Intelligent Optimization and Quality Improvement of Material Forming and Control Engineering in Mechanical Manufacturing
Abstract
As the mechanical manufacturing industry undergoes a critical phase of intelligent transformation, technological advancement and
quality control in material forming and control engineering have become pivotal breakthroughs. This study focuses on intelligent optimization
and quality enhancement in material forming and control engineering, analyzing three core technological pillars: machine learning, digital
twins, and industrial IoT. It further explores three key pathways for quality improvement, including intelligent perception and monitoring,
and proposes an integrated optimization solution that combines intelligent technologies with forming engineering. The aim is to overcome
the limitations of traditional process reliance on experience, achieve precise control of forming processes, and implement closed-loop quality
management, thereby providing robust technical support for the high-quality development of the mechanical manufacturing industry
quality control in material forming and control engineering have become pivotal breakthroughs. This study focuses on intelligent optimization
and quality enhancement in material forming and control engineering, analyzing three core technological pillars: machine learning, digital
twins, and industrial IoT. It further explores three key pathways for quality improvement, including intelligent perception and monitoring,
and proposes an integrated optimization solution that combines intelligent technologies with forming engineering. The aim is to overcome
the limitations of traditional process reliance on experience, achieve precise control of forming processes, and implement closed-loop quality
management, thereby providing robust technical support for the high-quality development of the mechanical manufacturing industry
Keywords
Materials Forming and Control Engineering; Intelligent Optimization; Quality Improvement
Full Text:
PDFReferences
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DOI: http://dx.doi.org/10.70711/frim.v4i6.9700
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