摘要
通过试验研究滚刀磨损量与滚刀转速、冷风流量、冷风喷射速度、冷风温度、描述滚刀状态的已磨损量和已复磨量以及工件硬度等7项因素之间的数量关系,建立基于上述因素的多层前馈人工神经网络BP模型。在BP网络模型基础上,将滚刀磨损与7个因素之间的关系转化成滚刀总寿命与除描述滚刀状态的参数外的5个因素间的关系,建立滚刀总寿命推算程序作为滚刀总寿命的预测手段,据此分析滚刀总寿命与上述因素间的关系。试验研究与分析表明:在低温冷风微量润滑条件下,滚齿工艺参数对滚刀总寿命的影响是多方面的,在所考察的工艺参数范围内,冷风喷射速度的影响最大,达到238%,其次是冷风流量,达13.5%,而冷风温度的影响仅4.4%。
An ANN BP model was built to imitate the relation between output, the wear of hobbing cutter, and inputs, such as rotary speed of cutter, air flow, air jet speed, air temperature, situation of hobbing cutter and hardness of workpiece. A program was compiled to extrapolate the total life of bobbing cutter based on the BP model. The conclusion could be obtained by the program about the total life of hobbing cutter in cryogenic air with MQL, as follow. When parameters vary in given zone, air jet speed has the largest effect on the total life of bobbing cutter by 23.8%, and follows the air flow by 13.5%, and last is the air temperature by 4. 4%.
出处
《机床与液压》
北大核心
2017年第20期62-65,共4页
Machine Tool & Hydraulics
关键词
滚刀磨损
低温冷风加工
人工神经网络
微量润滑
Hobbing cutter wear
Low-temperature cold air cutting
Artificial neural network
Minimum quantity lubrication
