Chin Fong Machine Industrial is one of the top five operators in the global stamping and forging machine industry, with more than 70 years of experience. It is also the largest professional manufacturer of mechanical punches in Taiwan. It specializes in manufacturing various types of machines, providing complete stamping and forming solutions for automotive sheet metal stamping production lines, engine silicon steel sheet and computer chassis.
As an industry pioneer, Chin Fong Machine Industrial has since 2015 established a smart factory operation. Recently, the company has started integrating artificial intelligence solutions to implement lean manufacturing and develop smart equipment – and it is this requirement that has led to a successful collaboration with ASUS IoT.
The challenge: Accelerate visual inspections of complex components
Industry 4.0 is a buzzword, but it is not without substance. In manufacturing, this means combining the Internet of Things (IoT), digital factories, cloud services and communications to create an “intelligent” digital-physical system – bringing intelligence into the production process and changing the business thinking of the traditional manufacturing process.
IoT technologies allow machines to communicate with other machines and with people, transforming traditional production methods into highly personalized, intelligent and service-oriented business models. It also provides the ability to quickly manufacture a small quantity of products and respond to rapidly changing markets, to improve business competitiveness and profitability.
Traditionally, detecting defects on press machine production lines has required manual visual inspections. However, human inspectors lack efficiency and accuracy, especially for metal parts. These components are particularly difficult due to their reflectivity, which means that parts often have to be flipped or rotated multiple times. Understanding the surface characteristics of optics and components is critical to obtaining accurate defect data.
In addition, the types of metal molds vary, which further complicates human inspections. It’s for this reason that automatic optical inspection, or AOI, is increasingly being implemented in the field – and it’s why Chin Fong Machine Industrial turned to ASUS IoT.
The solution: An AI-powered visual inspection system from ASUS IoT
ASUS IoT AISVision is an easy-to-use software toolkit for AI-powered vision applications, especially suited for metal stamping and plastic injection process monitoring, as well as assembly of electronic parts. AI-powered training models mean it can be quickly adapted to almost any visual inspection requirement, accurately and efficiently detecting a wide range of defects including scratches, crushes, dirt and more. It is even capable of distinguishing hidden defects in concentric circles and capillary metal parts.
With these advanced capabilities, AISVision optimizes production processes and delivers superior anomaly detection performance, dramatically reducing model training timeframes from hours to minutes and meeting modeling and implementation requirements. fast at the factory. Additionally, AISVision supports the Intel OpenVino framework for inference without additional GPU accelerators, reducing hardware investment.
AISVision offers rapid no-code modeling, with a training architecture that includes supervised and unsupervised learning modes. Thus, only a small number of samples are needed to create an accurate model.
ASUS IoT has developed an exclusive AI-powered visual inspection technology that leverages four functions. These include multi-object classification, accurate flaw detection, rapid object identification, and rapid modeling anomaly detection, and provide high-speed, high-accuracy detection capabilities. Various data filtering functions can be performed to check the training status for different scenarios, and the model check report is also organized and exported in HTML format for training other models. The unique recycling mechanism ensures data confidentiality and powerful API support is built-in, including C, C++ and C#, which makes program integration faster and easier.
The result: Significantly improved speed, efficiency and accuracy on the production line
Chin Fong Machine Industrial has implemented several ASUS IoT AISVision solutions, installing the necessary cameras and lighting on its production lines. Once part stamping is complete, image capture and inspection is performed immediately, with defective objects being identified with new levels of speed and accuracy. The detection accuracy has eliminated both the human fallibility and the fatigue problem.
Compared to traditional AI-based projects, AISVision saves up to 80% of project development time, contributing to the digital transformation of the smart factory of the future.
Partly through its cooperation with ASUS IoT, Chin Fong Machine has evolved from an equipment manufacturer to a system integrator (SI), providing a one-stop inspection service to help enterprise customers increase both their benefits and competitive advantages. We continue to work together to use digital visualization technologies to create a next generation ecosystem for the metal processing industry.
“In recent years, we have focused on stamping industry applications, and we have implemented IoT and AI-based technologies as a framework to integrate stamping and forging operations. and management issues. We look forward to further working with ASUS IoT to create high value for our customers through complementary services and application software,” said Sheng-Ming Tseng, General Manager of Chin Fong Machine Industrial.
The ASUS IoT team believes that AI-based machine vision and edge computing are the core of smart manufacturing. We provide customers with process optimization, cost reduction, and process quality and efficiency improvement, so companies can increase revenue and create new business models. With powerful AI technology, we provide our customers with intelligent solutions, accelerate the processing of huge data, strengthen the supply chain and set a new benchmark for AI-based manufacturing.
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