BP网络在优化机械加工参数中的应用

机械加工中的误差复映现象使加工参数的选择主要依靠工人的经验,为此,利用BP网的任意非线性映射能力,通过学习人的经验,实现机械加工参数的优化选择。分析了机械加工中误差复映问题的特点,利用改进后的BP网络算法的非线性映射能力,逼近误差复映系数与工件材料硬度、进给量等因素之间的非线性关系,对训练成熟的网络输入加工前毛坯误差、工件材料硬度等,可以输出满足加工要求的加工次数和各次的加工量。通过实例说明了采用附加动量法和自适应学习率改进后的BP算法收敛快,且不易限入局部极小值。在分析误差复映问题模型和比较不同网络结构的训练结果的基础上,确定了BP网络结构。通过在MATLAB中对网络的测试结果,验证了用BP网络实现优化机械加工参数的可行性。

Application of BP network in optimizing machining parameters

The error reflection in machining parameters selection mainly depended on workers' experience. To optimize the selection of parameters, the non-linear mapping ability of mended Back-Propagation (BP) network arithmetic was utilized to approximate the non-linear relationship between error reflection coefficiency and several factors such as hardness of work piece, feeding speed and so on. As machining conditions such as error of rough before machining, hardness of work piece were input to the network having been trained, times of processing and processing quantity of each time could be acquired as output. BP arithmetic was mended by self-adapting learning rate and appended momentum. Finally, an example was given to prove that the mended BP arithmetic could converge more quickly and reduce the chances of falling into a local minimum. The structure of BP network was determined by analyzing model of error reflection phenomenon and comparing training results with different network structures. The feasibility of optimizing machining parameters with BP network was validated by testing network with test collection in MATLAB.