数控钻孔混合自适应控制提高刀具寿命和表面质量

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION Insight Pub Date : 2023-10-01 DOI:10.1784/insi.2023.65.10.570
J Susai Mary, M A Sai Balaji, D Dinakaran
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引用次数: 0

摘要

智能加工要求对加工参数进行在线自适应,以提高刀具寿命和产品质量,降低加工成本。提出了一种新型的钻井过程混合自适应控制系统。HAC系统是两种自适应控制的组合:几何自适应控制(GAC)和优化自适应控制(ACO)。它使孔的粗糙度保持在公差范围内,而不影响工具寿命。响应面模型(RSM)用于以速度、进给、加速度和力信号作为输入,对钻头磨损和表面粗糙度进行建模。该模型对磨损和粗糙度的预测精度分别为97.1%和93.6%。通过麻省理工学院的规则实现粗糙度控制,并通过遗传算法优化最小化刀具磨损。对自适应算法给出的加工条件进行了仿真和验证。结果表明,刀具寿命提高了7%,表面粗糙度提高了11%。
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Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality
Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.
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来源期刊
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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