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【2020 Application Example】 "AI Color Recognition and Cost Optimization Control System" automatically recognizes colors, breaks through the traditional color grading model, significantly reduces costs, and improves yield!

Mixing new colors relies on the experience of master craftsmen!

The so-called "computer color matching" in the paint industry is simply the selection of "existing colors" for mixing, but there is actually no way to mix paint for a “new color” and it all relies on the experience of master craftsmen. Hence, it is necessary to start from scratch when a new color is encountered, which consumes a lot of manpower and time. Moreover, due to the different color mixing habits of each master craftsman, the cost can be significantly different despite producing the same result!

The trilogy when paint factories face the crises of transformation

I. Lack of color mixing standards

Generally, when traditional paint factories produce new colors, they will use a "spectrophotometer" to measure the LAB value of the sample color, and then the paint mixer will mix the paint of that color based on past experience. After color mixing is completed, the instrument will be used to test the LAB value and C and H wavelength. This process does not have a complete system and database records, and there are not standards for color mixing.

II. Production costs are difficult to control

Paint factories produce many pigments with different materials and functions, and the cost of paint will vary depending on the "color masterbatch material" used. Even if the color number of the masterpiece is the same, the cost will be different if the ratio of the color masterbatch is different. Paint mixers do not have a set of color mixing standards when mixing paint, making it difficult to control production costs.

III. The color grading process is lengthy and personnel training is difficult

As instruments cannot replace manual color mixing, the training of a paint mixer requires years of experience in paint mixing, familiarity with chromatology, as well as basic understanding of hue, saturation, and brightness. If there is no basic reference color values when mixing paint, the paint mixer must spend a lot of time repeatedly mixing colors, resulting in a loss from time cost.

Developing an "AI Color Recognition and Cost Optimization Control System"

The paint factory engaged in industry-academia collaboration with the Department of Computer Science & Information Engineering of Chaoyang University of Technology through CDIT Information Co. Ltd., and utilized the university's AI research capabilities to jointly develop the "AI Color Identification and Cost Optimization Control System." It established a database of "paint color numbers" and "color masterbatch material cost," and analyzes the optimal color mixing and optimal cost formula through data mining methods. The paint mixer can refer to the formula analyzed by the system for color mixing, and then input the formula into the system after paint mixing is completed. The formula is fed back to the basic database and an "artificial neural network model" is used by the system for deep learning, establishing a color grading standardization system for cost control and data collection, so as to solve the current difficulties faced by paint factories.

In the early stages of system development, CDIT planned the system requirements of the paint factory, established the system architecture and system database, and then worked with Chaoyang University of Technology on the implementation of model functions for the application of data mining and artificial neural network.

After the system is completed, CDIT will assist the paint factory in system testing and correction. The system will be introduced after correction and testing are completed, and training on system use will be provided to ensure the correct use of the system.

System Interface Diagram

▲System Screen

Differences before and after system implementation

▲Differences before and after using the system

Expand new markets for the paint industry to see the paint industry thrive!

The "AI Color Recognition and Cost Optimization Control System" collects the color mixing formulas of paint mixers, establishes a paint color masterbatch formula database, and records the cost of each color number. The system's deep learning function is then used with a spectrophotometer to analyze the optimal color mixing formula for each data entry, so that the paint factory can control the cost of paint mixing. The optimal color mixing formula recommended by the system increases the speed of paint mixing and increases output value.

Future benefits include: The improvement in product yield reduces customer complaints and improves customer satisfaction. The breakthrough in the traditional color mixing model improves corporate image. Improves the efficiency of paint mixing, and allows the remaining time to be invested in training to enhance the professional capabilities of personnel. It will also allow the joint expansion of new markets with the paint industry and learning of new application technologies, and promote them to other paint companies, enhancing the industry's overall competitiveness to see the paint industry thrive!

Recommend Cases

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[2023 Case Study] AI Steps into Philanthropy: Stylish Tech at Food Banks

Taiwan Food Bank AssociationHereinafter referred to as 'the Association'With the mission of providing food aid, poverty relief, reducing food waste, and building a hunger-free network, there are locations across Taiwan that gather donations from wholesalers, intermediaries, retailers, manufacturers, and even generous individuals These sites also rescue food that would otherwise be discarded, properly allocate and distribute it to needy households, thus aiding local vulnerable families55Food banks at various locations collect daily donations from wholesale stores, intermediaries, retailers, manufacturers, and even benevolent individuals from all over Taiwan These places also rescue about-to-be-discarded edible materials, properly sort them, and distribute to needy households, assisting local vulnerable populations However, each location requires significant human and volunteer resources to manage daily operations using traditional methods of communication with non-profit organizations and donors After receiving donations, these resources are then allocated to needy families or individuals There is a potential issue of uneven distribution of resources due to a lack of digitalization and integrated information management in these processes Warehouse and Transportation Centers and Mini Food Banks Distributing Resources to the Disadvantaged The location under validation by the Kaohsiung Charitable Organizations Association,Hereinafter referred to as 'Kaohsiung Charity' In109year6month24Officially inaugurated Taiwan's first 'Food Bank-Warehouse and Transportation Center' at a location measuring200square meters, enhancing the efficiency of food resource redistribution, proper storage, and management So far, nearly two hundred tons of vegetables and fruits have been saved, serving over a hundred organizations and benefiting over5thousand vulnerable households, and continues to serve19mini food banks, with planned completion across multiple districts in Kaohsiung, distributing food resources to over10ten thousand vulnerable families Kaohsiung Charity 'Food Bank-Warehouse and Transportation Center' in the Dasha Community Photo Source Kaohsiung Charitable Organizations Association Challenges in Labor and Food Resource Management Facing the needs of a large number of economically disadvantaged families, the management of the 'Food Bank-Warehouse and Transportation Center' is particularly critical During procurement, tasks such as sorting, purging, and bookkeeping must be performed, while during shipment, food resource needs suggested by social workers must be followed These activities rely on manual judgment and accumulated experience Many volunteers involved are elderly and have limited physical strength, making warehouse tasks physically demanding and recruitment challenging If a large batch of food resources arrives, space and manpower are consumed in sorting and inventory management, raising concerns about the effective use of resources and turnover rate This highlights the challenge of scaling up food bank services while lacking corresponding labor and material management systems At the same time, food bank resources come from various donations, thus they vary greatly in type, shelf life, standards, and quantity Volunteers at mini food banks, mostly also elderly, must handle multiple responsibilities such as case services, food resource management,resource allocation, and resource development Sometimes they must also explain and accept immediate, large quantities of specific resources, such as adults receiving baby formula 'Food Bank-Warehouse and Transportation Center' Resource Inventory Relies Entirely on Manual Labor Mini Food Bank Volunteers Handle Multiple Responsibilities Photo Source Taiwan Food Bank Association Reducing Scrap Resources60 Increasing Speed of Resource Transfer80 To enhance resource management and ensure effective use of materials, and to address personnel shortages, this field validation case has introduced 'Food Bank Warehouse Resource CollectionAITo advance resource management, ensure effective use of resources, and solve manpower shortages, this validation site has implemented an 'Automated Early Warning Needs Assessment System' for the food bank's warehouse resource gathering The first part involves building a classification model, setting up and collecting warehouse information at the site, andAItraining the model Past sitewarehouse information is collected and stored in a database, allowingAIfor preprocessing, classification, and other tasks At the same time, depending on the dependency conditions of the types of goods as features, algorithms are introduced for computation and modeling, and the data collected is used for retraining, ultimately validating the field and organizing data for the five most common types of goods into training and test datasets as required The second part involves constructing the classification model using AI techniques further use of reinforcement learning constructs the management mechanism for the food bank's warehouse, perfecting the classification of donated goodsRNNTechnical construction of classification models further use of reinforcement learning constructs food bank warehouse management mechanisms, making the classification of donated goods perfectlike white rice, instant drinks, noodles, instant noodles, and canned goodscan then be automatically assigned storage based on storage assignment principles AI Service System Process and Description Source Taiwan Food Bank Association AtAIUnder forecasts, it can optimize the speed of resource transfer and allocation, effectively and accurately match resource donations reducing the loss in the donation process, increase the accuracy of resource distribution, and improve the service rate—the successful donation rate—reducing the waste of resources due to incorrect items, and enabling instant monitoring of food resource stock, ensuring operators can respond quickly to needs, effectively providing resource assistance WithAIthe system's introduction and the establishment of data intelligence, it helps the operations of the warehouse and transportation center, allowing more time for the allocation of donated goods The introduction aims to accelerate the digital service rollout for social welfare organizations, thoroughly addressing the needs of the overall vulnerable segments of society Using the system for resource allocation and dispatching Photo Source Kaohsiung Charitable Organizations Association Following this field validation, it is possible to expand the system to other food bank service pointsAIThe system can also collaborate with more non-profit organizations, public welfare groups, and charitable organizations, expanding 'Food Bank Warehouse Resource CollectionAIAutomated Early Warning Demand Assessment System' application range such as medical supply distribution, helping more organizations manage and distribute more intelligently, reducing resource wastage, and enhancing social welfare 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」

【導入案例】維繫遊艇王國美譽 嘉信遊艇導入國內第一套FRP複材超音波智慧檢測
Maintaining the reputation of the “Kingdom of Yachts” - Kha Shing Enterprise introduces the first domestic FRP ultrasonic smart inspection of composite materials

The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the company, he made a breakthrough in the previous manufacturing model that relied mainly on the skills of master craftsmen, introduced digital manufacturing to accelerate shipbuilding, and began to make larger yachts, ranking in the top 20 manufacturers worldwide among manufacturers of large yachts over 24 feet It also set a record of delivering 94 yachts within one year, earning Taiwan the reputation of "Kingdom of Yachts" Defect detection ensures yacht quality, using AI to replace humans to achieve higher efficiency Defect detection is very important to ensuring yacht quality At present, the yacht industry still uses very traditional defect detection methods The hull structure is usually made by hand lay-up or the vacuum infusion process, using visual inspection or knocking and the frequency of the sound to determine defects It requires time-consuming manual inspection If there are any defects, they must be reworked and repaired, and a gel coat subsequently sprayed The hull must be constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed Schematic diagram of types of FRP hull In order to solve this problem, Kha Shing Enterprise has engaged in technical cooperated with the metal materials industry and the AI technology industry, combining the ultrasonic inspection expertise of the metal materials industry with AI technologies developed by the AI technology industry in recent years to help solve issues of Kha Shing Enterprise with defect detection The method uses PAUT on the composite material structure of yachts, conducts FRP ultrasonic evaluation to determine the thickness of the yacht hull and material properties, and evaluates the ultrasonic probe frequency applicable to the hull structure based on professional ultrasonic experience After testing, a frequency of 5MHz and a probe width of 45mm can successfully find the location and size of defects in the simulated defect test specimen The three parties jointly found defect detection solutions from array ultrasonic evaluation, AI technology model development, and actual application in yachts The image inspected is an ultrasound image The image displays different colors based on the ultrasonic feedback signal An AI model that automatically identifies defective parts is established through the YOLO algorithm If the amount of abnormal data collected is insufficient for training, the CNN-based Autoencoder algorithm is used to collect normal image data for training and construct an AI model for abnormality detection The object detection YOLO model is trained by inputting image data marked as having defects, while the abnormality detection model is trained by inputting image data without defects Simulated defective specimen corresponding to PAUT results Defect detection by and AI system can shorten the construction period by 15 months and speed up determination by 50 After the development of this AI system is completed, it will be validated on actual 54-foot yachts of Kha Shing Enterprise, and can effectively resolve issues with defects The application of AI technology in ultrasonic inspection for intelligent determination is expected to accelerate determination by approximately 50, and will also shortens the construction period by 15 months, effectively improving the speed and quality of the yacht manufacturing process As Taiwan develops larger and more refined yachts, it will create opportunities for industry optimization and transformation, as well as opportunities for the development of key technologies The application of an AI ultrasonic inspection solution for composite materials is the first of its kind in the yacht industry, and is expected to attract more yacht manufacturers with inspection needs The AI ultrasonic inspection solution for composite materials has three major competitive advantages 1 Professional inspection experience and digital database to facilitate process management and analysis 2 Automatic AI determination and identification quickly identifies defects and provides immediate feedback to process engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market

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AI-Based PCBA Surface Defect Detection Improvements

With the introduction of theAOIAIWith the introduction of the system, we can improve product yield, reduce costs, and from a business perspective, increase customer trust and sales revenue Moreover, AIit has advantages that are difficult to imitate, unlike other equipment that can be bought with money, making it hard for our competitors to catch up with us Our company's current development We are committed toIOTsmart manufacturing our systems already include smart materials systems, environmental humidity control systems, anti-miscarriage systems, smart procurement computation systems, smart inventory systems, solder paste management systems, and production management systems We have asked other manufacturers about the possibility ofAIinspectingPCBAsurface defects, each hoping that we would purchase their equipment, but none were effective upon verification After discussing with IT service providers, we defined it asAOIAIa feasible operational model Tzuhong Technology has invested inAOIAIan inspection plan to checkSMTtext on components, solder joints, polarity, missing partsand usingAIto replace manual learningAOIand define the 'potentially defective' parts, enhancing productivity and reducing misjudgment rates Industry pain points Taiwan faces a severe labor shortage, especially those willing to perform visual inspections are few and typically older, increasing the frequency of missed inspections Thus, the most critical bottleneck in the pursuit of high-quality electronics has become post-production inspections Previous consumer products with undetected anomalies were acceptable within a certain ratio However, in the automotive industry today, undetected defects could lead to fatalities hence, the automotive industry has extremely high quality demands To survive in the automotive supply chain, we must address the issue of undetectable anomalies Moreover, as wages in Taiwan continue to rise, we can only endeavor toAIreplace traditional manpower with technology, otherwise, even if the anomaly leakage problem is resolved, the relatively high labor costs will still prevent competitiveness in this industry Application technology and explanation Initially,Figure 1,PCBUpon emerging,Reflowsystem, it will undergoAOIwill undergo inspection, dividing into 'suspected defective' and good products At this point, the 'suspected defective' portion accounts for20manual review for these20parts, further classifying the 'suspected defective' portion into good and defective products With We aim to leverageAItechnology, to shift from manual re-inspection of these20technology, we aim to replace manual review of 'suspected defective' products withAIand after review, the results still yield 'good' and 'suspected defective' products, but now 'suspected defective' comprises only3thus reducing the workload of Tzuhong's employees from20down to only3In theory, it isAOIIn theory, after inspection, it is further reviewed byAIbut it appears to go throughAOIonly, so we call this technologyA0IAIDetectionFigure 2。 The original AOI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOI information on defective products, then manually re-inspect one by one to determine if they are defective AOIAI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOIinformation on defective products after, then proceed byAIfirst performingAOIre-assessment of defective products, outputtingAIinformation on defective products afterward, then manually re-inspect one by one to determine if they are defective Process differences By introducing theAOIAIsystem, not only can we enhance the efficiency and yield of visual inspection personnel, we also have this timeAIexperience in system introduction, we will also incorporateAIthe use of big data into Tzuhong's existing smart manufacturing systems, further enhancing the performance of our smart manufacturing systems and reducing the pressure on employees Difference between pre and post-introduction Promotion strategy 1 Similar field diffusion allSMTmanufacturers face bottlenecks in inspections leading to shipment delays introducing this system can solve the severe labor shortage issue and enhance shipment speed and quality, allowing self-promotion to customers or through equipment dealers to cater to relevant needs 2 Cross-industry expansion plans negotiate withAOImanufacturers to directly integrateAIthe system intoAOItheir systems, enhancing their market competitiveness Profit strategy 1 In collaboration withAOImanufacturers, collect licensing fees 2 Direct sales toSMTthe manufacturing industryAIsystems 3 ProvideSMTmanufacturing industryAOIAIsystem subscription model「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-09」