Metal Products Industry with Smart Manufacturing
With the increasing use of IoT (Internet of Things), big data, cloud computing, artificial intelligence, etc., manufacturers in the metal product industry have also begun to develop toward Smart Manufacturing (Intelligent Manufacturing; IM), which in turn drives innovation in traditional manufacturing.
Smart manufacturing transformation needs of Taiwan's metal products industry
According to Market & Market's (2015) smart factory application market statistics, the automotive industry (Automotive) has the largest application market, with approximately US$20.43 billion in 2015, accounting for 40.4% of the global smart factory market. According to the report, the market size is expected to reach 26.56 billion U.S. dollars by 2020, with an average annual compound growth rate of 5.53%; Electrical & Electronics is the second largest application market, with market size of approximately 13.28 billion U.S. dollars in 2015. It accounts for 26.3% of the global smart factory market. It is estimated that the market size will reach 17.36 billion U.S. dollars by 2020, with an average annual compound growth rate of 5.66%; the metal/mining industry (Metals & Mining) is the third-largest application market for smart factories. The market size in 2015 was approximately US$5.74 billion, accounting for 11.4% of the global smart factory application market, and it is expected to grow to US$7.25 billion in 2020.
Application case analysis of smart manufacturing in the metal products industry
Although the global metal product industry’s investment in smart manufacturing is not as active as the automotive or electronics industry, many metal product companies are actively introducing smart manufacturing systems, using virtual and real integration or smart networking technologies to add Design, supply chain management, equipment, manufacturing process, simulation or service mode, etc. introduce integrated intelligent solutions to provide rapid customized development, immediate response to production lines and predictable production assistance, and establish the benefits of rapid response to diverse and multi variable markets in the industry, Even real-time quality monitoring, and feedback correction technology in the process.
Smart manufacturing practices on the equipment side-aluminum rim industry and fastener industry
Import smart manufacturing production line
The general fastener production process includes 9 processes such as disk element spheroidization, forming, thread rolling, heat treatment, surface treatment, and packaging and shipment. Each process requires strict quality control to ensure quality, especially in some production links. Relying on manual work makes it difficult to achieve the 0 ppm requirement for product defects. First, clarify the intelligent requirements of each stage from product idea, mold design, plate material selection, mold manufacturing, forming simulation, fastener trial mass production to formal mass production, and finally decide to prioritize mold manufacturing and product production process quality Monitor and import smart manufacturing-related technologies.
In terms of quality monitoring in the production process, since fastener forming is the most critical link that affects product quality, the traditional manual inspection cycle in the past was 1 to 2 hours. During this period, nearly 1,000 good products may be mixed with defective materials. Form the demand for the introduction of smart detection. Through the "real-time sensing technology of forming process", intelligent detection of material forming and force changes, tightly monitoring the forming and forging pressure curve of fasteners, to achieve abnormal warning and information visualization of the quality of fasteners finished products, which is beneficial to the equipment All fasteners are inspected, which can completely prevent defective products from being mixed into the production line and improve the yield of finished products. To further realize smart pre-planning and analysis, the production line sensing information is uploaded to the cloud management system, coupled with the "quality prediction and virtual measurement analysis technology", to grasp the machine status early and predict that the product is about to be defective At the time point, in addition to achieving the purpose of early response and warning, it can also eliminate the possibility of a temporary shutdown before the occurrence of defective products, effectively arrange the problem diagnosis schedule, and avoid the interruption of equipment, molds, product materials, and supply chain caused by unwarned shutdowns loss.
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