Review of improvements in wire electrode properties for longer working time and utilization in wire EDM machining

Wire electrical discharge machining (WEDM) is an important technology, which demands high-speed cutting and high-precision machining to realize productivity and improved accuracy for manufacturing hard materials. WEDM has experienced explosive growth and complexity of equipment as well as rising demand for the basic process tool (the wire electrode). Greater taper angles, thicker workpieces, automatic wire threading, and long periods of unattended operation make the selection of the idealwire a much more critical basis for achieving successful operation. This paper focuses on the evolution of EDM wire electrode technologies from using copper to the widely employed brass wire electrodes and from brass wire electrodes to the latest coated wire electrodes. Wire electrodes have been developed to help user demand and needs through maximum productivity and quantity by choosing the best wire. In the final part of the paper, the possible trends for future WEDM electrode research are discussed.

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Surface Roughness Prediction in End-Milling Process

Surface roughness prediction for the end-milling process, which is one of the major cutting processes, is a very important economical consideration in order to increase machine operation and decrease production cost in an automated manufacturing environment. In this study; prediction of surface roughness (Ra) for Brass (60/40) material based on cutting parameters: cutting speed, feed rate, and depth of cut; was studied. 

Adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in the end milling process. Surface roughness was used as dependant variable while cutting speed of range (750 - 1750rpm), feed rate of range (50 - 250mm/min) and depth of cut of range (0.3 - 0.7mm) were used as predictor variables. Normal and feed forces were used as predictor variables to verify the ANFIS model. Different membership functions were adopted during the training process of ANFIS.The effects of cutting parameters on the normal force, feed force and surface roughness were discussed. Experimental test data were used to examine the ANFIS model by defining the reliability and percentage error of the model. Experimental results demonstrate the effectiveness of the proposed model. While the predicted surface roughness was compared with measured data; the mean square error has been found equal to 8.5 % hence the achieved accuracy is equal to 91.5 %. Although this work focuses on prediction of surface roughness for endmilling operation, the concepts introduced are general; ie., prediction of surface roughness using ANFIS can be applied to many other cutting and machining processes.

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Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

Wire-cut electric discharge machining is a nontraditional technique by which the required profile is acquired using sparks energy. Concerning Wire-cut electric discharge machining, high cutting rates and precision machining is necessary to improve productivity and achieve high quality of machined workpieces. In this research work, an experimental investigation was introduced to achieve higher productivity of the wire electrode associated with sustainable production in terms of product quality and less heat-affected zone. For this purpose, the effects of machining parameters including peak current, pulse on time and wire preloading were investigated using adaptive neuro-fuzzy inference system along with the Taguchi method. From this study, the optimal setting of machining parameters to achieve higher productivity and sustainability was identified. Moreover, Neuro-fuzzy modelling was successfully used to build an empirical model for the selection of machining parameters to achieve higher productivity at highest possible surface quality and minimum cost for sustainable production.

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Improve wire EDM performance at different machining parameters – ANFIS modeling

This study presents an experimental investigation of wire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.

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Investigating the Machinability of Al–Si–Cu cast alloy containing bismuth and antimony using coated carbide insert

Surface roughness and cutting force are two key measures that describe machined surface integrity and power requirement evaluation, respectively. This investigation presents the effect of melt treatment with addition of bismuth and antimony on machinability when turning Al–11%Si–2%Cu alloy. The experiments are carried out under oblique dry cutting conditions using a PVD TIN-coated insert at three cutting speeds of 70, 130 and 250 m/min, feed rates of 0.05, 0.1, 0.15 mm/rev, and 0.05 mm constant depth of cut. It was found that the Bi-containing workpiece possess the best surface roughness value and lowest cutting force due to formation of pure Bi which plays an important role as a lubricant in turning process, while Sb-containing workpiece produced the highest cutting force and highest surface roughness value. Additionally, change of silicon morphology from flake-like to lamellar structure changed value of cutting force and surface roughness during turning.

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Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity.

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Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling

Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical control (CNC) end milling. Spindle speed, feed rate, and depth of cut were the predictor variables. Experimental validation runs were conducted to validate the ANFIS model. The predicted surface roughness was compared with measured data, and the maximum prediction error for surface roughness was 6.25 %, while the average prediction error was 2.75 %.

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Surface roughness prediction in end milling using multiple regression and adaptive neuro-fuzzy inference system

Multiple regression and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the surface roughness in the end milling process. Spindle speed, feed rate and depth of cut were used as predictor variables. Generalized bell memberships function (gbellmf) was adopted during the training process of ANFIS in this study. The predicted surface roughness using multiple regression and ANFIS were compared with measured data, the achieved accuracy were 91.9% and 94% respectively. These results indicate that the training of ANFIS with the gbellmf is accurate than multiple regression in the prediction of surface roughness.

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System configuration of Intelligent CNC machining

Machining processes are fundamentally complex, nonlinear, multi variate, and often subjected to various unknown external disturbances. A machining process is usually performed by a skilled operator who uses decision-making capabilities based on the intuition and rules of thumb gained from experience. This process is not accurate enough and in many cases product faults occur. For this reason and to realize highly productive and flexible machining, a reliable, automated machining system with intelligent functions (intelligent machining) is needed.

 

I tried to solve this problem in the following paper. 
http://link.springer.com/article/10.1007/s00170-014-6379-1 

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