振动与冲击
振動與遲擊
진동여충격
JOURNAL OF VIBRATION AND SHOCK
2015年
12期
140-145
,共6页
超声振动辅助磨削%牙科氧化锆陶瓷%切削力预测%指数模型%BP 神经网络%理论模型
超聲振動輔助磨削%牙科氧化鋯陶瓷%切削力預測%指數模型%BP 神經網絡%理論模型
초성진동보조마삭%아과양화고도자%절삭력예측%지수모형%BP 신경망락%이론모형
ultrasonic vibration assisted grinding%dental zirconia ceramics%cutting force prediction%index model%BP neural networks%theoretical model
通过单因素实验设计，开展了牙科氧化锆陶瓷的超声振动辅助磨削实验，分别建立了超声振动辅助磨削加工切削力指数预测模型、BP 神经网络预测模型以及理论预测模型。通过验证实验对比分析了三种模型的预测精度，并阐明了误差产生的原因。结果表明，基于 BP 神经网络的切削力预测模型相对于指数和理论模型具有较高的预测精度，其平均相对误差仅为9．60％，理论模型因未能考虑材料的塑性流动去除，导致预测精度较低。
通過單因素實驗設計，開展瞭牙科氧化鋯陶瓷的超聲振動輔助磨削實驗，分彆建立瞭超聲振動輔助磨削加工切削力指數預測模型、BP 神經網絡預測模型以及理論預測模型。通過驗證實驗對比分析瞭三種模型的預測精度，併闡明瞭誤差產生的原因。結果錶明，基于 BP 神經網絡的切削力預測模型相對于指數和理論模型具有較高的預測精度，其平均相對誤差僅為9．60％，理論模型因未能攷慮材料的塑性流動去除，導緻預測精度較低。
통과단인소실험설계，개전료아과양화고도자적초성진동보조마삭실험，분별건립료초성진동보조마삭가공절삭력지수예측모형、BP 신경망락예측모형이급이론예측모형。통과험증실험대비분석료삼충모형적예측정도，병천명료오차산생적원인。결과표명，기우 BP 신경망락적절삭력예측모형상대우지수화이론모형구유교고적예측정도，기평균상대오차부위9．60％，이론모형인미능고필재료적소성류동거제，도치예측정도교저。
Ultrasonic vibration assisted grinding (UVAG)experiments on dental zirconia ceramics were conducted by using single-factor method.An index prediction model,a BP neural networks prediction model,as well as a theoretical prediction model of cutting force in UVAG were proposed.A verification experiment was carried out to study the prediction accuracy of these three models,and the causes of errors were revealed.The results indicate that the BP neural networks prediction model has higher precision compared with the two others,and the relative error is only 9.60%.The prediction accuracy of theoretical model is poor due to the neglect of plastic flow removal during UVAG.
