AI: Powering the Engine of Tomorrow's Manufacturing Landscape
Shan
Wu, editor
In the journey towards digital transformation in manufacturing, AI (Artificial Intelligence) stands as an indispensable element. The utilization of AI trends to bolster Taiwan's manufacturing industry and foster international competitiveness is currently a focal point of attention.
Taiwan's industrial technological prowess has long held a significant position in global industry chains, spanning from information technology, consumer electronics, precision machinery, to the research and development of general consumer goods. It boasts abundant international competitive advantages. However, in recent years, factors such as the rise of emerging nations and political changes worldwide have led to the movement of global manufacturing sectors. Taiwan's previous model of mass production with low profit margins no longer serves as the most competitive profit model. For the manufacturing industry, quality, flexibility, and speed are now the keys to success. Through the integration of AI into manufacturing, achieving a mode of production characterized by small quantities and high diversity can enhance operational efficiency or service quality, thereby achieving transformation and upgrading.
Challenges for the manufacturing
industry are increasingly mounting
AI empowers equipment with learning
capabilities
The
greatest help AI brings to manufacturing lies in predictive maintenance,enabling machine
equipment to have judgment capabilities akin to humans. Not only can it
automatically complete various manufacturing processes, but through the
training and learning of big data, it can also make judgments, predictions, and
take appropriate actions. Predictive diagnosis of manufacturing equipment is a
major advantage of AI in the manufacturing industry. Predictive diagnosis
should include equipment life cycles, part life cycles, maintenance and repair
management, decision support management, real-time operation monitoring, and
other systems. By collecting real-time data and utilizing AI learning
techniques to establish predictive models, unplanned failure losses can be
reduced, ensuring stable operation of production lines.
Improving yield
is a key indicator for reducing production costs and enhancing product quality.
Integrating AI algorithms to enhance process analysis techniques can optimize
product quality, rapidly predict product quality characteristics, effectively
shorten the R&D cycle, improve yield, and accelerate time to market.
Introducing AI enables equipment to have visual learning capabilities. When
encountering product defects, there is no longer a need for manual reinspection
or adjustment of judgment criteria.
Machine arms learning autonomously
CHUNG YI-
EZiFASS: https://www.tmts.tw/en/product/2194
HOLDING ELECTRIC- RFID for smart Tool
Management
https://www.tmts.tw/en/product/1710
LNC- Articulated Robot Controller: https://www.tmts.tw/en/product/1724
BLUM- LC50
Digilog: https://www.tmts.tw/en/product/1692
EVERMORE- Industrial Robotic Arm: https://www.tmts.tw/en/product/50
DOWELL- ESPRIT CAM:
https://www.tmts.tw/en/product/1459
YCM- NH500A - High Production 2-Pallet
Horizontal Machining Center: https://www.tmts.tw/en/product/923
Sources：ITRI / https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=18_content&SiteID=1&MmmID=1036452026061075714&MGID=1035141040456274172
