Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM,ANN and SVM

In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.     

Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel

This paper presents the application of Taguchi method with logical fuzzy reasoning for multiple output optimization of high speed CNC turning of AISI P-20 tool steel using TiN coated tungsten carbide coatings. The machining parameters (cutting speed, feed rate, depth of cut, nose radius and cutting environment) are optimized with considerations of the multiple performance measures (surface roughness, tool life, cutting force and power consumption). Taguchi’s concepts of orthogonal arrays, signal to noise (S/N) ratio, ANOVA have been fuzzified to optimize the high speed CNC turning process parameters through a single comprehensive output measure (COM). The result analysis shows that cutting speed of 160 m/min, nose radius of 0.8 mm, feed of 0.1 mm/rev, depth of cut of 0.2 mm and the cryogenic environment are the most favorable cutting parameters for high speed CNC turning of AISI P-20 tool steel.     

Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel

In the present investigation, three different type of support vector machines (SVMs) tools such as least square SVM (LS-SVM), Spider SVM and SVM-KM and an artificial neural network (ANN) model were developed to estimate the surface roughness values of AISI 304 austenitic stainless steel in CNC turning operation. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. For this purpose, a three-level full factorial design of experiments (DOE) method was used to collect surface roughness values. A feedforward neural network based on backpropagation algorithm was a multilayered architecture made up of 15 hidden neurons placed between input and output layers. The prediction results showed that the all used SVMs results were better than ANN with high correlations between the prediction and experimentally measured values.     

Hybrid multiobjective optimization algorithm based on multivariate mean square error and fuzzy decision maker

This work presents the turning process of AISI H13 hardened steel with the PCBN 7025 tool, considering six output variables: tool life, machining total cost, surface roughness, machining force, sound pressure level, and specific cutting energy. Several problems are encountered in engineering processes that have adverse effects on the reliability of complex engineering systems. Hence, the aim of this work is to optimize the hardened steel turning process by applying mathematical methods to reduce dimensionality and eliminate the correlation between the multiple responses. The resultant latent response surfaces and their respective targets constitute the normalized multivariate mean square error (MMSE) function that is minimized by the normal boundary intersection (NBI) method. Furthermore, a fuzzy algorithm is applied to identify the best solution from several feasible solutions of the Pareto frontier that is compared with the performances of normalized normal constraint, arc homotopy length, global criterion method, and desirability method. The results show that NBI-MMSE has a higher performance than the other methods. In addition, NBI-MMSE is tested with benchmark functions to evaluate its effectiveness and robustness. Therefore, NBI-MMSE identifies the dynamics of the turning process of AISI H13 steel by revealing the optimal solutions for the input process parameters.     

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).     

Investigation and FEA-based simulation of tool wear geometry and metal oxide effect on cutting process variables

In machining, it is clearly noticed that the cutting tool wear influences the cutting process. However, it is difficult with experimental methods to study the effects of tool wear on several machining variables. Thus, in the literature, some earlier studies are performed separately on the effect of tool flank wear and crater wear on cutting process variables (such as cutting forces and temperature). Furthermore when the workpiece material adheres in cutting tool, it affects considerably the heat transfer phenomena. Accordingly, in this work the finite element analysis (FEA) is performed to investigate the influence of combination of tool flank and crater wear on the local or global variables such as cutting forces, tool temperature, chip formation on the one hand and the effects of the oxidized adhesion layer considered as oxide (Fe₂O₃/Fe₃O₄/FeO) on the heat transfer in cutting insert on the other hand. In this investigation, an uncoated cutting insert WC–6Co and medium carbon steel grade AISI 1045 are used. The factorial experimental design technique with three parameters (cutting speed Vc, flank wear land VB, crater wear depth KT) is used for the first investigation without adhesion layer. Then, only linear investigation is performed. The analysis has shown the influence of the different configurations of the tool wear geometry on the local or global cutting process variables, mainly on temperature and cutting. The simulation’s results show also, the highly influence of the oxidized adhesion layer (oxide Fe₂O₃/Fe₃O₄/FeO) on the heat transfer.     

Optimization and predictive modeling using S/N,RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18 Å, dielectric liquid pressure of 58.78 bar and electrode tool rotational speed of 100 rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

     

Multi-objective optimization and bio-inspired methods applied to machinability of stainless steel

Nowadays, due to the growing needs of market, the simultaneous optimization of various responses is configured as a necessary strategy in real process. Machinability of stainless steel has always been considered a difficult task and any movement toward optimization of this process are really worthy. Traditionally, the treatment of this problem is done through the application of the desirability function that consists in transforming the original multi-response problem in a similar with one objective. In spite of various applications involving this methodology, the quality of the solution obtained is dependent on the choice of the inferior and superior limits and on goals for each one of the responses. To overcome this disadvantage, the present work proposes a methodology to solve the original multi-objective problem by using the Bio-inspired Optimization Methods (BiOM). The strategy proposed consists in the extension of the BiOM to problems with multiple objectives, through the incorporation of two operators into the original algorithm: (i) the rank ordering, and (ii) the crowding distance. The proposed algorithm is applied to the machinability of stainless steel AISI (ABNT) 420 using a model that considers the tool life and cutting forces responses in terms of cutting speed, feed per tooth and axial depth of cut, in end milling process. The effects of these variables in the responses were investigated crossing information contained in response surfaces of material removal rate and cutting forces. The results obtained showed that the methodology used represents an interesting approach to the treatment of the optimization problem formulated.     

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.     

Fuzzy Petri nets with neural networks to model products qualityfrom a CNC-milling machining centre

This paper presents a Petri net approach for the modeling of a CNC-milling machining centre. Next, by utilizing fuzzy logic with Petri nets (fuzzy Petri nets), a technique based on 9 fuzzy rules is developed. This paper demonstrates how fuzzy input variables, fuzzy marking, fuzzy firing sequences, and a global output variable should be defined for use with fuzzy Petri nets. The technique employs two fuzzy input variables (spindle speed and feed rate), throughout the milling operation in order to determine surface roughness. Additionally, a fuzzy Petri net is used with an artificial neural network for the modeling and control of surface roughness. Experimental results illustrate that the technique developed can be of benefit when the cutting tool has suffered damage throughout the milling operation. It also shows how the technique can react when the quality is high, medium, or low. The surface roughness represents the quality specification of products from the CNC-milling machining centre     

Optimization of laser butt welding parameters based on the orthogonal array with fuzzy logic and desirability approach

This paper presents the study carried out on 3.5 kW cooled slab laser welding of 904 L super austenitic stainless steel. The joints had butts welded with different shielding gases like argon, helium and nitrogen at a constant flow rate. Super austenitic stainless steel (SASS) normally contains high amount of Mo, Cr, Ni, N and Mn. The mechanical properties are controlled to obtain good welded joints. The quality of the joint is evaluated by studying the features of weld bead geometry such as bead width (BW) and depth of penetration (DOP). In this paper, the tensile strength and bead profiles (BW and DOP) of laser welded butt joints made of AISI 904 L SASS are investigated. Taguchi approach is used as statistical design of experiment (DOE) technique for optimizing the selected welding parameters. Fuzzy logic and desirability approach are applied to optimize the input parameters considering multiple output variables simultaneously. Confirmation experiment has also been conducted for both the analyses to validate the optimized parameters.     

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.     

Cutting tool wear estimation for turning

The experimental investigation on cutting tool wear and a model for tool wear estimation is reported in this paper. The changes in the values of cutting forces, vibrations and acoustic emissions with cutting tool wear are recoded and analyzed. On the basis of experimental results a model is developed for tool wear estimation in turning operations using Adaptive Neuro fuzzy Inference system (ANFIS). Acoustic emission (Ring down count), vibrations (acceleration) and cutting forces along with time have been used to formulate model. This model is capable of estimating the wear rate of the cutting tool. The wear estimation results obtained by the model are compared with the practical results and are presented. The model performed quite satisfactory results with the actual and predicted tool wear values. The model can also be used for estimating tool wear on-line but the accuracy of the model depends upon the proper training and section of data points.     

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.     

Tool wear modeling in rotary turning modified by ultrasonic vibration

Modification of conventional turning operation is carried out by using different methods to improve machinability conditions. In this study, rotary turning is modified by adding ultrasonic vibrations to cutting tool. Accordingly, the effect of this method on output parameters namely, tool wear and temperature, cutting force, and surface roughness, is investigated. Having detailed analysis, finite element method is used beside the experiments. As a result, it was revealed that tool-chip engagement time during rotary motion of cutting tool significantly reduced wear propagation on tool faces. This was explained by heat analysis in which disengagement time resulted in lower heat transfer from chip to tool. Moreover, the result of surface roughness produced in vibratory-rotary turning was compared by rotary one.     

Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission

In modern manufacturing industry, developing automated tool condition monitoring system become more and more import in order to transform manufacturing systems from manually operated production machines to highly automated machining centres. This paper presents a nouvelle cutting tool wear assessment in high precision turning process using type-2 fuzzy uncertainty estimation on acoustic Emission. Without understanding the exact physics of the machining process, type-2 fuzzy logic system identifies acoustic emission signal during the process and its interval set of output assesses the uncertainty information in the signal. The experimental study shows that the development trend of uncertainty in acoustic emission signal corresponds to that of cutting tool wear. The estimation of uncertainties can be used for proving the conformance with specifications for products or auto-controlling of machine system, which has great meaning for continuously improvement in product quality, reliability and manufacturing efficiency in machining industry.     

A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process

The main focus of research in hard-milling domain has been the enhancement of tool life and the improvement in workpiece surface quality. This paper deals with the application of expert system technology in order to use the experimental data for optimization of milling parameters so as to achieve targets of enhancing tool life and improving workpiece surface finish. Hard-milling experiments were conducted to study the effects of workpiece material hardness, cutter’s helix angle, milling orientation and coolant upon tool life, workpiece surface roughness, and cutting forces. The experimental data were converted to useful information using ANOVA and numeric optimization, and this information was used to develop the knowledge-base in form of IF–THEN rules. Expert system utilized fuzzy logic for its reasoning mechanism, while, fuzzy data sets and crisp sets were freely mixed in antecedents and consequents of the rules. Effectiveness of the expert system was based upon two modules, namely optimization module and prediction module, with each of them operating upon different set of rules. Optimization module provides the optimal selection and combination of aforementioned milling parameters according to the desired objective, while the prediction module provides the prediction of performance measures for the combination of parameters finalized by the optimization module.     

Taguchi Method–GONNS integration: Complete procedure covering from experimental design to complex optimization

Determination of the optimal operating conditions from the experimental data without fitting any analytical or empirical models is very convenient for manufacturing applications. In this paper, integration of Taguchi Method and Genetically Optimized Neural Networks (GONNS) is proposed. The proposed procedure covers all the steps from experimental design to complex optimization. The feasibility of the approach was evaluated by estimating the optimal cutting conditions for the milling of Ti6Al4V titanium alloy with PVD coated inserts. The test conditions were determined by the Taguchi Method. The optimal cutting condition and influences of the cutting speed, feed rate and cutting depth on the surface roughness were analyzed with the same method. GONNS estimated that the optimal cutting conditions were very close to the Taguchi Method when the same criterion was used. GONNS was also capable to minimize or maximize one of the output parameters while the others were kept within the desired range. Study demonstrated that Taguchi Method and GONNS complement each other for creation of a robust procedure for determination of the test conditions, analysis of the quality of the collected data, estimation of the influence of each parameter on the output(s) and estimation of optimal conditions with complex optimization objective functions.     

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.     

Effect of tool vibrations on surface roughness during lathe dry turning process

Choice of optimized cutting parameters is very important to control the required surface quality. In fact, the difference between the real and theoretical surface roughness can be attributed to the influence of physical and dynamic phenomena such as: built-up edge, friction of cut surface against tool point and vibrations. The focus of this study is the collection and analysis of surface roughness and tool vibration data generated by lathe dry turning of mild carbon steel samples at different levels of speed, feed, depth of cut, tool nose radius, tool length and work piece length. A full factorial experimental design (288 experiments ) that allows to consider the three-level interactions between the independant variables has been conducted. Vibration analysis has revealed that the dynamic force, related to the chip-thickness variation acting on the tool, is related to the amplitude of tool vibration at resonance and to the variation of the tool's natural frequency while cutting. The analogy of the effect of cutting parameters between tool dynamic forces and surface roughness is also investigated. The results show that second order interactions between cutting speed and tool nose radius, along with third-order interaction between feed rate, cutting speed and depth of cut are the factors with the greatest influence on surface roughness and tool dynamic forces in this type of operation and parameter levels studied. The analysis of variance revealed that the best surface roughness condition is achieved at a low feed rate (less than 0.35 mnt/rev), a large tool nose radius (1.59 mm) and a high cutting speed (265 m/min and above). The results also show that the depth of cut has not a significant effect on surface roughness, except when operating within the built-up edge range. It is shown that a correlation between surface roughness and tool dynamic force exist only when operating in the built-up edge range. In these cases, built-u edge formation deteriorates surface roughness and increases dynamic forces acting on the tool. The effect of built-up edge formation on surface roughness can be minimized by increasing depth of cut and increasing tool vibration. Key words:design of experiments, lathe dry turning operation, full factorial design, surface roughness, measurements, cutting parameters, tool vibrations.