Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization

Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses – MRR and ASR are still difficult. Advanced structural risk minimization based learning system – support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM – C, ɛ and σ are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination.     

Optimization of magnetic field assisted EDM using the continuous ACO algorithm

In this paper, a rotary tool with rotary magnetic field has been used to better flushing of the debris from the machining zone in electrical discharge machining (EDM) process. Two adaptive neuro-fuzzy inference system (ANFIS) models have been designed to correlate the EDM parameters to material removal rate (MRR) and surface roughness (SR) using the data generated based on experimental observations. Then continuous ant colony optimization (CACO) technique has been used to select the best process parameters for maximum MRR and specified SR. Here, the process parameters are magnetic field intensity, rotational speed and product of current and pulse on-time. Also, ANFIS models of MRR and SR are the objective and constraint functions for CACO, respectively. Experimental trials divided into three main regimes of low energy, the middle energy and the high energy. Results showed that the CACO technique which used the ANFIS models as objective and constrain functions can successfully optimize the input conditions of the magnetic field assisted rotary EDM process.     

Genetic synthesis of a fuzzy pulse discriminator in electrical discharge machining

In this paper, a sensor system, called a fuzzy pulse discriminator, is developed to classify various discharge pulses in electrical discharge machining (EDM). The fuzzy rules of the pulse discriminator are obtained based on the features of the gap voltage and gap current between the tool workpiece. The membership functions of the fuzzy pulse discriminator are automatically synthesized by using genetic algorithms. The effectiveness of this approach is verified under different cutting parameters.     

Computer aided contouring operation for traveling wire electric discharge machining (EDM)

This paper develops a computational method for numerical control (NC) of traveling wire electric discharge machining (EDM) operation from geometric representation of a desired cut profile in terms of its contours. Normalized arc length parameterization of the contour curves is used to represent the cut profile and a subdivision algorithm is developed together with kinematic analysis to generate the required motions of the machine tool axes. In generating the tool motions for cutting sections with high curvatures such as corners with small radii, a geometric path lifting method is presented that increases the machining gap and prevents gauging or wire breakage.     

On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS)

In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (R _a), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (G _a)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of R _a, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), G _a were taken as input parameters and R _a, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.     

A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process

Due to the controversy associated with modelling Electrical Discharge Machining (EDM) processes based on physical laws; this task is predominantly accomplished using empirical modelling methods. The modelling studies reported in the literature deal predominantly with quantitative parameters i.e. ones with numerical levels. In fact, modelling categorical parameters has been devoted a scant attention. This study reports the results of an EDM experiment conducted on the Ti–6Al–4V alloy. Its aim was to model the relationship between the Material Removal Rate (MRR) and the parameters of the process, namely, current, pulse on-time and pulse off-time along with a categorical factor (electrode material). The modelling process was accomplished using adaptive neuro-fuzzy inference system (ANFIS) and polynomial modelling approaches. In fact, one purpose of this study was to compare the performance of these modelling approaches as no study was found contrasting their prediction capability in the literature. Regarding the polynomial model, two numerical parameters (current and pulse on-time) were declared significant in the ANOVA together with the electrode material and its interaction with pulse on-time. Thus, they were all incorporated in the developed polynomial model. Furthermore, five ANFIS models with 6, 9, 19, 21 and 51 rules were developed utilizing the first order Sugeno fuzzy approach by back-propagation neural networks training algorithm. Of these, the ANFIS model with 21 rules was the best. This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values. The study sheds light on the powerful learning capability of ANFIS models and its superiority over the conventional polynomial models in terms of modelling complex non-linear machining processes.     

Artificial neural network models for the prediction of surface roughness in electrical discharge machining

In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab^® with associated toolboxes, as well as Netlab^®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining.     

Automation in the design of EDM electrodes

Plastic parts, especially those designed for consumer electronics products, are becoming more compact and freeform in shape. This requires the construction of compact and delicate geometric features in the injection moulds that produce these parts. It is now common practice to employ electric discharge machining (EDM) whenever the conventional machining process fails to machine such delicate features. Although CAD/CAM systems are widely used in mould design and manufacturing, and specific commercial CAD/CAM systems that are customized for injection mould applications are also available, there are still no intelligent CAD tools that address the specific requirements of EDM electrode design. The aim of this research is to develop an intelligent CAD tool that supports EDM electrode design. This CAD tool uses a new technique that recursively splits the EDM region into sub-regions until machinable electrodes can be constructed. The CAD tool has been implemented and integrated into a commercial CAD/CAM system, and 40 different real designs has been used to test its capability in handling a wide variety of geometric shapes. Performance data show that the efficiency of the design process can be improved by at least 50%, with a potential to achieve 85% through improved implementation and further research.     

Combination of electric discharge machining and laser ablation in microstructuring of hardened steels

Improving the efficiency of manufacturing processes becomes more and more important. This paper describes a new approach where two technologies are combined to manufacture wear-resistant mold inserts for powder injection molding. By combining laser ablation and electrical discharge machining (EDM) specific advantages of the respective technology can be utilized while drawbacks can partially be eliminated.In the following machining results of die-sinking EDM, EDM milling and laser ablation with structure sizes down to 20 μm are presented and a concept for a new type of electrode for EDM is introduced. Furthermore the combined machining center is presented.     

Traversing the machining graph of a pocket

A simple and linear-time algorithm is presented for solving the problem of traversing a machining graph with minimum retractions encountered in zigzag pocket machining and other applications. This algorithm finds a traversal of the machining graph of a general pocket P with N_h holes, such that the number of retractions in the traversal is no greater than OPT+N_h+N_r, where OPT is the (unknown) minimum number of retractions required by any algorithm and N_r is the number of reducible blocks in P (to be defined in the paper). When the step-over distance is small enough relative to the size of P, N_r becomes zero, and our result deviates from OPT by at most the number of holes in P, a significant improvement over the upper bound 5OPT+6N_h achieved [Proceedings of the Seventh ACM-SIAM Symposium on Discrete Algorithms, 1996; Algorithmica 2000 (26) 19]. In particular, if N_h is zero as well, i.e. when P has no holes, the proposed algorithm outputs an optimal solution. A novel computational modeling tool called block transition graph is introduced to formulate the traversal problem in a compact and concise form. Efficient algorithms are then presented for traversing this graph, which in turn gives rise to the major result.     

Three dimensional thermal finite element simulation of electro-discharge diamond surface grinding

The key to achieve good surface integrity in the workpiece due to Electro-Discharge Diamond Grinding (EDDG) process, which is hybrid of grinding and EDM, is by preventing the excessive temperature and thermal stress generated during the process. EDDG in surface grinding mode called Electro-Discharge Diamond Surface Grinding (EDDSG), used for finishing operation, is a complex machining process where several disciplines of science and engineering are involved in its theory. The complexity of the process includes the random occurrence of spark during EDM process and nonlinear behavior of workpiece material includes temperature dependent thermal properties. The present work involves the development of a simulation model to simulate the complex EDDSG process which consists of simulation of each constituent process namely EDM and surface grinding for temperature and thermal stress distribution. In order to simulate the realistic complex conditions, the three dimensional FEM is used in the process of development of the model accounting the random occurrence of the spark during EDM. The effect of different dielectric fluid, duty factor and energy partition during EDM on the temperature distribution and MRR study related to EDM contribution are reported. It is observed that the spark contributes primarily to the temperature. The predicted results can be used to determine the surface integrity of the machined surface.     

Adaptive control optimization of the EDM process using minicomputers

Electro-Discharge Machining (EDM) is an electro-physical machining process fulfilling a number of requirements making it suitable for the application of adaptive control. This paper describes the EDM machine with its adaptive control system, including a minicomputer, some sensors and control devices and their respective interfaces. Software, including some strategies implemented for searching optimal working conditions, are reported. Some preliminary results illustrate the overall performance of the adaptive control system.     

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.

     

A new approach to z-level contour machining of triangulated surface models using fillet endmills

Precision z-level contour machining is important for various computer-aided manufacturing (CAM) applications such as pocket machining and high-speed machining (HSM). This paper presents a new z-level contour tool-path generation algorithm for NC machining of triangulated surface models. Traditional approaches of z-level machining rely on the creation of accurate CL (cutter location) surfaces by surface offsetting or high-density z-map generation, which is computationally expensive and memory demanding. In contrast, this paper presents a novel approach to the generation of CL data directly from the section polygon of a triangulated surface model. For each polygon vertex of the contour, the offset direction is determined by the normal to the edge, while the offset distance is not fixed but is determined from the cutter shape and the part surface. An interference-free tool-path computation algorithm using fillet endmills is developed. Since there is no need to create a complete CL surface or high-density z-map grids, this proposed method is highly efficient and more flexible, and can be directly applied to triangulated surfaces either tessellated from CAD models, or reconstructed from 3D scanned data for reverse engineering (RE) applications.     

MONITOR AND CONTROL OF DISCHARGE ENERGY DURING EDMING

A pulse energy at 100MHz is verified to be able to determine the normal and abnormal discharge during electrical discharge machining (EDMing). A band pass filter is designed to detect the energy at this frequency, and can verify which discharge pulses are normal and abnormal, respectively. A field programmable gate array (FPGA) is used to implement the discharge detection circuit, including detection of the time of ignition (T_d) and discharge high‐energy. Function analysis is used to design the proportional (P) T_d controller to allow the bounded variation of reference input and the feedback signals. Energy of about 100MHz can be maintained within a specified pulse width by regulating the position of the electrode, including a slight regulation and jump motion. The rate of erosion during the roughing and finishing of a die‐sinking EDM is confirmed to demonstrate the improvements obtained by the EDMing.     

Microstructuring of silicon by electro-discharge machining (EDM) — part I: theory

Currently, nearly all microcomponents are fabricated by micro-electronic production technologies like etching, deposition and other (photo) lithographic techniques. In this way, main emphasis has been put on surface micromechanics. The major challenge for the future will be the development of real three-dimensional microstructures. The main objective of the proposed research is the development of a production technology for three-dimensional micromechanical structures together with a study of the mechanical properties of these structures. Electrodischarge machining (EDM) is a versatile technique which is very well suited for machining complex microstructures. This paper starts with an overview of EDM technology, the current state-of-the-art of micro EDM, and a comparison of EDM with other micromachining technologies. Afterwards, the basic parameters for EDM of silicon are derived. It will be demonstrated that EDM of silicon is not only feasible, but also forms an interesting, powerful and complementary alternative to traditional silicon micromachining.     

Tool path generation for clean-up machining by a curve-based approach

This paper presents a new efficient and robust tool-path generation method that employs a curve-based approach for clean-up machining. The clean-up machining discussed in this paper is pencil-cut and fillet-cut for a polyhedral model of the STL form with a ball-end mill. The pencil-cut and fillet-cut paths are obtained from the curve-based scanning tool paths on the xz, yz, and xy planes. The scanning tool path has exact sharp-concave points and bi-contact vectors, both of which are very useful to detect ‘pencil-points’, to trace the pencil-cut path, and to generate the fillet-cut path. In the paper, some illustrative examples are provided, and the characteristics of the proposed method are discussed.     

Conditions of proper sculptured surface machining

Multi-axis machining of sculptured surfaces is in wide use in Conventional and Rapid Prototyping. Topic of the paper is in the area of sculptured surface machining (SSM). The problem under consideration is: how to define and how to analytically describe a set of necessary and sufficient conditions of proper SSM. The similar problem is already solved with respect to machining of regular surface [Facta Univ, Mech Engng, Univ Nis? 1 (1998) 637]. Here this problem is enhanced up to the area of machining of sculptured surface on multi-axis NC machine. The general approach to solve the problem is developed. The input data to the approach presented later is a complete information of topology of a sculptured surface to be machined and of the machining surface of a form cutting tool to be applied. The set of six necessary and sufficient conditions of proper SSM is developed as a result. This set of analytically described conditions can be considered as a sort of restrictions on the geometrical and kinematical parameters of a machining operation. An application of the set of conditions stated in the paper is useful and of importance in developing software for machining of sculptured part surface on multi-axis NC machine. The methodology presented here is a general one. It combines the activities of exploration of topology of a given sculptured surface, of the machining surfaces of a form cutting tool, and of kinematics of relative motion of these surfaces in machining operation, thus making the production process for conventional and rapid prototyping flexible, automatic and controllable. The effectiveness of the proposed approach is verified from numerical simulation and numerous practical applications, whose implementation will not detailed here.     

A thermal model to investigate the wire rupture phenomenon for improving performance in EDM wire cutting

A continuous trend towards automation can be noticed in wire cutting electro discharge machining due to the relatively low machining speed and large investments required. In this respect, the wire rupture phenomenon may be considered a major problem, limiting the overall efficiency of the operation. The thermal load on the wire seems to be a governing factor determining wire rupture. This has been proven by a number of tests using an EDM pulse analyzing system. In order to examine the relative influence of various process parameters on the thermal load of the wire, a mathematical thermal model was developed. The model enables the calculation of the temperature rise in the wire due to heat input by electrical discharges and from Joule's heat. The result of this analysis can be used in an adaptive control optimization system in order to maximize the cutting speed, without enhancing the risk of wire rupture.     

Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.