Non-traditional Manufacturing Process Research Articles

Mathematical and MATLAB based process optimization by simulated annealing algorithm for wire electrical discharge machining of EN31

The inevitability to machine hard materials and intricate shapes that are hard to be worked by traditional methods have stressed the need for non-traditional manufacturing processes specifically Wire Electro Discharge Machining (WEDM). WEDM encompasses intricate processes that include heating as well as cooling. The working parameters include pulse on time, pulse off time, table feed rate, flushing pressure, wire tension, wire velocity etc. has to be preferred suitably so that an optimized result can be attained. A single constraint has been chosen for the purpose of optimization to maximize the Metal Removal Rate (MRR). In this work a gear was machined out of EN31 material using the WEDM process and the process parameters such as pulse on time, peak current, wire feed rate and machining speed have been optimized using Simulated Annealing Algorithm and also using MATLAB based optimization. Simulated Annealing (SA) based optimization has been established to optimize the parameters in order to improve MRR. The objective function for metal removal rate is obtained based on the mathematical model.

Multi-cohort intelligence algorithm for solving advanced manufacturing process problems

In recent years, several nature-inspired optimization methods have been proposed and applied on various classes of problems. The applicability of the recently developed socio-inspired optimization method referred to as multi-cohort intelligence (Multi-CI) is validated by solving real-world problems from manufacturing processes domain, viz. non-traditional manufacturing processes. The problems are minimization of surface roughness for abrasive water jet machining (AWJM), electro-discharge machining (EDM), micro-turning and micro-milling processes. Furthermore, the taper angle for the AWJM, relative electrode wear rate for EDM, burr height and burr thickness for micro-drilling, flank wear for micro-turning process, machining time for micro-milling processes were minimized. It is important to mention that for the micro-drilling and micro-milling process different tool specifications were used. In addition, for EDM the material removal rate was maximized. The performance of the algorithm has been validated by comparing the results with other variations of CI algorithm and several contemporary algorithms such as firefly algorithm, genetic algorithm, simulated annealing and particle swarm optimization. In AWJM, Multi-CI achieved 5–8% and 8–23% minimization for surface roughness and taper angle, respectively. For EDM, 47–80% maximization of material removal rate; 2–13% and 92–98% minimization of surface roughness and relative electrode wear rate, respectively, have been attained. Furthermore, for micro-turning 2% minimization of flank wear and for micro-milling, 2–6% minimization of machining time were attained. For micro-drilling, 24% and 16–34% minimization of burr height and burr thickness were attained. In addition, the performance is compared with the regression and response surface methodology approaches and experimental solutions. The analysis regarding the convergence of all the algorithms is discussed in detail. The contributions in this paper have opened up several avenues for further applicability of the Multi-CI algorithm for solving real-world problems.

The Machinability of Steels by Abrasive Water Jet

Abrasive Water jet machining (AWJM) is a Non-Traditional manufacturing process, were the material is expelled from the workpiece by effect erosion of pressurized high-speed water stream mixed with high-speed abrasives. There are such a significant number of procedure parameters influence with nature of the machined surface can be done by AWJM. The process variables are, the cross speed, water driven weight, remain off separation, rough stream rate and kind of grating are significant. This measures in AWJM are Surface Roughness (SR, Rate of Material Removal (RMR), Kerf angle, cutting depth. At the past, the survive completed from different kinds of steel was inproved and investigated on AWJM research by identifying with execution estimates checking and procedure control and process factor advancement. In this paper, a study undergoes on AWJM about feature pattern regarding various types of steels.

Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process

Electrochemical machining is a promising non-traditional manufacturing process to make high-quality parts. The benefits of minimal thermally and mechanically induced stresses, free of burr, and a low surface roughness are appealing for industry and research institutes. However, the combined chemical reaction, electric field, fluid mechanics, and material properties involve a significant number of independent parameters which are difficult to analyze in order to draw comprehensive conclusions. To our current knowledge, process responses such as the material removal rate, optimal feed rate, and cutting profile cannot be represented accurately by analytical solutions. In recent years, deep learning has had tremendous success in analyzing sophisticated systems. The improved computation efficiency and reduced size of the training dataset required for deep learning have enabled various prediction models in the manufacturing industry. In this paper, a new approach is developed using the deep convolutional network with the Bayesian optimization algorithm to predict the diameters of the drilled hole from an electrochemical machining process. The Keras application programming interface (API) was used to build the deep convolutional network; the feed rate, pulse-on time, and voltage were used as input parameters to provide a fair comparison with a neural network from previous research. Random dropout layers were added to prevent overfitting of the network. Instead of tuning the network parameter by trial and error, the Bayesian parameter optimization algorithm was implemented to find the optimal set of parameters of the deep convolutional network that yields the minimum mean square error. The proposed algorithm was compared with a previously developed neural network with partially embedded physical knowledge. Improved training speed and accuracy were observed in comparison with the traditional neural network. The prediction model using the proposed deep learning algorithm demonstrated better prediction accuracy and provided a more systematic way to select the hyperparameter for the deep convolutional network.

Sustainable μECM machining process: indicators and assessment

Sustainability assessment of a manufacturing process is not an easy task and requires knowledge from inside of the process physics or chemistry as well as the overall process performance considering the effectiveness of the process and specific applications. Sustainability assessment is with increasing demand among the manufacturing companies. At present sustainability is considered only among the traditional manufacturing techniques and non-traditional processes do not receive enough attention in spite of the increasing demand for their use. Additionally micro and nano non-traditional manufacturing processes are nearly not considered in the studies for sustainability; and micro electrochemical machining (μECM) was not an exemption either. μECM is one of the promising non-conventional machining processes but its expensive structure, complex nature of the electrochemical reaction and process dependency on operator experiences has kept it back at research level. Securing a place for a new manufacturing process has to be done by proving its sustainability in comparison to the other existing processes. In this work, the aim is to establish a framework for assessment of the μECM sustainability based on five dimensions of the sustainability in order to justify its use and the initial investment cost. Indicators and measures for the effectiveness of the process are suggested as well as machining performance parameters are discussed. Routes for optimizing machining parameters is also explored. Finally the full picture sustainability assessment is generated.

Comparison of Fuzzy and Crisp Versions of an AHP and TOPSIS Model for Nontraditional Manufacturing Process Ranking Decision

Nontraditional manufacturing processes (NTMPs) are especially preferred when it is necessary to machine very small and delicate parts, obtain complex shapes or process very hard and high strength materials. New NTMPs are developed continually and the total number of NTMPs being used in the machining industry is increasing so that ranking and selection of the most proper NTMP requires multi-level and systematic models. [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] developed such an NTMP ranking model. The developed NTMP ranking model in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] had a two-level structure and used crisp Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together to rank feasible NTMPs. This study aims to replace crisp (nonfuzzy) versions of the AHP and TOPSIS with the fuzzy ones. Application of the fuzzy NTMP ranking model is illustrated and its results are compared with the ones obtained in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] to evaluate the significance of the differences in ranking results. The comparisons show that using fuzzy AHP and TOPSIS approaches instead of the crisp ones in the ranking model provided considerable ranking differences. The fuzzy NTMP ranking model is studied furthermore in the paper by updating the NTMP list and fine-tuning fuzzy weights of the pertinent attributes.

Experimental Investigations and Surface Morphology of Bio-Micromachining on Copper

Bio-micromachining is an emerging latest non-traditional manufacturing process and has various advantages. In this manufacturing process micro-organisms are used for material removal in desired shape and size. Acidithiobacillus ferrooxidans micro-organism are used as a tool for machining the copper workpiece and the effects of various parameters like pH, temperature, shaking speed, machining time are being calculated and there optimized value is being purposed. Vickers hardness test was also being performed and comparative study between bio-micromachining and photochemical machining is done in this paper.

Assessing the effects of different dielectrics on environmentally conscious powder-mixed EDM of difficult-to-machine material (WC-Co)

Electrical discharge machining (EDM) is a well-known nontraditional manufacturing process to machine the difficult-to-machine (DTM) materials which have unique hardness properties. Researchers have successfully performed hybridization to improve this process by incorporating powders into the EDM process known as powder-mixed EDM process. This process drastically improves process efficiency by increasing material removal rate, micro-hardness, as well as reducing the tool wear rate and surface roughness. EDM also has some input parameters, including pulse-on time, dielectric levels and its type, current setting, flushing pressure, and so on, which have a significant effect on EDM performance. However, despite their positive influence, investigating the effects of these parameters on environmental conditions is necessary. Most studies demonstrate the use of kerosene oil as dielectric fluid. Nevertheless, in this work, the authors highlight the findings with respect to three different dielectric fluids, including kerosene oil, EDM oil, and distilled water using one-variable-at-a-time approach for machining as well as environmental aspects. The hazard and operability analysis is employed to identify the inherent safety factors associated with powder-mixed EDM of WC-Co.

Development and Comparison of Mathematical Model for EN19 Material Using Statistical Analysis Software on WEDM

Wire cut EDM (WEDM) is a thermo electric nontraditional manufacturing process in which material is removed by localized heating and melting. It is discrete spark generation method applicable for hard and difficult to machine materials. In this paper mathematical model developed for EN19 material with Stastical analysis software. Experiments are carried out using L27 Orthogonal array by varying Material Thickness, pulse on time, Pulse Off time, Flushing Pressure, Wire Tension and Servo voltage. Analysis found that varying parameters are affected for consumable wire with constant wire feed in different way for different response. Attempt to compare different order mathematical model for accurate modeling done in this research. Higher order mathematical model developed with R 2 value 0.9863 for MRR and 0.9918 for Surface roughness obtained which gives more accurate output for given input parameters. Keywords-WEDM, PULSE ON TIME, PULSE OFF TIME , MRR, REGRESSION I. INTRODUCTION WEDM is a non conventional thermo electric material removal method for conductive materials to cut intricate shapes and profiles with a thin wire electrode. The electrode is a thin wire of a diameter 0.05 to 0.25 mm copper or brass coated with mo lybdenum. As wire feeds fro m reel to reel, material is eroded fro m work material by a series of discrete sparks occurring between the work p iece and the wire under the presence of dielectric fluid which is continuously fed to the mach ining zone (1). The WEDM process makes use of electrical energy generating a channel of plasma between the cathode and anode (2) and turns it into thermal energy at a temperature in the range of 8000-12,000 oC (3). When the pulsating direct current power supply occurring between 20,000 and 30,000 Hz is turned off, the plasma channel breaks down. This causes a sudden reduction in temperature allowing circulat ing dielectric fluid to implore plas ma channel and flush mo lten particles fro m the pole surfaces in fo rm of microscopic debris (4).Erosion of metals by spark was first reported by Joseph Priesily in 1978, however controlled machining by sparks was first introduced by Lazarenko in Russia in 1944. The first Britis h patent was granted to Rudorff in 1950 (5). In 1974 D.H. Dulebohn applied optical-line follower system to auto matically control shape of component to be machined by W EDM proc ess. By 1975, its popularity was rapidly increasing, as the process and its capabilities were better understood by the industry. II. MATHEMATICAL MODELING Regression analysis is used to investigate and model the relat ionship between a response variable and one or more predictors. The term mu ltip le regression literally means stepping back toward the average. It was used by British mathematician Sir Francis Galton. Regression analysis is a mathematical measure of the average relationship between two or more variables in terms of the orig inal units of the data. In regression analysis there are two types of variables. The value whose value is influenced or is to be predicted is called dependent variable and the variable which influences the values or is to be used for prediction is called independent variable. Regression analysis can be done in two ways; A. Bivariate regression B. Multi ple regression a) Bivariate regression Two variables X and Y may be related to each other or inexactly. In physical sciences, variab les frequently have an exact relationship to each other. The simp lest relationship can be expressed by Y=a+Bx Where the values of the coefficient, a and b, determine respectively the p recise height and steepness of the line. Thus coefficient a represent to as the intercept or constant, and coefficient b referred to as the slope. In contrast, relationship between variables in social sciences is almost always inexact. The equation for a linear relationship between two social science variables would be written as: Y=a+bX+e , Where e represents the presence of error. b) Multiple regression analysis Multiple regression analysis is use when more than two parameters are used. In this research work, six control parameters were used. For mu ltip le reg ression analysis various types of modeling tool used as shown in fig

Modeling of ECDM micro-drilling process using GA- and PSO-trained radial basis function neural network

Electrochemical discharge machining (ECDM) is a non-traditional manufacturing process potentially used to machine electrically non-conductive materials, such as ceramics and glass. The present paper explains the modeling of multi-input---multi-output ECDM micro-drilling of silicon nitride ceramics using radial basis function neural network (RBFNN). To establish the model, the process parameters such as applied voltage, electrolyte concentration and inter-electrode gap are treated as inputs and the important machining criteria namely material removal rate, radial overcut and heat affected zone are considered as outputs. A batch mode of training has been implemented to tune the developed RBFNN by utilizing a genetic algorithm (GA) and particle swarm optimization (PSO) methods, separately. Once, the optimal RBFNN is obtained, the performances of GA-trained RfBFNN (GA-RBFNN) and PSO-trained RBFNN (PSO-RBFNN) are compared with the help of experimental test cases. It has been observed that PSO-RBFNN is found to perform marginally better than GA-RBFNN.

Special Issue on Non-Traditional Manufacturing Processes Study at Micro- and Nano-Scales

Micro- and nano-fabrication processes are increasingly important in modern technology and economy. Basic fabrication methods include additive, subtractive and deformation-based processes. In recent years, novel fabrication processes such as laser micro/nano machining, 3D printing of miniature features, etc., have undergone significant growth and have reduced the manufacturing cost and enabled new designs for emerging markets such as smart phones, photovoltaics (PVs), and advanced batteries.This special issue focuses on the established and emerging nontraditional manufacturing technologies with various energy sources (mechanical, thermal, optical, etc.) at micro- and nano-scales. It consists of several invited papers and papers presented at the symposium on micro-/nano-scale fabrication processes at the manufacturing science and engineering conference, held on June 9–13, 2014 at the University of Michigan, organized by the Manufacturing Engineering Division of the American Society of Mechanical Engineers (ASME). The included papers consist of a variety of non-traditional manufacturing processes at small scales, ranging from novel process development and innovative simulation method, to broad application examples for different industries.Among different micro- and nano-processes, laser manufacturing brings significant attention from both academia and industry with its special capability of creating small features in a fast production rate. One contribution discusses the different capabilities of two processes to produce microchannels in stainless steel using laser induced plasma micromachining and ultrashort pulsed laser ablation processes; and then the superior capability of the former process for machining different materials is addressed in another paper in this special issue. The optimum scribing conditions of a picosecond laser machining process are determined thin-film solar cells and the potential processing speed is increased. One comprehensive comparison of micro-scale rapid prototyping techniques is presented for three different processes (laser ablation, micromilling, and 3D printing) to fabricate a sample part with features representative of microfluidic devices. In simulation method, a model of the plasmonic lithography process is developed, which is another promising way for nano-fabrication and can produce feature size with 22 nm resolution. On the application side, several contributions covering very diverse fields are also part of the special issue. Examples include synthesis of CdS nanoparticle using a reverse oscillatory flow method, laser diagnostics of plasma in synthesis of grapheme-based materials, and fabricating a hemodialysis device. Finally, a novel approach to “3D print” polymer nanocomposite structures brings us to the additive manufacturing world at nanoscales. It also has great potential for low cost, large scale fabrication with its inkjet printing and reel-to-reel fiber alignment techniques.There are still challenges ahead as well as application domains where such methods are yet to have an impact. This special issue is published with the hope to provide the readers with a small window to discern the recent trends and the capability of different micro- and nano-manufacturing processes and encourage expansion in new application areas. The Editors of this special issue would like to thank all the authors for the effort in preparing their contributions and to thank all the reviewers for their help in reviewing these papers. We would also like to thank Associate Editor Nicholas Fang who contributed his Editorial support for some of the papers submitted in this issue. Last but not least, we would like to thank the Journal of Micro- and Nano-Manufacturing Editorial Office and the Production Coordinator at the ASME for their encouragement and help during the course of the special issue publication.

A novel friction stir welding robotic platform: welding polymeric materials

The relevance, importance and presence of industrial robots in manufacturing have increased over the years, with applications in diverse new and nontraditional manufacturing processes. This paper presents the complete concept and design of a novel friction stir welding (FSW) robotic platform for welding polymeric materials. It was conceived to have a number of advantages over common FSW machines: it is more flexible, cheaper, easier and faster to setup and easier to programme. The platform is composed by three major groups of hardware: a robotic manipulator, a FSW tool and a system that links the manipulator wrist to the FSW tool (support of the FSW tool). This system is also responsible for supporting a force/torque (F/T) sensor and a servo motor that transmits motion to the tool. During the process, a hybrid force/motion control system adjusts the robot trajectories to keep a given contact force between the tool and the welding surface. The platform is tested and optimized in the process of welding acrylonitrile butadiene styrene (ABS) plates. Experimental tests proved the versatility and validity of the proposed solution.

Preface for the special issue on robotics in smart manufacturing

Today, perhaps more than ever, manufacturing industry is seen by society as a major means of generating economic growth and jobs creation. In the current context of the global market, manufacturing companies need to be competitive to survive, i.e., produce more, better, cheaper, and customer-oriented. At the same time, these companies, usually smalland mediumsized enterprises (SMEs), have to be flexible to react to market demands in appropriate time. Industrial robots are key elements in flexible manufacturing as they can be programmed and re-programmed to perform different tasks in different environments. However, it is not easy to interact with robots and integrate them in manufacturing cells. Robot programming is still a non-intuitive task that requires technical skills in robot programming and in the manufacturing process being applied. Nevertheless, industrial robots are increasingly being used in diverse traditional and non-traditional manufacturing processes. In fact, recent advances in human-robot interaction, robot autonomy, humanrobot co-working, and robot-robot interaction have led to the emergence of more and more robots operating in autonomous and smart manufacturing systems. These are not created by decree or with the purpose of recreating science fiction. On the contrary, they emerged due to the global market demands for cheaper, diversified, and better-quality products. This special issue of The International Journal of Advanced Manufacturing Technology (IJAMT) contains revised and extended versions of selected papers presented at the Workshop on Robotics in Smart Manufacturing (WRSM) organized under the umbrella of the 23rd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2013, which was held in Porto, Portugal. The workshop attracted 29 paper submissions, and 20 papers were accepted for presentation (69 % acceptance rate). Contributions were mainly received not only from European countries but also from the People’s Republic of China, Canada, Algeria, and Brazil. In summary, the WRSM was an international forum that brings together practitioners and researchers to present recent developments and identify possible directions for further research in the field. The workshop included presentations on diverse issues related to the interdisciplinary field of industrial robotics, all of them describing the application of robots in manufacturing processes. This special issue contains extended versions of the seven papers with the highest reviewer ratings. All papers present both theoretical and applicationoriented contributions. In addition, each paper is complemented with a demonstration video showing the achieved results. Some papers report tangible results arising from flagship European research projects. The selected papers address the following subjects:

Model of Pulse Power Generator in Electrical Discharge Machining (EDM) System

Electrical discharge machining (EDM) is a non-traditional manufacturing process of removing material. EDM system consists of a shaped tool and the work piece, which are connected to a power supply and placed in a dielectric fluid. EDM pulse power generator applies voltage pulses between the electrode and workpiece to generate sparks through the gap. The essential requirement for EDM system is to obtain the output waveform similar to the ideal EDM waveform. The design is based on transistor type pulse power generator circuit. This paper discuss in modeling of current and voltage gap waveform for EDM system.

Role of multi-wall carbon nanotubes on the main parameters of the electrical discharge machining (EDM) process

Electrical discharge machining is a very accurate non-traditional manufacturing process for creating tiny apertures, complex shapes and geometries within mechanical parts and assemblies. Its performance is evaluated in terms of surface roughness, existence of cracks, voids, and recast layer on the surface of product. The high heat generated on the electrically discharged material during the electrical discharge machining (EDM) process unfortunately decreases the quality of product. In this paper, the high strength and unique electrical and thermal properties of multi-wall carbon nanotubes are used to improve the EDM performance when machining the AISI H13 tool steel, by means of copper electrodes. Material removal rate, electrode wear rate, surface roughness, and recast layer were measured in presence of carbon nanotubes in the dielectric, then compared to the outcome of traditional EDM. Experiments show that mixing multi-wall carbon nanotubes within the dielectric makes the EDM more efficient, particularly if machining parameters are set at low pulse of energy.

Abrasive water jet review and parameter selection by AHP method.

An Abrasive Water Jet is one of the most recently developed non-traditional manufacturing processes. Abrasive water jet offers the potential for the development in cutting which is less sensitive to material properties, has virtually no thermal effects, and imposes minimal stresses. As it is a cold process has also important applications where heat-affected zones are to be avoided. In this work, a deep study of this newer non-conventional technique of machining i.e., abrasive water jet machining is done. In this paper more focus is on selection of various process parameters like-angle of impact, Pressure inside the pumping system, abrasive material type, Stand-off distance, focusing tube diameter, nozzle speed, abrasive mass flow rate and target material properties for getting the required output like- depth of cut and cut quality. For cutting Stainless steel (Grade 304), important task is to find out a few parameters which influence more. With the help of Analytical Hierarchy Process technique the selection of a few parameters are done which are comparatively more influencing. Weighteges are given to parameters on the basis of previous study and experience of owner of the company who is dealing with abrasive water jet technology since many years. Keyword-AHP Technique, process parameter, Quality of cut, Weight ages. I. Inroduction Cutting of stainless steel plates without losing its metallurgical properties for the purpose of making fixtures part was the problem. Literature review provided solution and abrasive waterjet technique suited best for the given practical problem. Abrasive waterjet (AWJ) machining technology has been found to be one of the most recent developed advanced non-traditional methods used in industry for material processing with the distinct advantages of no thermal distortion, high machining versatility, high flexibility and small cutting forces. Various machining technologies of high-speed cutting by using of liquid jets can be included in this category. High speed of the out flowing stream of water containing abrasives makes it possible to machine many industrial materials such as paper, paperboard, wood, plastic, building materials, rocks and also metals and their alloys (2).Experimental study of the surface quality produced by abrasive waterjet (AWJ) on metallic materials and measurement of surface roughness/waviness was quantitatively evaluated by using the contactless optical measurement is done by the researcher (2). The waterjet is a versatile tool and can be used in applications such as cutting, drilling, and milling, cleaning, peening, forming and coating removal. It can machine almost any material and competes with other technologies since there is minimal force, no heat damage and it is an environmentally friendly process. The drive for flexibility, speed and harder to process materials has meant that the uptake of waterjet technology in recent years has risen significantly. The process can be used with or without the addition of abrasive media and new applications are being continuously found (1).Researcher reported effect of abrasive material type on surface cut quality (4). Abrasive waterjet cutting has been proven to be an effective technology for processing various engineering materials. Surface roughness of machined parts is one of the major machining characteristics that play an important role in determining the quality of engineering components (5). In this paper aim is to find important process parameter which will be use for the further experimentation. Literature review provided consequences among various process parameters which affects the cutting quality and this is solved with the help of AHP technique. Three topographical components-waviness, roughness and error of form-compose a machined parts surface texture .the irregular nature of a surface texture arises from several parameters (3).

Multi-attribute decision making for green electrical discharge machining

This paper aims to develop a combination of Taguchi and fuzzy TOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Electrical Discharge Machining (EDM), a commonly used non-traditional manufacturing process was considered in this study. A decision making model for the selection of process parameters in order to achieve green EDM was developed. An experimental investigation was carried out based on Taguchi L9 orthogonal array to analyze the sensitivity of green manufacturing attributes to the variations in process parameters such as peak current, pulse duration, dielectric level and flushing pressure. Weighing factors for the output responses were determined using triangular fuzzy numbers and the most desirable factor level combinations were selected based on TOPSIS technique. The model developed in this study can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes.

A new electrode wear compensation method for improving performance in 3D micro EDM milling

As one of the non-traditional manufacturing processes, micro electrical discharge machining (EDM) has been widely applied for manufacturing precise and complex microstructures. However, a number of issues remain to be studied before micro EDM can become a reliable processing method. Efforts to get higher machining quality and to improve efficiency have been carried out. The electrode wear compensation method is one of the key factors in micro EDM milling. This paper proposes a new electrode wear compensation method, the combination of the linear compensation method (LCM) and the uniform wear method (UWM), called the CLU method. This approach combines LCM, UWM and the theoretical model. Experimental results indicate that machining performance such as material removal rate, electrode wear ratio and surface roughness using the proposed method has been improved compared to that by the uniform wear method.

Investigation of the effect of process parameters on the formation and characteristics of recast layer in wire-EDM of Inconel 718

Inconel 718 is a high nickel content superalloy possessing high strength at elevated temperatures and resistance to oxidation and corrosion. The non-traditional manufacturing process of wire-electrical discharge machining (EDM) possesses many advantages over traditional machining during the manufacture of Inconel 718 parts. However, certain detrimental effects are also present and are due in large part to the formation of the recast layer. An experimental investigation was conducted to determine the main EDM parameters which contribute to recast layer formation in Inconel 718. It was found that average recast layer thickness increased primarily with energy per spark, peak discharge current, and current pulse duration. Over the range of parameters tested, the recast layer was observed to be between 5 and 9 μm in average thickness, although highly variable in nature. The recast material was found to possess in-plane tensile residual stresses, as well as lower hardness and elastic modulus than the bulk material.

NTM-01: Finite Element Study of Effects of a Non-uniform Initial Density Distribution on Powder Forging Process(NTM-I: NON TRADITIONAL MANUFACTURING PROCESS)

NTM-01: Finite Element Study of Effects of a Non-uniform Initial Density Distribution on Powder Forging Process(NTM-I: NON TRADITIONAL MANUFACTURING PROCESS)