and peak hours, increasing the efficiency of car dispatch, reducing waiting time, and reducing wasted electricity. .
Introducing elevator smart dispatch to improve transportation efficiency and have environmental protection functions
Huang Changding added that just like the previous traffic lights at intersections, the system has already The number of seconds to stop and pass on highways, sub-trunks and small streets is programmed. Smart traffic lights are now used to flexibly adjust waiting times to make road sections prone to congestion smoother. Using AI to learn usage scenarios and introducing a smart dispatch system into elevators will improve transportation efficiency and make it more environmentally friendly. In addition to introducing smart elevator dispatching, Nairili also introduced AI into the smart production and shipment scheduling system of elevator factories. Elevator factories often cannot accurately estimate the customer's elevator delivery date. For example, office buildings or stores must be completed to a certain extent before the elevator can be installed on the construction site. If affected by unexpected factors such as delays in the customer's construction period, the elevator factory will often be idle or the schedule will be difficult to arrange.
Tang Guowei pointed out that generally those who understand the progress of client projects may be from business or engineering, but overall, the accuracy rate of shipments is only about 60%, which means that 40% of them will not be shipped as scheduled. . Therefore, if the shipping schedule can be accurately estimated, the production line can be freed up for emergency orders or other product production needs. The AI smart scheduling system will analyze past shipment data, about 20-30 parameters such as climate, distance between the factory and the construction site, and customer credit, and put them into the AI algorithm to accurately predict whether shipments can be made as scheduled. goods.
Huang Changding also specifically stated that the machine learning of Naili Technology is not ordinary machine learning, but also incorporates various calculation methods such as traditional image processing technology and statistics. Only by being very familiar with the domain knowledge can we make good products. AI models are also where the company’s competitiveness lies. He emphasized that the data that general SaaS platforms can process is very limited, and the accuracy rate has increased from 70% to 75% at most. Naili’s strength lies in AI algorithms and machine learning, and it must be coupled with in-depth industry knowledge to produce output. Good AI model.
Narili Technology started with the AI project, gradually deepened the technology, chose to start with the more difficult tasks, and accumulated rules of thumb. It is expected to develop SaaS services this year (2022), based on customer needs. starting point, gradually gaining a foothold and becoming an important partner in smart manufacturing.
▲The picture (left) shows the general manager of Naruili Technology. Tang Guowei and Chairman Huang Changding (right)
「Translated content is generated by ChatGPT and is for reference only. Translation date：2024-05-19」
