Calibration-based tool condition monitoring for repetitive machining operations

Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

On line tool wear monitoring based on auto associative neural network

This paper presents a new tool wear monitoring method based on auto associative neural network. The main advantage of the model lies that it can be built only by the data under normal cutting condition. Therefore, the training samples of the tool wear status are no longer needed during the training process that makes it easier to be applied in real industrial environment than other neural network models. An averaged distance indicator is proposed to denote not only the occurrence of the tool wear but also its severity. Moreover, the Levenberg–Marquardt (LM) training algorithm is introduced to improve the convergence accuracy of the auto associative neural network. Based on the proposed method, a framework for online tool condition monitoring is illustrated and the cutting force data under different tool wear status are collected to simulate the online modeling and monitoring process for the rough and finish milling respectively. The results show that the proposed indicator can reflect the evolution process of tool wear correctly and the LM algorithm is more accurate in comparison with the gradient descent methods. Therefore, it casts new light on practical application of neural network in the field of on line tool condition monitoring.     

Knowledge-based diagnosis of drill conditions

One major bottleneck in the automation of the drilling process by robots in the aerospace industry is drill condition monitoring. This paper describes a system approach to solve this problem through the advancement of new machine design, sensor instrumentation, metal-cutting research, and intelligent software development. All drill failures can be detected and distinguished: chisel edge wear, flank wear, crater wear, margin wear, corner wear, breakage, asymmetry, lip height difference, and chipping at lips. However, in the real manufacturing environment, different workpiece materials, drill size, drill geometry, drill material, cutting speed, feed rate, etc. will change the criteria for judging the drill condition. The knowledge base used for diagnosing the drill failures requires a huge data bank and prior exhaustive testing. A self-learning scheme is therefore introduced to the machine in order to acquire the threshold history needed for automatic diagnosis by using the same new tool under the same drilling conditions.     

A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming

Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.     

An update method for digital twin multi-dimension models

Digital twin, as an effective means to realize the fusion between physical and virtual spaces, has attracted more and more attention in the past few years. Based on ultra-fidelity models, more accurate service, e.g. real-time monitoring and failure prediction, can be reached. Against the background, some scholars studied the related theories and methods on modeling to depict various features of physical objects. Some scholars studied how to use Internet of Things to realize the connections and interactions, thereby keeping the consistency between the virtual and physical spaces. During this process, a new question arises that how to update the models once digital twin models are inconsistent with the practical situations. To solve the problem, this paper proposed a general digital twin model update framework at first. Then, the update methods for multi-dimension models are further explored. The cutting tool is the core component of machine tools which are the key equipment in industry. The precise cutting tool models are essential for realizing the digitalization and servitization of machine tools. Therefore, this paper takes a cutting tool as the application object to discuss how to conduct physics model update based on the proposed framework and methods. Through model update, a more accurate and updated tool wear model could be obtained, which contributes to the prognostics and health management for machine tools.     

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers’ demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity.     

Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Development of decision support knowledge based system for tool wear diagnosis

This paper deals with the problems faced by small and medium sized metal cutting industries, with the perspective of tool monitoring. In a small or medium size metal cutting industry employing major metal cutting process, one of the primary problem is that of tool monitoring and wear diagnosis. The problem is of immediate concern especially in those industries where the processes or operations employed are flexible and production depends entirely on orders from customers. Due to a flexible manufacturing setup, almost all major metal cutting processers need to be carried out. However, it becomes increasingly difficult for such small or medium size metal cutting industries to employ skilled manpower for each operation as well as expert technicians to supervise the operation, and carry out fault diagnosis and tool monitoring. Also, the problem associated with tool monitoring is that human operator carrying out the monitoring has to rely either on observation such as ceasing of tool, rise in temperature, generation of fumes, noisy operation, vibrations, considerable change in shape etc, or by monitoring the quality of the finished product. Also, there can be instances where the operator does notice a symptom but does not have the expertise to identify the cause of the trouble. Errors in tool monitoring can lead to considerable damage both to the machine as well as the workpiece. On the other hand, if the tool is replaced before it reaches its useful life expectancy, it leads to unnecessary additional cost. A Decision Support Knowledge Based System (DSKBS) has therefore been developed in this paper with the above considerations. The DSKBS provides the user with a friendly environment to diagnose a particular tool wear and obtain the necessary repair or replacement instructions. The goal is to increase productivity, decrease cost of operation and enhance total quality and reliability of the operation.     

Selection of cutting conditions for power constrained parallel machine scheduling

Most previous studies on machining optimization focused on aspects related to machining efficiency and economics, without accounting for environmental considerations. Higher cutting speed is usually desirable to maximize machining productivity, but this requires a high power load peak. In Taiwan, electricity prices rise sharply if instantaneous power demand exceeds contract capacity. Many studies over the previous decades have examined production scheduling problems. However, most such studies focused on well-defined jobs with known processing times. In addition, traditional sequencing and scheduling models focus primarily on economic objectives and largely disregard environmental issues raised by production scheduling problems. This study investigates a parallel machine scheduling problem for a manufacturing system with a bounded power demand peak. The challenge is to simultaneously determine proper cutting conditions for various jobs and assign them to machines for processing under the condition that power consumption never exceed the electricity load limit. A two-stage heuristic approach is proposed to solve the parallel machine scheduling problem with the goal of minimizing makespan. The heuristic performance is tested by distributing 20 jobs over 3 machines with four possible cutting parameter settings.     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

Model referenced monitoring and diagnosis — application to the manufacturing system

This paper describes “model referenced monitoring and diagnosis” a systematic method of monitoring and diagnosis. In this method, a model is used either to generate standard values for the monitoring parameters, or to derive a systematic diagnostic algorithm. As examples of the model referenced monitoring in manufacturing systems, the cutting torque prediction system and tool breakage detection system are described. In the former example, the cutting torque values at each point in time, which can be used as reference values in monitoring the abnormal cutting condition, are evaluated based on a solid modelling system. In the latter, an autoregressive (AR) model is adaptively fitted to the cutting torque signal in order to detect any sudden change in the cutting state due to tool breakage. Two examples are also described for the case of model referenced diagnosis; the diagnosis of sequentially controlled machines using the state graph model, and diagnosis by means of a failure causality model. The former method is applicable to machines controlled by sequence control. Based on the state graph of the machine and the controller, the diagnostic programme can be generated in combination with the control programme. The failure causality model represents the propagation of the effects of failures in the machine. All possible combinations of failure causes are obtained by solving the simultaneous Boolean equations derived from the model.     

基于图像连通区域数的刀具磨损状态特征提取实验研究

为了利用计算机视觉技术进行刀具状态监测,设计了机械加工刀具状态监测实验系统,并通过将图像处理技术引入到机械加工刀具磨损状态监测中,提出了一种通过提取工件表面图像的连通区域数来判断刀具磨损状态的新方法。该方法首先采集被加工工件的表面图像;然后对图像进行预处理,并对区域行程算法进行了改进,再用改进的区域行程标记算法对机械加工工件表面图像进行标记;最后通过统计连通区域数来判断刀具的磨损状态。理论和实验分析表明,由于加工工件表面图像的连通区域数和刀具磨损有很强的相关性,其可以间接判断刀具磨损情况,从而可达到对刀具状态进行监测的目的。实验表明,该方法计算简单、识别速度快,可以有效地判断刀具的磨损状态。     

An interactive multi-objective artificial neural network approach for machine setup optimization

In this paper, we develop an artificial neural network method for machine setup problems. We show that our new approach solves a very challenging problem in the area of machining i.e. machine setup. A review of machine setup concepts and methods, along with feedforward artificial neural network is presented. We define the problem of machine setup to assessing the values of machine speed, feed and depth of cut (process inputs) for a particular objective such as minimize cost, maximize productivity or maximize surface finish. We use cutting temperature, cutting force, tool life, and surface roughness (process outputs) rather than objective functions to communicate with the decision maker. We show the relationship between process inputs to process outputs. This relationship is used in determining machine setup parameters (speed, feed, and depth of cut). Back propagation neural network is used as a decision support tool. The network maps, the forward relationship, and backward relationship between process inputs and process outputs. This mapping facilitates an interactive session with the decision maker. The process input is appropriately selected. Our method has the advantage of forecasting machine setup parameters with very little resource requirement in terms of time, machine tool, and people. Forecast time is almost instantaneous. Accuracy of the forecast depends on training and a well determined training sample provides very high accuracy. Trained network replaces the knowledge of an experienced worker, hence labor cost can be potentially reduced.     

生产现场数据分析与刀具耐用度模型的建立

选择适当的刀具材料和合理的加工条件，是双轴数控立式车床实现火车车轮经济高效加工的关键之一，首要问题是建立加工条件下的刀具耐用度模型。本文根据车轮生产现场实际情况，首先提出一种多次端面快速刀具耐用度试验方法，通过适当安排试验因子和利用最小二乘法原理，推导出一系列数据处理公式；然后利用生产现场采集与处理数据，建立了多种涂层硬质合金加工火车车轮的刀具耐用度模型。最后，根据试验建立的刀具耐用度模型，对几种进口的涂层硬质合金刀具和两种国产硬质合金刀具的切削性能进行了比较。试验结果证明该方法具有贴合实际、准确可靠、经济实用、不影响生产等特点，为合理选择刀具和优化加工用量提供了理论依据。     

一种曲线路径剪裁中刀具形变及其误差分析

动态移动切削阻力载荷对高速数控裁床加工过程中刀具形变及其剪裁误差具有的重要影响,提出了一种适用多层布料/皮革曲线剪裁路径的刀具形变及其误差计算方法;建立了动态负载条件下可伸缩刀具的挠度与转角方程,进而推导出高频振动裁刀剪裁误差及其随切削深度变化规律;计算结果表明,数控布料/皮革剪裁刀的动态载荷、高频振动参数、切削深度对剪裁误差具有重要影响,深入剖析高层数控裁床的加工机理,动态参数数据分析,对于提高机床加工效率,降低加工误差,提高刀具使用寿命具有一定的工程应用价值。     

Using evolutionary artificial neural networks in monitoring binary and polytomous logistic profiles

A statistical profile is a relationship between a quality characteristic (a response) and one or more explanatory variables to characterize quality of a process or a product. Monitoring profiles or checking the stability of profiles over time, has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable denoted by generalized linear models (GLM). Whereas, some of the potential applications of profile monitoring are cases where the response can be modelled using logistic profiles entailing binary, nominal and ordinal models. Also, most of existing control charts in this field have been developed by statistical approach and employing machine learning techniques have been rarely addressed in the related literature. Hence, to implement on-line process monitoring of logistic profiles, a novel artificial neural network (ANN) as a control chart with a heuristic training procedure is proposed in this paper. Performance of the proposed approach is investigated and compared using simulation studies in binary and polytomous models based on average run length (ARL) criterion. Simulation results revealed a good performance of the proposed approach. Nevertheless, to enhance the detection ability of the proposed approach more, the idea of combining run-rule which is a supplementary tool for making more sensitive control chart with final statistic is also implemented in this paper. Furthermore, a diagnostic method with machine learning schemes is employed to identify the shifted parameters in the profile. Results indicate the superior performance of the proposed approaches in most of the simulations. Finally, an example is used to illustrate the implementation of the proposed charting scheme.     

Distributed fault detection in industrial system based on sensor wireless network

In metal cutting processes, an effective monitoring system, based on a suitably developed scheme or set of algorithms can maintain machine tools in good condition and delay the occurrence of tool wear. In this paper, an approach is developed for fault detection based on a distributed system. Firstly, identifying of sensor instrumentation system is responsible for the signal processing and the system fault information. Secondly, the sensor wireless networks are used to transmit the data (lower layer) to or receive the commands from the computer center (top layer). Thirdly, the computer center at the top layer will monitor the overall system and generate the alarm signals or the commands when the faults occur.