Abhijit Saha

Multi Criteria Selection of Optimal Machining Parameter in Turning Operation Using Comprehensive Grey Complex Proportional Assessment Method for ASTM A36

Turning is one amongst the most adequate and cost-effective method in manufacturing environment. To meet the difficulties of extensive competitiveness in manufacturing industry the issues of selecting best preferable methodologies, design of process, machine and equipment becomes more mind boggling as there are lots of options in view of conflicting criteria. To help these selection processes, different Multi Criterion Decision-Making (MCDM) techniques are currently accessible. In this paper, MCDM technique was performed considering hybrid approach comprehensive grey complex proportional assessment (COPRAS-G) to identify optimal parameters for important multi-performance criteria like Surface roughness, power consumption and frequency of tool vibration. The important process parameters used during turning of ASTM A36 mild steel were depth of cut, spindle speed and feed using full factorial Taguchi’s design of experiment. Finally, optimal settings of input process parameters were found as speed: 160 r.p.m., depth of cut: 0.1 mm. and feed rate: 0.08 mm/rev. This study aimed to focus the applicability of COPRAS-G as a strategic decision making tool to handle multi objective optimization problem for tuning operation.

Multi-objective Optimization of Welding Parameters in MMAW for Nano-structured Hardfacing Material Using GRA Coupled with PCA

Hardfacing is one of the most productive and practical approaches to cut down operating expense on the maintenance front and at the same time to improve performance and reliability of the equipment. Presence of nano-particles in hard facing materials significantly enhances surface area to volume ratio and accordingly it improves conductivity, hardness, heat and wear resistant properties. The main objective of this paper is to efficiently apply manual metal arc welding process for hardfacing of nano-structure based electrode. The most important process variables that have been considered in conducting the experiments are welding current, arc voltage and welding speed; while the response parameters include weld bead width, reinforcement and bead hardness, respectively. Taguchi’s (L25) orthogonal array has been used to perform the experimental runs. A combination of grey relational analysis coupled with principal component analysis has been applied to identify optimal settings of the input process parameters. Moreover, the exact input and output welding parameters have been examined with the help of genetic algorithm. Finally, confirmation test has also been carried out with the optimal welding process parameters to validate the experiment result.

Optimization of Process Parameters in Submerged Arc Welding Using Multi-objectives Taguchi Method

Submerged arc welding (SAW) is one of the oldest automatic welding processes to provide high quality of weld. The quality of weld in SAW is mainly influenced by independent variables such as welding current, arc voltage, welding speed, and electrode stick out. The prediction of process parameters involved in SAW is very complex process. Researchers attempted to predict the process parameters of SAW to get smooth quality of weld. This paper presents an alternative method to optimize process parameters of SAW of IS: 2062, Gr B mild steel with multi-response characteristics using Taguchi’s robust design approach. Experimentation was planned as per Taguchi’s L8 orthogonal array. In this paper, experiments have been conducted using welding current, arc voltage, welding speed, and electrode stick out as input process parameters for evaluating multiple responses namely weld bead width and bead hardness. The optimum values were analyzed by means of multi-objective Taguchi’s method for the determination of total normalized quality loss (TNQL) and multi-response signal-to-noise ratio (MRSN). The optimum parameters for smaller bead width and higher bead hardness are weld current at low level (12.186 A), arc voltage at low level (12.51 V), welding speed at low level (12.25 mm/min), and electrode stick out at low level (12.29 mm). Finally, confirmation experiment was carried out to check the accuracy of the optimized results.

Optimisation of wire electric discharge machining process: a review and reflection

Wire electric discharge machining (WEDM) process is a potential electro-thermal non-conventional machining process which is useful for machining difficult-to-cut electrically conductive materials. In addition, the development of newer and more exotic materials has challenged the viability of the WEDM process in the future manufacturing environment. Hence, continuous improvement needs to be made to the current WEDM in order to extend the machining capability and increase the productivity and efficiency. Optimisation methods in machining processes, considered being a vital tool for continual improvement of output quality in products and processes. This paper presents a review of several modelling and optimisation techniques in wire electric discharge machining (WEDM). A generic framework for optimisation of process parameters in WEDM process is also suggested for the selection of appropriate WEDM process parameters and improvement of the process performance. The paper also discusses the future trend of research work in the same area.

Statistical Analysis and Optimization of Process Parameters in Wire Cut Machining of Welded Nanostructured Hardfacing Material

Wire electric discharge machining (WEDM) is a nontraditional machining technique to cut hard and conductive material with the assistance of a moving electrode. Nanostructured hardfacing material is a hard alloy with high hardness and wear resisting property. The motivation behind this research is to explore the impact of parameters on material removal rate, surface roughness and machining time for WEDM using welded nanostructured hardfacing material as work piece. The hardfacing layer was prepared by manual metal arc welding (MMAW). Taguchi’s L25 orthogonal array was utilized to design the investigational runs. Different hardfaced layer thicknesses were examined to bring out the influence of hardfacing on WEDM performances. Moreover, Multi-objective optimization was carried out using TOPSIS and PCA to recognize optimal process parameters. Optimum combination of input process parameters for the multiple performance characteristics should be preferred as A1B5C5D5E5 (brass wire) and A2B3C4D5E1 (Zinc coated brass wire).

Welding Parameters Optimization in MMAW Assisted Nano-Structured Hardfacing Using Desirability Function Analysis Embedded with Taguchi Method

In the assembling enterprises, welding assisted hardfacing has pulled in expanding consideration for its powerful protection against erosion, thermal shock, and abrasion. Nano-particle reinforcements can fundamentally enhance the mechanical properties of the lattice by more viably advancing the molecule solidifying components than micron estimate particles. This paper presents the multi-target optimization of manual metal arc welding (MMAW) process parameters in hardfacing with Nano-technology based electrode. Experimentation was arranged according to Taguchi’s orthogonal array. In this paper, tests have been led utilizing welding current, arc voltage, welding speed as input process parameters for assessing numerous responses in particular weld bead width, reinforcement and bead hardness. A mix of Taguchi’s robust design idea with desirability function analysis (DFA) has been connected to enhance the process parameters. A composite desirability value is gotten for the multi-responses utilizing individual desirability values from the DFA. In light of composite desirability value, the best possible levels of parameters have been distinguished.

Machining Optimization of Nano-structured Hardfaced Tool Insert in WEDM Using MOORA Method

In recent years, nano-structured hardfacing alloy constituting fine carbides, borides and boro-carbides have become spearhead in cutting edge producing enterprises because of their prevalent properties like excellent hardness, toughness and wear resistance even at elevated temperatures. Nano-structured hardfacing alloy was produced using manual metal arc welding technique. This paper has focused on wire electrical discharge machining (WEDM) process identifies the influence of process parameters that affect the material removal rate, machining time and surface roughness while machining of nano-structured hardfacing alloy. The paramount process parameters have been employed during experiments such as discharge pulse time, discharge stop time, servo voltage, wire tension and wire feed rate. Experiments were designed using Taguchi’s L25 orthogonal array. Multi-objective optimization was performed using MOORA (multi-objective optimization on the basis of ratio analysis) method coupled with PCA (principal component analysis) to identify optimal process parameters. The outcome demonstrates that discharge pulse time (0.5 µs), discharge stop time (8 µs), servo voltage (35 V), wire tension (800 g) and wire feed rate (7 m/min) are the optimal process parameters using brass wire and for zinc coated brass wire that corresponds to discharge pulse time (0.5 µs), discharge stop time (8 µs), servo voltage (41 V), wire tension (500 g) and wire feed rate (7 m/min) respectively.