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金属加工过程中,切削刀具的状态对于生产效率和表面加工质量有重要影响,因此刀具磨损监测具有重要意义。刀具磨损监测是柔性制造系统研究工程的一个重要课题。切削力信号作为加工过程中最稳定和最可靠的信号,和刀具磨损密切相关。从实验上分析切削力与刀具磨损的相关性,提出刀具切削力变化与磨损变化是一致的。基于有限元分析软件对车削加工进行仿真研究,模拟了切削力的大小分布,并将模拟结果与实验结果进行了比较分析,为实际工艺参数的选择提供了理论指导。 相似文献
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《机械工程学报》2020,(2)
针对碳纤维复合材料钻孔加工过程中无法实现加工质量与效率统一以及离线优化得到的钻削参数没有考虑变参数刀具磨损等不确定因素影响问题,提出一种新的粗糙度在线监测与加工参数自适应优化方法。采用一组新的无量纲特征以实现变参数刀具磨损监测;以刀具磨损状态值、进给速度以及主轴转速构成特征向量,进而建立基于支持向量回归的孔壁粗糙度在线监测模型。当监测系统判定孔壁粗糙度不合格时,利用模拟退火算法在当前刀具磨损状态下进行钻削参数优化。利用复合材料变参数钻削试验来验证上述方法的有效性。试验结果表明,该方法能够准确判定粗糙度的质量状态,同时有效实现钻削参数自适应优化,解决了复合材料制孔过程中加工质量与效率统一的问题。 相似文献
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在实际切削加工中刀具磨损的全状态先验知识获取困难,而刀具磨钝状态下的先验知识则较易获取。针对这种不完备先验知识情况,以切削力为监测信号,提出基于连续隐马尔可夫模型(CHMM)的刀具磨损状态评估技术。应用小波包分解技术提取信号特征信息,利用刀具磨钝状态下的先验归一化特征信息建立CHMM监测模型;根据刀具未知状态特性向量与监测模型间的对数似然度获取刀具性能指标,实现刀具磨损状态评价。铣刀全寿命磨损实验表明:该方法能在仅具备磨钝状态先验知识情况下,实现对刀具的磨损状态的初步评估,且所需样本数较少,训练速度快。 相似文献
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基于切削力系数的铣刀磨损状态监测方法提出了与切削参数独立的刀具磨损指标。由于存在干扰机床正常加工、实时性不佳、传感器安装不便和成本过高等问题,限制了其在实际工业环境中的应用。针对上述问题,结合切削力与主轴电流的关系,提出一种基于主轴切削电流系数的铣刀磨损状态监测方法。首先,融合切削力系数和主轴电流的优点,建立铣削电流模型;其次,根据切削电流模型进行切削电流系数辨识,记录新刀状态下切削系数;然后,使用切削系数实时估计相同加工工况下新刀切削电流,监测实际切削电流偏离估计值的程度,判断铣刀磨损状态;最后,通过实验与力信号对比验证该方法的正确性。实验结果表明,该方法可以替代基于切削力系数的磨损状态监测方法,能有效、实时、无干扰、便利和低成本地识别新刀、正常和严重3种磨损状态。 相似文献
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为实现刀具磨损的准确预测,对加工过程的换刀和参数优化提供指导,提出一种基于最大信息系数(MIC)和改进的Bagging集成高斯过程回归(Bagging-GPR)的刀具磨损预测方法,建立切削力信号与刀具磨损间的非线性映射关系。采集加工的切削力信号,运用时域、小波包分解和经验模态分解提取切削力信号特征,并利用MIC分析特征与刀具磨损的相关度来实现特征选择,避免预测模型的“维数灾难”。为提高预测模型的精度,考虑高斯子模型内部核函数的差异性及准确性,利用Bagging对高斯核函数进行随机组合,作为各子模型的核函数,构建改进的Bagging-GPR模型实现刀具磨损值预测,并基于铣削实验数据验证了所提方法的有效性和优异性。 相似文献
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M. Aramesh Y. Shaban S. Yacout H. A. Kishawy M. Balazinski 《Machining Science and Technology》2016,20(1):132-147
A survival analysis methodology is employed through a novel approach to model the progressive states of tool wear under different cutting conditions during machining of titanium metal matrix composites (Ti-MMCs). A proportional hazards model (PHM) with a Weilbull baseline is developed to estimate the reliability and hazard functions of the cutting inserts. A proper criterion is assigned to each state of tool wear and used to calculate the tool life at the end of each state. Accounting for the machining time and different stages of tool wear, in addition to the effect of cutting parameters, an accurate model is proposed. Investigating the results obtained for different states, it was shown that the evolution of the time-dependent phenomena, such as different tool wear mechanisms, throughout the whole machining process were also reflected in the model. The accuracy and reliability of the predicted tool lives were experimentally validated. The results showed that the model gives very good estimates of tool life and the critical points at which changes of states take place. 相似文献
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S. Jeyakumar K. Marimuthu T. Ramachandran 《Journal of Mechanical Science and Technology》2013,27(9):2813-2822
The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes. 相似文献
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J. M. Zhou M. Andersson J. E. Stahl 《The International Journal of Advanced Manufacturing Technology》2003,22(9-10):697-702
In precision hard turning, tool flank wear is one of the major factors contributing to the geometric error and thermal damage in a machined workpiece. Tool wear not only directly reduces the part geometry accuracy but also increases the cutting forces drastically. The change in cutting forces causes instability in the tool motion, and in turn, more inaccuracy. There are demands for reliably monitoring the progress of tool wear during a machining process to provide information for both correction of geometric errors and to guarantee the surface integrity of the workpiece. A new method for tool wear monitoring in precision hard turning is presented in this paper. The flank wear of a CBN tool is monitored by feature parameters extracted from the measured passive force, by the use of a force dynamometer. The feature parameters include the passive force level, the frequency energy and the accumulated cutting time. An ANN model was used to integrate these feature parameters in order to obtain more reliable and robust flank wear monitoring. Finally, the results from validation tests indicate that the developed monitoring system is robust and consistent for tool wear monitoring in precision hard turning. 相似文献
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K.M. Rezaur Rahman M. Rahman K.S. Neo M. Sawa Y. Maeda 《The International Journal of Advanced Manufacturing Technology》2006,27(9-10):911-917
Diamond tools are used in ultra precision machining for their outstanding hardness and crystalline structure, which enable the fabrication of very sharp cutting edges. Single crystal diamond tools are thus extremely useful to machine electroless nickel-plated dies which are generally used for making molds for optical components. This paper deals with the objective to evaluate the performance and suitability of a single crystal diamond tool during microgrooving on electroless nickel plated workpieces. Effects of different machining parameters on overall machining performance were also investigated. The experimental results revealed that long distance (50 km) machining of microgrooves on electroless nickel is possible with a single crystal diamond tool without any significant tool wear. Some groove wear on the rake face were found after machining 28.5 km. No evidence of chipping or wear had been observed on the flank face during the total machining length. The surface roughness range of the machined workpieces was found to be 4–6 nm. Both thrust and cutting components of the machining forces showed an increasing trend with increasing machining distance, though magnitude of the thrust forces were found to increase more than the cutting forces. 相似文献
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Y. Choi R. Narayanaswami A. Chandra 《The International Journal of Advanced Manufacturing Technology》2004,23(5-6):419-428
Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine. 相似文献
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为实现在正常生产条件下进行刀具磨损的长期在线监测,提出了基于主轴电流信号和粒子群优化支持向量机模型(PSO-SVM)的刀具磨损状态间接监测方法。首先对数控机床主轴电机电流信号进行分析,将与刀具磨损相关的主轴电流信号多个特征参数和EMD能量熵进行特征融合作为输入特征向量;其次,通过粒子群寻优算法(PSO)对支持向量机模型(SVM)参数进行优化,建立基于主轴电流信号融合特征和PSO-SVM理论的刀具磨损状态识别模型;最后,通过实验采集某立式加工中心主轴在刀具不同磨损状态下电流信号进行验证,并与传统SVM模型、BP神经网络模型进行了对比分析。结果表明,所提出的方法具有较高的准确率和较好的泛化能力。能够实现正常生产条件下对刀具磨损的长期在线监测。 相似文献
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自动化切削加工过程中,准确可靠地监测刀具磨损状态是保证加工质量和加工效率的关键。针对刀具磨损状态相关特征提取繁琐、准确率低及传统的深度学习网络不能全面提取数据隐含信息等问题,提出了一种以卷积神经网络(CNN)和双向长短时记忆(BiLSTM)网络集成模型为基础并通过在卷积神经网络中添加批量标准化层和采用两个双向长短时记忆网络层的改进模型,该模型通过自动提取小波阈值降噪等预处理和降采样后的切削力、振动和声音信号的空间和时序特征来实现刀具磨损状态监测。将改进模型与CNN-BiLSTM模型及传统的深度学习模型进行对比,发现改进模型在精度和稳定性方面有较大提升。所提方法为准确监测自动化加工过程中刀具磨损状态、提高生产效率和加工质量提供了技术支持。 相似文献
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Micro-end milling is used for manufacturing of complex miniaturized components precisely in wide range of materials. It is important to predict cutting forces accurately as it plays vital role in controlling tool and workpiece deflections as well as tool wear and breakage. The present study attempts to incorporate process characteristics such as edge radius of cutting tool, effective rake and clearance angles, minimum chip thickness, and elastic recovery of work material collectively while predicting cutting forces using mechanistic model. To incorporate these process characteristics effectively, it is proposed to divide cutting zone into two regions: shearing- and ploughing-dominant regions. The methodology estimates cutting forces in each partitioned zone separately and then combines the same to obtain total cutting force at a given cutter rotation angle. The results of proposed model are validated by performing machining experiments over a wide range of cutting conditions. The paper also highlights the importance of incorporating elastic recovery of work material and effective rake and clearance angle while predicting cutting forces. It has been observed that the proposed methodology predicts the magnitude and profile of cutting forces accurately for micro-end milling operation. 相似文献
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针对不同走刀路径下的复杂曲面加工过程进行球头铣刀铣削Cr12MoV加工复杂曲面研究,分析不同走刀路径下铣削力和刀具磨损的变化趋势。试验结果表明:通过对比分析直线铣削和曲面铣削过程中的最大未变形切屑厚度,可以得出单周期内曲面铣削的力大于直线铣削过程的力,铣削相同铣削层时环形走刀测得的切削力普遍大于往复走刀测得的切削力;以最小刀具磨损为优化目标,运用方差分析法分析得出不同走刀路径的影响刀具磨损的主次因素,同时利用残差分析方法建立球头铣刀加工复杂曲面刀具磨损预测模型,并通过试验进行验证。 相似文献
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实时准确地监测铣削状态对于提高加工质量与加工效率具有重要意义,切削力作为重要的加工状态监测对象,因其监测设备昂贵且安装不便而受到限制,为此提出一种考虑刀具磨损的基于主轴电流的铣削力监测方法.首先基于切削微元理论建立了考虑后刀面磨损的铣削力模型,并通过铣削实验进行铣削力模型系数标定;然后对主轴电流与铣削力的关系进行理论建... 相似文献