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首页> 外文期刊>Optics and Lasers in Engineering >Modeling and optimization on Nd:YAG laser turned micro-grooving of cylindrical ceramic material
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Modeling and optimization on Nd:YAG laser turned micro-grooving of cylindrical ceramic material

机译:Nd:YAG激光旋转圆柱陶瓷材料微刻槽的建模与优化

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摘要

Nd:YAG laser turning is a new technique for manufacturing micro-grooves on cylindrical surface of ceramic materials needed for the present day precision industries. The importance of laser turning has directed the researchers to search how accurately micro-grooves can be obtained in cylindrical parts. In this paper, laser turning process parameters have been determined for producing square micro-grooves on cylindrical surface. The experiments have been performed based on the statistical five level central composite design techniques. The effects of laser turning process parameters i.e. lamp current, pulse frequency, pulse width, cutting speed (revolution per minute, rpm) and assist gas pressure on the quality of the laser turned micro-grooves have been studied. A predictive model for laser turning process parameters is created using a feed-forward artificial neural network (ANN) technique utilized the experimental observation data based on response surface methodology (RSM). The optimization problem has been constructed based on RSM and solved using multi-objective genetic algorithm (GA). The neural network coupled with genetic algorithm can be effectively utilized to find the optimum parameter value for a specific laser micro-turning condition in ceramic materials. The optimal process parameter settings are found as lamp current of 19 A, pulse frequency of 3.2 kHz, pulse width of 6% duty cycle, cutting speed as 22 rpm and assist air pressure of 0.13 N/mm~2 for achieving the predicted minimum deviation of upper width of -0.0101 mm, lower width 0.0098 mm and depth -0.0069 mm of laser turned micro-grooves.
机译:Nd:YAG激光车削是一种用于在当今精密工业所需的陶瓷材料圆柱表面上制造微细沟槽的新技术。激光车削的重要性已指导研究人员研究如何在圆柱零件上获得精确的微沟槽。在本文中,已经确定了激光车削工艺参数,以在圆柱表面上产生方形微槽。实验是基于统计五级中央复合设计技术进行的。研究了激光车削工艺参数(即灯电流,脉冲频率,脉冲宽度,切割速度(每分钟转数,rpm)和辅助气压)对激光车削微槽质量的影响。使用前馈人工神经网络(ANN)技术创建了激光车削工艺参数的预测模型,该技术利用了基于响应面方法(RSM)的实验观察数据。该优化问题是基于RSM构造的,并使用多目标遗传算法(GA)进行了求解。结合遗传算法的神经网络可以有效地利用它来为陶瓷材料中特定的激光微车削条件找到最佳参数值。最佳的工艺参数设置是:灯电流为19 A,脉冲频率为3.2 kHz,脉冲宽度为6%的占空比,切割速度为22 rpm,辅助气压为0.13 N / mm〜2,以实现预期的最小偏差激光车削微槽的上宽度为-0.0101毫米,下宽度为0.0098毫米,深度为-0.0069毫米。

著录项

  • 来源
    《Optics and Lasers in Engineering》 |2009年第9期|917-925|共9页
  • 作者单位

    Department of Production Engineering, Jadavpur University, Kolkata 700032, India;

    Department of Production Engineering, Jadavpur University, Kolkata 700032, India;

    Department of Production Engineering, Jadavpur University, Kolkata 700032, India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    nd:YAC laser; micro-grooving; RSM; ANN; GA;

    机译:nd:YAG激光;微刻槽RSM;人工神经网络GA;

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