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<p>Aiming at the multi-objective balancing difficulty of rock-breaking efficiency, wear life, energy consumption and running stability for conical pick group in mining equipment, this paper firstly introduces factor space theory (FST) to the multi-objective optimization of mechanical structures, and proposes a factor-coupling-knowledge embedded collaborative optimization method distinguished from traditional blind optimizers and empirical grouping cooperative co-evolutionary algorithms. A total of 23 key factors covering structural geometry, material properties, dynamic working conditions and comprehensive performance are identified; an improved grey relational analysis combined with Sobol global sensitivity analysis quantifies nonlinear factor coupling and screens cone angle, axial spacing and matrix hardness as core control parameters. A physical-mechanism-data hybrid mapping function is constructed to establish the multi-objective optimization mathematical model. On this basis, a decision space decomposition strategy driven by factor interaction matrix is put forward, and an improved FS-NSGA-II genetic algorithm guided by factor sensitivity indices is developed for high-dimensional coupled optimization searching. A three-level verification system including finite element simulation, laboratory cutting bench test and underground industrial trial is built to verify the proposed method. Bench test results show that compared with traditional empirical design, the optimized pick group achieves 30.0% lower volumetric wear rate, 19.0% higher rock-breaking efficiency and 26.0% reduced rock-breaking specific energy under fixed rock samples and standardized cutting parameters. In a 30-day underground industrial trial with variable complex geology, the optimized scheme reduces unit excavation power consumption by 18.4%, lifts tunneling efficiency by 17.3% and cuts pick consumption cost by 39.9%. This research forms a complete closed-loop technical system of factor identification-coupling quantification-surrogate modeling-collaborative optimization-engineering verification, providing theoretical support and engineering scheme for intelligent green design of mining cutting tools, and offering a new multi-disciplinary collaborative optimization paradigm for complex mechanical systems.</p>

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Multi-objective collaborative optimization of conical pick group based on factor space theory

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月1日

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  • Jianyong Wang,
  • Lu Zhang,
  • Huan Wang,
  • Caifen Zhang,
  • Honglei Su,
  • Jiujun Duan,
  • Jianpeng Hu

摘要

Aiming at the multi-objective balancing difficulty of rock-breaking efficiency, wear life, energy consumption and running stability for conical pick group in mining equipment, this paper firstly introduces factor space theory (FST) to the multi-objective optimization of mechanical structures, and proposes a factor-coupling-knowledge embedded collaborative optimization method distinguished from traditional blind optimizers and empirical grouping cooperative co-evolutionary algorithms. A total of 23 key factors covering structural geometry, material properties, dynamic working conditions and comprehensive performance are identified; an improved grey relational analysis combined with Sobol global sensitivity analysis quantifies nonlinear factor coupling and screens cone angle, axial spacing and matrix hardness as core control parameters. A physical-mechanism-data hybrid mapping function is constructed to establish the multi-objective optimization mathematical model. On this basis, a decision space decomposition strategy driven by factor interaction matrix is put forward, and an improved FS-NSGA-II genetic algorithm guided by factor sensitivity indices is developed for high-dimensional coupled optimization searching. A three-level verification system including finite element simulation, laboratory cutting bench test and underground industrial trial is built to verify the proposed method. Bench test results show that compared with traditional empirical design, the optimized pick group achieves 30.0% lower volumetric wear rate, 19.0% higher rock-breaking efficiency and 26.0% reduced rock-breaking specific energy under fixed rock samples and standardized cutting parameters. In a 30-day underground industrial trial with variable complex geology, the optimized scheme reduces unit excavation power consumption by 18.4%, lifts tunneling efficiency by 17.3% and cuts pick consumption cost by 39.9%. This research forms a complete closed-loop technical system of factor identification-coupling quantification-surrogate modeling-collaborative optimization-engineering verification, providing theoretical support and engineering scheme for intelligent green design of mining cutting tools, and offering a new multi-disciplinary collaborative optimization paradigm for complex mechanical systems.