Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars

This paper aims to introduce a computer-based estimation and compensation method for diametral errors in cantilever bar turning without additional hardware requirements. In the error estimation method, the error characteristics of workpieces are determined experimentally depending on cutting speed, depth of cut, feed rate, workpiece diameter, length from the chuck and the geometric error sum of CNC lathe. An Artificial Neural Network (ANN) model is trained using these experimental error characteristics for estimation of the error. The ANN model estimated the workpiece dimensional errors with a good accuracy. Error correction is realised via turning of workpieces with a CNC part program which modified based on the estimated error profile. The dimensional errors are reduced approximately by 90% with the proposed method.     

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al₂O₃ + TiN and Al₂O₃) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R², RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.     

Dynamic response of a workpiece in turning with continuously varying speed

In order to study the influence of workpiece speed variation on the dynamic response of a workpiece in turning operations, this paper develops a finite element model to describe the lateral vibration of a rotating Euler-beam, in which both axial force and damping effect are included and the workpiece speed changes continuously and periodically. A simplified cutting force model which depends on the feed-rate and workpiece deflection is used along with the proposed finite element model. To solve the obtained finite element equations of a workpiece in turning, the Newmark integration scheme is employed to further discretize the time domain. At each time step the resulting nonlinear equations are solved iteratively. The numerical results show the influence of the workpiece speed variation frequency and amplitude on the dynamic response of turning operations. Also, the suppression of self-excited vibration in turning by changing the workpiece speed periodically is demonstrated by using the present analytical model.     

Off-line compensation of the tool path deviations on robotic machining: Application to incremental sheet forming

In this paper, a coupling methodology is involved and improved to correct the tool path deviations induced by the compliance of industrial robots during an incremental sheet forming task. For that purpose, a robust and systematic method is first proposed to derive the elastic model of their structure and an efficient FE simulation of the process is then used to predict accurately the forming forces. Their values are then defined as the inputs of the proposed elastic model to calculate the robot TCP pose errors induced by the elastic deformations. This avoids thus a first step of measurement of the forces required to form a test part with a stiff machine. An intensive experimental investigation is performed by forming a classical frustum cone and a non-symmetrical twisted pyramid. It validates the robustness of both the FE analysis and the proposed elastic modeling allowing the final geometry of the formed parts to converge towards their nominal specifications in a context of prototyping applications.     

Control of tolerances in turning by predictive control with neural networks

Stochastic and non-deterministic influences have an effect on cutting processes and lead to an unsteady and dynamic process behaviour. Concepts for the improvement of process reliability and for the control of tolerances have to be developed in order to fulfil the increasing requirements on product quality. A concept for the improvement of manufacturing accuracy through artificial neural networks (ANN) will be presented as an example for the turning process. This ANN model makes it possible to predict the dimensional deviation caused by tool wear. Feeding this back in an open loop within the machine controller the deviation can be compensated by using an adaptive control of the depth of cut.     

Design of an optimal damper to minimize the vibration of machine tool structures subject to random excitation

The vibration of machine tools during machining adversely affects machining accuracy and tool life, and therefore must be minimized. The cutting forces for stable turning are generally known to be random, and hence excite all the resonance modes. Of all these modes, those that generate relative motions between a cutting tool and a workpiece are of concern.This paper presents a new approach for designing an optimal damper to minimize the relative vibration between the cutting tool and workpiece during stable machining. An approximate normal mode method is employed to calculate the response of a machine tool system with nonproportional damping subject to random excitation. The major advantage of this method is that it reduces the amount of computation greatly for higher-order systems when responses have to be calculated repeatedly in the process of optimization. An optimal design procedure is presented based on a representative lumped parameter model that can be constructed by using existing experimental or analytical techniques. The two-step optimization procedure based on the modified pattern search and univariate search effectively leads the numerical solution to the global minimun irrespectively of initial values even under the existence of many local minima.     

Estimation of cutting forces and surface roughness for hard turning using neural networks

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.     

On Clamping Planning in Workpiece-Fixture Systems

Deformations of contacts between the workpiece and locators/clamps resulting from large contact forces cause overall workpiece displacement, and affect the localization accuracy of the workpiece. An important characteristic of a workpiece-fixture system is that locators are passive elements and can only react to clamping forces and external loads, whereas clamps are active elements and apply a predetermined normal load to the surface of workpiece to prevent it from losing contact with the locators. Clamping forces play an important role in determining the final workpiece quality. This paper presents a general method for determining the optimal clamping forces including their magnitudes and positions. First, we derive a set of “compatibility” equations that describe the relationship between the displacement of the workpiece and the deformations at contacts. Further, we develop a locally elastic contact model to characterize the nonlinear coupling between the contact force and elastic deformation at the individual contact. We define the minimum norm of the elastic deformations at contacts as the objective function, then formulate the problem of determining the optimal clamping forces as a constrained nonlinear programming problem which guarantees that the fixturing of the workpiece is force closure. Using the exterior penalty function method, we transform the constrained nonlinear programming into an unconstrained nonlinear programming which is, in fact, the nonlinear least square. Consequently, the optimal magnitudes and positions of clamping forces are obtained by using the Levenberg–Marquardt method which is globally convergent. The proposed planning method of optimal clamping forces, which may also have an application to other passive, indeterminate problems such as power grasps in robotics, is illustrated with numerical example.      

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.     

Intelligent Fixture Design through a Hybrid System of Artificial Neural Network and Genetic Algorithm

In designing fixtures for machining operations, clamping scheme is a complex and highly nonlinear problem that entails the frictional contact between the workpiece and the clamps. Such parameters as contact area, state of contact, clamping force, wear and damage in the contact area and deformation of the component are of special interest. A viable fixture plan must include the optimum values of clamping forces. Along research efforts carried out in this area, this comprehensive problem in fixture design needs further investigation. In this study, a hybrid learning system that uses nonlinear finite element analysis (FEA) with a supportive combination of artificial neural network (ANN) and genetic algorithm (GA) is discussed. A frictional model of workpart–fixture system under cutting and clamping forces is solved through FEA. Training and querying an ANN takes advantage of the results of FEA. The ANN is required to recognize a pattern between the clamping forces and state of contact in the workpiece–fixture system and the workpiece maximum elastic deformation. Using the identified pattern, a GA-based program determines the optimum values for clamping forces that do not cause excessive deformation/stress in the component. The advantage of this work against similar studies is manifestation of exact state of contact between clamp elements and workpart. The results contribute to automation of fixture design task and computer aided process planning (CAPP).     

Effect of workpiece springback on micromilling forces

The machining forces present in micromilling with tools in the 50–100 m diameter range are dominated by contact pressure and friction between the tool cutting edges and the workpiece. A model of the micromilling process was developed based on the elastic contact between the tool and the workpiece along the side and bottom cutting edges of the tool. Micromilling experiments were conducted on 6061-T6 aluminum to obtain machining forces in the feed and cross-feed directions during slot milling and partial engagement end milling. Comparisons with the experimental data indicate reasonable agreement for full slot milling as well as end milling with radial depths of cut in the range of 2 m to 40 m. It was concluded that this model is adequate for predicting micromilling forces with the precision needed to reduce tool breakage and workpiece clamping forces and for predicting tool deflection that affects wall slope and feature size.This work was supported primarily by the Engineering Research Centers Program of the National Science Foundation under Award Number EEC-9986866. The Engineering Research Center for Wireless Integrated Microsystems is also hereby acknowledged. All machining was performed at the Micromechanical Applications and Processes Laboratory at Michigan Technological University.     

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.     

A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle

This paper presents a neuro-genetic approach proposed to suggest the process parameters for maintaining the desired depth of cut in abrasive waterjet (AWJ) cutting by considering the change in diameter of focusing nozzle, i.e. for adaptive control of AWJ cutting process. An artificial neural network (ANN) based model is developed for prediction of depth of cut by considering the diameter of focusing nozzle along with the controllable process parameters such as water pressure, abrasive flow rate, jet traverse rate. ANN model combined with genetic algorithm (GA), i.e. neuro-genetic approach, is proposed to suggest the process parameters. Further, the merits of the proposed approach is shown by comparing the results obtained with the proposed approach to the results obtained with fuzzy-genetic approach [P.S. Chakravarthy, N. Ramesh Babu, A hybrid approach for selection of optimal process parameters in abrasive water jet cutting, Proceedings of the Institution of Mechanical Engineers, Part B: J. Eng. Manuf. 214 (2000) 781–791]. Finally, the effectiveness of the proposed approach is assessed by conducting the experiments with the suggested process parameters and comparing them with the desired results.     

Optimizing feedforward artificial neural network architecture

Despite the fact that feedforward artificial neural networks (ANNs) have been a hot topic of research for many years there still are certain issues regarding the development of an ANN model, resulting in a lack of absolute guarantee that the model will perform well for the problem at hand. The multitude of different approaches that have been adopted in order to deal with this problem have investigated all aspects of the ANN modelling procedure, from training data collection and pre/post-processing to elaborate training schemes and algorithms. Increased attention is especially directed to proposing a systematic way to establish an appropriate architecture in contrast to the current common practice that calls for a repetitive trial-and-error process, which is time-consuming and produces uncertain results.This paper proposes such a methodology for determining the best architecture and is based on the use of a genetic algorithm (GA) and the development of novel criteria that quantify an ANN's performance (both training and generalization) as well as its complexity. This approach is implemented in software and tested based on experimental data capturing workpiece elastic deflection in turning. The intention is to present simultaneously the approach's theoretical background and its practical application in real-life engineering problems. Results show that the approach performs better than a human expert, at the same time offering many advantages in comparison to similar approaches found in literature.     

Mechanistic modelling of the milling process using an adaptive depth buffer

A mechanistic model of the milling process based on an adaptive and local depth buffer is presented. This mechanistic model is needed for speedy computations of the cutting forces when machining surfaces on multi-axis milling machines. By adaptively orienting the depth buffer to match the current tool axis, the need for an extended Z-buffer is eliminated. This allows the mechanistic model to be implemented using standard graphics libraries, and gains the substantial benefit of hardware acceleration. Secondly, this method allows the depth buffer to be sized to the tool as opposed to the workpiece, and thus improves the depth buffer size to accuracy ratio drastically. The method calculates tangential and radial milling forces dependent on the in-process volume of material removed as determined by the rendering engine depth buffer. The method incorporates the effects of both cutting and edge forces and accounts for cutter runout. The simulated forces were verified with experimental data and found to agree closely. The error bounds of this process are also determined.     

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).     

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.     

Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method

This paper presents a 3D simulation system which is employed in order to predict cutting forces and tool deflection during end-milling operation. In order to verify the accuracy of 3D simulation, results (cutting forces and tool deflection) were compared with those based on the theoretical relationships, in terms of agreement with experiments. The results obtained indicate that the simulation is capable of predicting the cutting forces and tool deflection.     

Prediction of surface roughness in CNC face milling using neural networks and Taguchi''s design of experiments

In this paper, a neural network modeling approach is presented for the prediction of surface roughness (R_a) in CNC face milling. The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method. The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force. Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 1.86% and to be consistent throughout the entire range of values.     

Surface roughness prediction in turning using artificial neural network

In this work, a back propagation neural network model has been developed for the prediction of surface roughness in turning operation. A large number of experiments were performed on mild steel work-pieces using high speed steel as the cutting tool. Process parametric conditions including speed, feed, depth of cut, and the measured parameters such as feed and the cutting forces are used as inputs to the neural network model. Roughness of the machined surface corresponding to these conditions is the output of the neural network. The convergence of the mean square error both in training and testing came out very well. The performance of the trained neural network has been tested with experimental data, and found to be in good agreement.