Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Damage detection in structural elements: using adaptive Mamdani model

In real life all the structural and machine elements work under dynamic or variable loading. Application of dynamic loading leads to fluctuating stress. Due to fluctuating stress fatigue cracks initiates. These fatigue cracks are the main reason of failures. So, it is very important to detect the crack and predict the crack life. There are different types of damages but crack is one of the most encountered damage. There are different conventional methods to detect the damage but these methods are time taking and requires removal from the machines. Therefore, researchers are giving more importance to the unconventional methods to find the damage. In the present work a method has been introduced to find the damage site using Fuzzy Logic System and Regression Analysis. In particular, this paper focuses on applying statistical process control methods. A data pool has been created from the dynamic analysis of the cracked cantilever beam and then the data pool is trained in the proposed methodology to find the crack location. It has been noticed that the proposed methodology gives result within the tolerable range

Support Vector Regression approach for prediction of delamination at entry and exit during drilling of GFRP Composites

The demand for Composites in the modern era have increased immensely due to its vast applications and superior properties over conventional materials. Glass Fibre Reinforced Plastic (GFRP) is one of the economic alternative to conventional engineering materials due to its high specific modulus of elasticity, high specific strength, good corrosion resistance, high fatigue strength and lightweight. Components made out from GFRP composites are usually near net shaped and require holes for assembly integration. Drilling is an important process as concentrated forces can cause major damage to the composite. Drilling of GFRP causes various damage such as thermal degradation, fibre breakage, matrix cracking and delamination. A substantial damage is caused by delamination which can occur both on the entry and exit sides of the composite, exit side delamination considered more severe. Therefore, selection of proper process parameters during drilling operation is very much essential. In the present work, a support vector regression (SVR) model is developed to predict the delamination at entry and exit during the drilling of GFRP composites. The model is developed based on the data obtained from experimentation. The model accuracy is evaluated by the three performance criteria including root mean square error (RMSE), Nash–Sutcliffe efficiency co-efficient (E) and co-efficient of determination (R2). The model provides an inexpensive and time saving alternative to study the delamination at entry and exit of the GFRP composite actual drilling operation

Study of Orientation of Triangular and Square die hole profiles on Extrusion Load in Multi-hole Extrusion process

The multi-hole extrusion process is an extrusion process with a die having more than one hole. The multi-hole extrusion process is used to extrude more than one product from the same billet with less extrusion load. Different die profiles are used to get different extruded profiles as per the need. Different profiles such as circular, triangular, square etc., are used. The symmetric die hole profiles are easy to fabricate on the extrusion die but the location and positioning of other types of hole profiles like triangular and square need attention as they cause different material flow behavior during material flow. In the present study, triangular and square dies of 2-hole, 3-hole and 4-hole with different orientations of apex and edges have been used to study the extrusion load behavior. Both finite element analysis and experimental study reveals that square hole dies need less extrusion load than that of triangular ones