【摘要】 为准确预测塔形组合式金刚石圆盘锯在荒料锯切过程中的功率，以组合锯切系统中单锯片的单磨粒平均未变形切屑厚度为媒介，建立了锯切功率的参数模型，并对其进行修正，提出一种少样本快速预测模型，通过锯切实验测量不同参数组合下的锯切功率，采用多元线性回归方法拟合数据以获取可靠的模型系数。最后以锯切参数为优化变量，以锯切比能和锯切时间最小为优化目标建立优化模型，并采用改进粒子群算法对模型进行优化求解。试验结果表明，参数模型充分解释了各锯切参数对锯切功率的影响，能够准确预测不同锯片组合方式下的锯切功率，改进的粒子群算法有较高的优化性能，使用优化后的参数能够显著降低锯切功率。更多还原

【Abstract】 To precisely forecast the power of a tower combined diamond circular saw blade during material sawing, a model for sawing power was established using the average thickness of an undeformed chip from a single grinding grain of an individual saw blade within the combination saw as a medium. This model was then refined to enhance accuracy.A fast and accurate prediction model, requiring only a small number of samples, was developed. Sawing power was measured through various parameter combinations via sawing experiments, and model coefficients were obtained by fitting the data using multivariate linear regression techniques. An optimization model was subsequently established, with sawing parameters as optimization variables. The objectives of this model were to minimize sawing specific energy and reduce sawing time. An optimized particle swarm algorithm was adopted to solve the model. The experimental results reveal that the parameter model can comprehensively elucidate the influence of various sawing parameters on sawing power, with the model accurately forecasting the sawing power under different saw blade combinations. The improved particle swarm algorithm demonstrates strong optimization performance, with optimized parameters contributing to significant reductions in sawing power.更多还原