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<p>To achieve high efficiency and low energy consumption in smart manufacturing, this study proposes a feed-rate optimization control strategy for CNC milling processes, aiming to reduce spindle power consumption and machining time by equalizing cutting loads during rough machining. Predictive models for energy consumption and surface roughness were established through experimental tests and mechanistic derivations. Constant parameters were used during finishing operations to evaluate the compensation capability for surface defects left by rough machining. Results indicated that moderate load increases significantly reduced energy consumption by minimizing non-cutting time and enhancing motor efficiency. However, overly aggressive load targets led to surface defects exceeding the compensatory capabilities of finishing, causing irreversible quality deterioration. Multi-objective Pareto-based optimization, utilizing Gaussian process regression (GPR), identified a monotonic Pareto front clearly illustrating trade-offs between surface roughness and energy consumption and determined an optimal load target range (10–12%) effectively balancing energy efficiency, machining productivity, and surface quality. This strategy underscores the importance of considering tool wear and finishing compensation to implement high-efficiency, low-energy, and high-quality manufacturing practices.</p>

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Feed-rate optimization and load equalization for energy-efficient CNC milling: a multi-objective approach

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Ta-Jen Peng,
  • Guan-Wei Du

摘要

To achieve high efficiency and low energy consumption in smart manufacturing, this study proposes a feed-rate optimization control strategy for CNC milling processes, aiming to reduce spindle power consumption and machining time by equalizing cutting loads during rough machining. Predictive models for energy consumption and surface roughness were established through experimental tests and mechanistic derivations. Constant parameters were used during finishing operations to evaluate the compensation capability for surface defects left by rough machining. Results indicated that moderate load increases significantly reduced energy consumption by minimizing non-cutting time and enhancing motor efficiency. However, overly aggressive load targets led to surface defects exceeding the compensatory capabilities of finishing, causing irreversible quality deterioration. Multi-objective Pareto-based optimization, utilizing Gaussian process regression (GPR), identified a monotonic Pareto front clearly illustrating trade-offs between surface roughness and energy consumption and determined an optimal load target range (10–12%) effectively balancing energy efficiency, machining productivity, and surface quality. This strategy underscores the importance of considering tool wear and finishing compensation to implement high-efficiency, low-energy, and high-quality manufacturing practices.