A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

The present work attempts to study the effect of important machining variables on performance characteristics such as material removal rate and tool wear in turning of Inconel 718 using chemical vapour deposition (CVD) coated tungsten carbide (WC) tool. A three dimensional machining model using Lagrangian approach has been developed using DEFORM 3D. The machining simulation is carried out to predict the flank wear and material removal rate (MRR). Flank wear is calculated using Usui’s wear model in the simulation model. The results from simulation model are compared with experimental data generated by the use of Taguchi’s L₁₆ orthogonal array for reducing the experimental runs. Analysis of variance (ANOVA) is performed to identify the most influencing variables for both the performance characteristics. It is found that simulation results are in good agreement with experimental results. A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.     

Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks

This study covers two main subjects: (i) The experimental and theoretical analysis: the cutting forces and indirectly cutting tool stresses, affecting the cutting tool life during machining in metal cutting, are one of very important parameters to be necessarily known to select the economical cutting conditions and to mount the workpiece on machine tools securely. In this paper, the cutting tool stresses (normal, shear and von Mises) in machining of nickel-based super alloy Inconel 718 have been investigated in respect of the variations in the cutting parameters (cutting speed, feed rate and depth of cut). The cutting forces were measured by a series of experimental measurements and the stress distributions on the cutting tool were analysed by means of the finite element method (FEM) using ANSYS software. ANSYS stress results showed that in point of the cutting tool wear, especially from von Mises stress distributions, the ceramic cutting insert may be possible worn at the distance equal to the depth of cut on the base cutting edge of the cutting tool. Thence, this wear mode will be almost such as the notch wear, and the flank wear on the base cutting edge and grooves in relief face. In terms of the cost of the process of machining, the cutting speed and the feed rate values must be chosen between 225 and 400 m/min, and 0.1 and 0.125 mm/rev, respectively. (ii) The mathematical modelling analysis: the use of artificial neural network (ANN) has been proposed to determine the cutting tool stresses in machining of Inconel 718 as analytic formulas based on working parameters. The best fitting set was obtained with ten neurons in the hidden-layer using back propagation algorithm. After training, it was found the R² values are closely 1.     

Monitoring the tool wear,surface roughness and chip formation occurrences using multiple sensors in turning

Tool wear, chip formation and surface roughness of workpiece under different cutting conditions have been investigated in machining using acoustic emission (AE) and vibration signature in turning. The investigation has shown that the AE and vibration components can effectively respond to the different occurrences in turning including tool wear and surface roughness. The AE has shown a very significant response to the tool wear progression whereas the resultant vibration (V) represented the surface roughness in turning. The vibration components V_x, V_y and V_z described the chip formation type and are found to have the most significant response to the change of feed, depth of cut and cutting speed respectively. The amplitude of vibration components, V_x, V_y and V_z increased with the increase of feed rate, depth of cut and cutting speed respectively. Even though the frequency of different signal components fluctuated at the different stages of tool wear and at different cutting conditions, the frequency of vibration components was always within a band of 98–40 kHz, and the AE has varied between 51 kHz and 620 kHz.     

Computer observer for in-process measurement of lathe tool wear

In the laboratory, tool wear is measured by direct visual observation of flank and crater wear dimensions using a tool maker's microscope. On the shop floor, the journeyman machinist uses chip appearance, sound, vibration, and surface finish to access tool condition. More precise information can be provided by between pass measurements of work piece dimensions. Although there is a body of research directed toward in-process measurement of tool wear, none has found practical application on the shop floor. This, despite the demands of unattended operation of machine tools in the automated factory. Two of the authors have developed a state space model of metal cutting on a lathe. Operation of that model was experimentally accessed using the Advanced Continuous Simulation Language (ACSL) [1]. The state space model embodied in the simulation is comprised of six state variables. In an actual lathe, four of those variables (spindle speed and torque, and cutting speed and force) would be directly measurable. The other two variables, flank and crater tool wear, would not be measurable. This paper describes a linear observer that reconstructs the two tool wear state variables based on system inputs and the four measurable state variables. In actual use the observer would be implemented using a microcomputer dedicated to the lathe. In this study, the previously developed ACSL model was substituted for the lathe, and the linear observer was incorporated as an extension to the simulation. Operation of the observer, including response to initial errors, is demonstrated.     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

Design of artificial neural networks for tool wear monitoring

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.     

Thermodynamic optimization of the PbO–FeO–Fe2O3–SiO2 system

Liquidus phase equilibrium data of the present authors for the PbO–FeO–Fe₂O₃ system at various oxygen potentials, PbO–“Fe₂O₃”–SiO₂ system in air, and PbO–“FeO”–SiO₂ in equilibrium with metallic Pb, combined with phase equilibrium and thermodynamic data from the literature, have been used to obtain a self-consistent set of parameters of the thermodynamic models for all phases in the PbO–FeO–Fe₂O₃–SiO₂ system. The modified quasichemical model is used for the liquid slag phase. For the liquid phase, the PbO–FeO, PbO–Fe₂O₃, PbO–FeO–Fe₂O₃, PbO–FeO–SiO₂ and PbO–Fe₂O₃–SiO₂ parameters are optimized in the present study. From these model parameters, the optimized ternary phase diagram is back calculated. Present set of parameters describe previous and new experimental data well, and can be used for predictions of the PbO–FeO–Fe₂O₃–SiO₂ phase equilibria over wide ranges of oxygen partial pressured, compositions and temperatures, as well as multicomponent systems.     

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.     

Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

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.     

Connectivity oriented fast Hough transform for tool wear monitoring

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.     

Thermodynamic optimization of the ZnO–FeO–Fe2O3–SiO2 system

Liquidus phase equilibria experimental data of the present authors for the ZnO–“Fe₂O₃”–SiO₂ system in air, ZnO–“FeO”–SiO₂ in equilibrium with metallic Fe, and ZnO–“FeO”–SiO₂–minor Cu₂O at p(O₂) ~10⁻⁷ … 10⁻⁸ atm (obtained as a part the research on the multicomponent PbO–ZnO–FeO–Fe₂O₃–Cu₂O–CaO–SiO₂ system), combined with phase equilibrium and thermodynamic data from the literature, have been used to obtain a self-consistent set of parameters of the thermodynamic models for all phases in the ZnO–FeO–Fe₂O₃–SiO₂ system for the whole range of compositions, p(O₂) between ~10⁻¹³ to 1 atm, and temperature range corresponding to liquid slag existence (1150–1700 °C). The modified quasichemical model is used for the liquid slag phase. From these optimized model parameters, the ternary phase diagrams are back calculated. The model based on the present set of parameters is in a good agreement with previous and new experimental data, and can be used for predictions of the ZnO–FeO–Fe₂O₃–SiO₂ phase equilibria over wide ranges of p(O₂), compositions and temperatures, as well as multicomponent systems.     

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。     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

An assessment of the system Fe-o-SiO2 using a structure based model for the liquid silicate

The non-ideal activity of FeO(1) in iron silicate and iron oxide melts is described by treating the liquid as ideal and as consisting of a few complexes. The calculations show that the melt in the system Fe-O-SiO₂ can be described by considering the formation of the complexes Fe₃Si₂O₇ and Fe₃Si₆O₁₅ in addition to the components FeO, FeO_1.5 and Fe₂SiO₄.Using this model for the liquid phase, phase equilibrium relations including partial pressures of oxygen and activities of FeO(1) have been calculated in the system Fe-O-SiO₂. The calculated results are consistent with available experimental and theoretical data.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.