LÜ L S , DENG Z H , LIU T , et al . Intelligent technology in grinding process driven by data:a review [J ] . Journal of Manufacturing Processes , 2020 , 58 : 1039 - 1051 . DOI: 10.1016/j.jmapro.2020.09.018 http://doi.org/10.1016/j.jmapro.2020.09.018 https://linkinghub.elsevier.com/retrieve/pii/S1526612520306071 https://linkinghub.elsevier.com/retrieve/pii/S1526612520306071

周昊飞 , 刘玉敏 . 基于深度置信网络的大数据制造过程实时智能监控 [J ] . 中国机械工程 , 2018 , 29 ( 10 ): 1201 - 1207 ,1213. 针对基于浅层学习模型的过程监控方法难以对大数据制造过程运行状态进行实时智能监控的问题，提出了基于深度置信网络的大数据制造过程实时智能监控方法。利用灰度图建立大数据制造过程质量图谱，以精准表达其过程的运行状态；构建用于识别大数据制造过程质量图谱的深度置信网络；应用离线训练好的深度置信网络模型对当前监控窗口内的过程质量图谱进行识别，实现大数据制造过程实时智能监控。最后，应用该方法对某注塑件大数据制造过程进行实时质量智能监控，结果表明：所提方法的识别性能明显优于基于主成分分析与BP神经网络、支持向量机的识别模型，能有效应用于大数据制造过程实时质量智能监控。

ZHOU H F , LIU Y M . Real-time intelligent monitoring for manufacturing processes with big data based on deep belief networks [J ] . China Mechanical Engineering , 2018 , 29 ( 10 ): 1201 - 1207 ,1213. (in Chinese) Aimed at the problems that process monitoring method based on shallow learning model was difficult to fulfill the requirements of real-time intelligent monitoring for manufacturing processes with big data，a real-time intelligent monitoring method was proposed herein based on deep belief network.Firstly，a quality spectrum for manufacturing processes with big data was established using gray scale images to represent operation states of manufacturing processes with big data.Secondly，the deep belief network was established to recognize the quality spectrum for manufacturing processes with big data.Then， the process quality-spectrum in the “monitoring window” was recognized by the deep belief networks from off-line training to realize real-time intelligent monitoring for manufacturing processes with big data.Finally， the proposed monitoring method was applied to monitor the operation states of an injection molding manufacturing processes with big data.Results indicate that the proposed monitoring method has a better recognition performance compared with the BP neural networks based on principal component analysis and the support vector machines based on principal component analysis， which demonstrates that the proposed monitoring method is efficient.

TROMPT G M , KRASIL’NIKOV A Y . Improving the grinding precision by stable operation of an active monitoring system [J ] . Russian Engineering Research , 2013 , 33 ( 1 ): 36 - 38 . DOI: 10.3103/S1068798X13010115 http://doi.org/10.3103/S1068798X13010115 http://link.springer.com/10.3103/S1068798X13010115 http://link.springer.com/10.3103/S1068798X13010115

林峰 , 焦慧锋 , 傅建中 . 基于贝叶斯网络的平面磨削状态智能监测技术研究 [J ] . 中国机械工程 , 2011 , 22 ( 11 ): 1269 - 1273 . 为解决平面磨削过程中工件表面粗糙度预测和砂轮钝化监测困难的问题，利用贝叶斯网络建立了平面磨削状态智能监测模型。该模型在获取系统磨削用量和工件材料的基础上，在线提取磨削声发射信号的峭度系数，可以有效预测工件粗糙度和识别砂轮钝化状态，为数控系统调节加工参数提供参考。该模型在平面磨床的磨削监测试验中取得了良好的效果。

LIN F , JIAO H F , FU J Z . Research on intelligent monitoring technique of machining state for surface grinder based on bayesian network [J ] . China Mechanical Engineering , 2011 , 22 ( 11 ): 1269 - 1273 . (in Chinese)

THOMAZELLA R , LOPES W N , AGIAR P R , et al . Digital signal processing for self-vibration monitoring in grinding:a new approach based on the time-frequency analysis of vibration signals [J ] . Measurement , 2019 , 145 : 71 - 83 . DOI: 10.1016/j.measurement.2019.05.079 http://doi.org/10.1016/j.measurement.2019.05.079 https://linkinghub.elsevier.com/retrieve/pii/S0263224119305238 https://linkinghub.elsevier.com/retrieve/pii/S0263224119305238

姜晨 , 李郝林 . 基于声发射信号的精密外圆切入磨削时间评估算法及试验研究 [J ] . 机械工程学报 , 2014 , 50 ( 5 ): 194 - 200 . 针对精密外圆切入磨削智能监控的需求，设计一种基于声发射信号的磨削时间在线评估方法。通过建立声发射信号方均根值曲线预测模型，获得声发射信号与磨削系统时间常数的关系，设计磨削系统时间常数在线计算方法；利用在线检测的声发射信号识别砂轮运动去除状态，推导基于声发射信号的外圆切入磨削表面粗糙度评价和工件几何精度预测模型，以此建立砂轮进给与驻留时间的评估算法；编写磨削时间分析评估软件，设计磨削时间在线评估方法，通过加工试验分析磨削时间对磨削加工精度与表面粗糙度的影响规律，并对评估算法进行验证。试验结果表明：该评估方法能够根据磨削时间有效评价加工质量，为精密外圆切入磨削智能监控与工艺优化提供决策依据。

JIANG C , LI H L . Algorithm and experiment of estimation of time of precision cylindrical plunge grinding based on acoustic emission signal [J ] . Journal of Mechanical Engineering , 2014 , 50 ( 5 ): 194 - 200 . (in Chinese) To satisfy the requirement of intelligence processing of precision cylindrical plunge grinding, an estimation method of grinding time of axis parts is presented using acoustic emission (AE) signal. The relationship between the grinding system time constant and the AE signal is presented by establishing the model of the root mean square (RMS) curve of the AE signals. The estimation of the grinding system time constant is proposed and the kinematics of wheel is analyzed by the on-line AE signals. According to deducing the model of size error in the precision cylindrical plunge grinding, the estimation algorithms of infeed and dwell times are established using the AE measurement. In addition, the estimation software is developed and the estimation methods are designed. The experiments were preformed to analyze the relationship of grinding time and machining qualities, such as surface roughness and size error, and verified the proposed algorithms. The experimental results indicate that this method can be used in the estimation of processing time of the precision cylindrical plunge grinding to ensure the grinding precision and improve the grinding efficiency for intelligence control and process optimizing.

BOTCHA B , RAJAGOPAL V , BABU N R , et al . Process-machine interactions and a multi-sensor fusion approach to predict surface roughness in cylindrical plunge grinding process [J ] . Procedia Manufacturing , 2018 , 26 : 700 - 711 . DOI: 10.1016/j.promfg.2018.07.080 http://doi.org/10.1016/j.promfg.2018.07.080 https://linkinghub.elsevier.com/retrieve/pii/S2351978918307510 https://linkinghub.elsevier.com/retrieve/pii/S2351978918307510

朱欢欢 , 迟玉伦 , 闻章 , 等 . 断续磨削表面烧伤机理与在线监测方法研究 [J ] . 表面技术 , 2021 , 50 ( 9 ): 1 - 16 .

ZHU H H , CHI Y L , WEN Z , et al . Research on burn mechanism of intelligent grinding surface and online monitoring method [J ] . Surface Technology , 2021 , 50 ( 9 ): 1 - 16 . (in Chinese)

OLIVEIRA J F G , BRAGA A P S , CARVALHO A C P L F . Development of a dressing monitoring system through artificial intelligence and acoustic maps for high performance grinding [J ] . International Journal of Manufacturing Technology and Management , 2007 , 12 ( 1 ): 171 - 183 . DOI: 10.1504/IJMTM.2007.014148 http://doi.org/10.1504/IJMTM.2007.014148 http://www.inderscience.com/link.php?id=14148 http://www.inderscience.com/link.php?id=14148

PANDIYAN V , CAESARENDRA W , TJAHJOWIDODO T , et al . In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm [J ] . Journal of Manufacturing Processes , 2018 , 31 : 199 - 213 . DOI: 10.1016/j.jmapro.2017.11.014 http://doi.org/10.1016/j.jmapro.2017.11.014 https://linkinghub.elsevier.com/retrieve/pii/S1526612517303481 https://linkinghub.elsevier.com/retrieve/pii/S1526612517303481

TIAN Y B , LIU F , WANG Y , et al . Development of portable power monitoring system and grinding analytical tool [J ] . Journal of Manufacturing Processes , 2017 , 27 : 188 - 197 . DOI: 10.1016/j.jmapro.2017.05.002 http://doi.org/10.1016/j.jmapro.2017.05.002 https://linkinghub.elsevier.com/retrieve/pii/S1526612517301135 https://linkinghub.elsevier.com/retrieve/pii/S1526612517301135

赵飞 , 梅雪松 , 李光东 , 等 . 数控成型磨齿机加工误差在线监测及补偿 [J ] . 机械工程学报 , 2013 , 49 ( 1 ): 171 - 177 . 研究数控机床加工过程在线监测及加工误差分析与补偿方法的问题，提出用于数控成型磨齿机齿向误差在线监测及补偿方法。数控机床加工过程中，加工工件和刀具间的相对位置关系对机床加工精度具有重要影响。通过获取数控机床内置信号(光栅、编码器)可以得到这一重要信息。基于内置信号同步采集方法，利用齐次坐标变换原理对数控机床进给轴的内置信号进行变换分析，获得机床空间加工轨迹，从而实现机床加工误差的在线监测与评估。同时，通过分析加工过程中各进给轴的动态位置信息，可以确定影响加工误差的主要因素。基于分析结果，通过软件实时补偿原理，结合数控机床控制系统特点，对主要误差源进行在线补偿，从而达到提高加工精度的目的。采用该方法对一数控成型磨齿机加工过程齿向误差进行评估与补偿，与三坐标测量机检测结果对比表明该方法可以有效地获取加工误差形貌。根据基于分析结果进行补偿后，使该机床齿向误差明显降低，提高了机床的加工精度等级。

ZHAO F , MEI X S , LI G D , et al . Machining error online monitoring and compensation of numerical control forming gear grinding machine [J ] . Journal of Mechanical Engineering , 2013 , 49 ( 1 ): 171 - 177 . (in Chinese) The problem of machining error online monitoring and compensation method is studied, and the machining error online monitoring and compensation method is proposed which takes numerical control forming gear grinding machine as the research object. The relative position of work piece and tool is important for machining precision during machining process. One can get the relative position by testing the built-in signal which include encoder and direct encoder signal. In the proposed method, the space trace of machining is calculated from the built-in signal which is processed by the homogeneous coordinate transformation. The main factors that bring out the machining error can be determined by analyzing the processed built-in signal, and compensated into the numerical control system by online real time method. The proposed method is implemented on a numerical control forming gear grinding machine to improve its precision. Compared with the coordinate measuring machine test result, the proposed method can detect the form of the machining error. After compensating, the tooth alignment machining error is lowered obviously.

NGUYEN D , YIN S , TANG Q , et al . Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis [J ] . Precision Engineering , 2019 , 55 : 275 - 292 . DOI: 10.1016/j.precisioneng.2018.09.018 http://doi.org/10.1016/j.precisioneng.2018.09.018 https://linkinghub.elsevier.com/retrieve/pii/S0141635918302277 https://linkinghub.elsevier.com/retrieve/pii/S0141635918302277

GAO Z Y , LIN J , WANG X F , et al . Grinding burn detection based on cross wavelet and wavelet coherence analysis by acoustic emission signal [J ] . Chinese Journal of Mechanical Engineering , 2019 , 32 ( 4 ): 104 - 113 . DOI: 10.1186/s10033-019-0420-0 http://doi.org/10.1186/s10033-019-0420-0 Magnetic Barkhausen Noise (MBN) method is known as an effective nondestructive evaluation (NDE) method for evaluation of residual stress in ferromagnetic materials. Some studies on the feasibility of the MBN method for NDE of residual strains were also conducted and found applicable. However, these studies are mainly focused on the state of residual strains which were introduced through a one-cycle-loading process. In practice, however, structures may suffer from an unpredictable and complicated loading history, i.e., the final state of plastic strain may be induced by several times of large loads. Whether the loading history has influences on MBN signals or not is of great importance for the practical application of the MBN method. In this paper, several ferromagnetic specimens with the same final state of residual strain but of different loading history were fabricated and inspected by using a MBN testing system. The experimental results reveal that the loading history has a significant influence on the detected MBN signals especially for a residual strain in range less than 1%, which doubts the feasibility to apply the MBN method simply in the practical environment. In addition, micro-observations on the magnetic domain structures of the plastic damaged specimens were also carried out to clarify the influence mechanism of loading history on the MBN signals.

FUKUHARA Y , SUZUKI S , SASAHARA H . Development of in-process monitoring system for grinding wheel surface temperature and grinding state [J ] . Advanced Materials Research , 2016 , 1136 : 624 - 629 . DOI: 10.4028/www.scientific.net/AMR.1136 http://doi.org/10.4028/www.scientific.net/AMR.1136 https://www.scientific.net/AMR.1136 https://www.scientific.net/AMR.1136

LLIO A D , PAOLETTI A , SFARRA S . Monitoring of MMCs grinding process by means of IR thermography [J ] . Procedia Manufacturing , 2018 , 19 : 95 - 102 . DOI: 10.1016/j.promfg.2018.01.014 http://doi.org/10.1016/j.promfg.2018.01.014 https://linkinghub.elsevier.com/retrieve/pii/S2351978918300143 https://linkinghub.elsevier.com/retrieve/pii/S2351978918300143

毕果 , 汤期林 , 王振忠 , 等 . 精密磨削机床智能监测系统开发与应用 [J ] . 航空制造技术 , 2019 , 62 ( 6 ): 32 - 40 .

BI G , TANG Q L , WANG Z Z , et al . Intelligent monitoring system of precision grinding machine [J ] . Aeronautical Manufacturing Technology , 2019 , 62 ( 6 ): 32 - 40 . (in Chinese)

MAHATA S , SHAKYA P , BABU N R . A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform [J ] . Precision Engineering , 2021 , 70 : 77 - 91 . DOI: 10.1016/j.precisioneng.2021.01.009 http://doi.org/10.1016/j.precisioneng.2021.01.009 https://linkinghub.elsevier.com/retrieve/pii/S014163592100012X https://linkinghub.elsevier.com/retrieve/pii/S014163592100012X

GUO W C , LI B Z , ZHOU Q Z . An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and long short-term memory network [J ] . Proceedings of the Institution of Mechanical Engineers , 2019 , 233 ( 13 ): 2436 - 2446 .

郭维诚 , 李蓓智 , 杨建国 , 等 . 磨削过程信号监测与砂轮磨损预测模型构建 [J ] . 上海交通大学学报 , 2019 , 53 ( 12 ): 1475 - 1481 .

GUO W C , LI B Z , YANG J G , et al . Monitroing of grinding signals and development of wheel wear prediction model [J ] . Journal of Shanghai Jiao Tong University , 2019 , 53 ( 12 ): 1475 - 1481 . (in Chinese)

CHENG C , LI J Y , LIU Y M , et al . An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters [J ] . Journal of Manufacturing Processes , 2020 , 50 : 80 - 89 . DOI: 10.1016/j.jmapro.2019.12.034 http://doi.org/10.1016/j.jmapro.2019.12.034 https://linkinghub.elsevier.com/retrieve/pii/S1526612519304529 https://linkinghub.elsevier.com/retrieve/pii/S1526612519304529

袁广超 , 鲍劲松 , 郑小虎 , 等 . 基于CNC实时监测数据驱动方法的钛合金高速铣削刀具寿命预测 [J ] . 中国机械工程 , 2018 , 29 ( 4 ): 457 - 462 ,470.

YUAN G C , BAO J S , ZHENG X H , et al . Tool life prediction in titanium high speed milling processes based on CNC real time monitoring data driven [J ] . China Mechanical Engineering , 2018 , 29 ( 4 ): 457 - 462 ,470. (in Chinese)

DURO J A , PADGET J A , BOWEN C R , et al . Multi-sensor data fusion framework for CNC machining monitoring [J ] . Mechanical Systems and Signal Processing , 2016 , 66 : 505 - 520 .

CHEN X Z , LI C , TANG Y,etal . A framework for energy monitoring of machining workshops based on IoT [J ] . Procedia CIRP , 2018 , 72 : 1386 - 1391 . DOI: 10.1016/j.procir.2018.03.085 http://doi.org/10.1016/j.procir.2018.03.085 https://linkinghub.elsevier.com/retrieve/pii/S2212827118301896 https://linkinghub.elsevier.com/retrieve/pii/S2212827118301896

陈鹏 , 刘飞 , 宁俊锋 . 机械加工自动生产线多目标监控集成模型及其应用 [J ] 计算机集成制造系统 , 2017 , 23 ( 3 ): 473 - 481 .

CHEN P , LIU F , NING J F . Integrated model for multi-objective monitoring on automatic machining line and its application [J ] . Computer Integrated Manufacturing Systems , 2017 , 23 ( 3 ): 473 - 481 . (in Chinese)

李恒 , 叶祖坤 , 查文彬 , 等 . 基于多传感器信息决策级融合的刀具磨损在线监测 [J ] . 兵工学报 , 2021 , 42 ( 9 ): 2024 - 2031 . DOI: 10.3969/j.issn.1000-1093.2021.09.023 http://doi.org/10.3969/j.issn.1000-1093.2021.09.023 针对无法精确掌控机械加工过程中刀具磨损状态的现状，提出一种基于多传感器信息决策级融合的刀具磨损在线动态监测模型。该模型对采集的振动、力、声发射传感器信号进行特征提取后，将特征按传感器类型划分为独立样本。划分后的独立样本分别对同一个刀具磨损量进行回归预测，进而对每一个独立样本预测得到的刀具磨损量进行加权综合决策，最终决策出刀具磨损量。实验结果表明：刀具磨损在线动态监测模型能够有效地提高刀具磨损动态预测精度，平均预测准确率可达97.9%；与现有研究方法相比，预测准确率至少提升4%以上，预测时间仅为0.016 s，具有较大优势。

LI H , YE Z K , ZHA W B , et al . Tool wear online monitoring based on multi-sensor information decision-making level fusion [J ] . Acta Armamentarii , 2021 , 42 ( 9 ): 2024 - 2031 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.09.023 http://doi.org/10.3969/j.issn.1000-1093.2021.09.023 An online dynamic monitoring model of tool wear based on multi-sensor information decision-making level fusion is proposed for accurately controlling the tool wear state in the machining process. After extracting the time, frequency and time-frequency features from the collected vibration, force and acoustic emission sensor signals, the monitoring model divides the sensor signal features into independent samples according to the sensor type. The same tool wear extent is regressively predicted using the independent samples, respectively. Then the tool wear extent predicted from the signal characteristics of each sensor is comprehensively determined. Finally, the tool wear extent is determined. The experimental results show that the on-line dynamic monitoring model of tool wear can effectively improve the accuracy of tool wear dynamic prediction, and the average prediction accuracy is 97.9%. Compared with existing research methods, the proposed method is used to increase the prediction accuracy rate by at least 4%, and the prediction time is only 0.016 s.

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ZHU H B . State monitoring system of CNC machine based on multi-sensor information fusion [D ] . Beijing : Beijing University of Posts and Telecommunications , 2019 . (in Chinese)

ZHANG Z Z , YAO P , WANG J , et al . Analytical modelling of surface roughness in precision grinding of particle reinforced metal matrix composites considering nanomechanical response of material [J ] . International Journal of Mechanical Sciences , 2019 , 157 - 158 : 243 - 253 .

吕黎曙 , 邓朝晖 , 刘涛 , 等 . 面向清洁生产的磨削工艺方案多层多目标优化模型及应用 [J ] . 中国机械工程 , 2022 , 33 ( 5 ): 589 - 599 .

LÜ L S , DENG Z H , LIU T , et al . Multi-layer and multi-objective optimization model and application of grinding process plan for cleaner production [J ] . China Mechanical Engineering , 2022 , 33 ( 5 ): 589 - 599 . (in Chinese)