High-strength Temperature-resistant Alloys Research Articles

Experimental Studies on Hastealloy C276 while Machining with CO2 Laser Cutting Machine

Laser cutting process is well suitable process for the cutting applications of high strength temperature resistant alloys, that are very difficult to cut with conventional process. Moreover, the lasers are widely using now-a-days for variety of cutting operations in many varieties of industries. Because of its complex nature of its operation, it is not easy to control so as to get the desired quality of cut in case of laser beam cutting process. This necessitates the importance of research to be carried out on the various processes i.e cutting, drilling, and welding etc by using the laser apparatus. The research mainly concentrated on the conduction of experiments i.e the cutting operation on Haste alloy C276 with CO2 laser beam. Total 31 trails are done using the CO2 laser beam apparatus. Laser power, speed, gas pressure and focal distance were the parameters chosen to vary. The outputs measured are surface roughness and the burr height which are direct measure to the quality of the cut surfaces. Two dimensional plots are plotted and analyzed the reasons for the increase or decrease of output with either the increase or decrease of input process parameters. Also, 3D plots are plotted to know the interactions.

Optimization of process parameters for turning of titanium alloy (Grade II) in MQL environment using multi-CI algorithm

The advancement of materials science during the last few decades has led to the development of many hard-to-machine materials, such as titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fibre-reinforced composites, and superalloys. Titanium is a prominent material and widely used for several industrial applications. However, it has poor machinability and hence efficient machining is critical. Machining of titanium alloy (Grade II) in minimum quantity lubrication (MQL) environment is one of the recent approaches towards sustainable manufacturing. This problem has been solved using various approaches such as experimental investigation, desirability, and with optimization algorithms. In the group of socio-inspired optimization algorithm, an artificial intelligence (AI)-based methodology referred to as Cohort Intelligence (CI) has been developed. In this paper, CI algorithm and Multi-CI algorithm have been applied for optimizing process parameters associated with turning of titanium alloy (Grade II) in MQL environment. The performance of these algorithms is exceedingly better as compared with particle swarm optimization algorithm, experimental and desirability approaches. The analysis regarding the convergence and run time of all the algorithms is also discussed. It is important to mention that for turning of titanium alloy in MQL environment, Multi-CI achieved 8% minimization of cutting force, 42% minimization of tool wear, 38% minimization of tool-chip contact length, and 15% minimization of surface roughness when compared with PSO. For desirability and experimental approaches, 12% and 8% minimization of cutting force, 42% and 47% minimization of tool wear, 53% and 40% minimization of tool-chip contact length, and 15% and 20% minimization of surface roughness were attained, respectively.

Problematic areas in micro-electrochemical milling of HSTR alloys

Micro-electrochemical milling has capability to fabricate intricate micro-features for fulfilling the rising needs of modern manufacturing industries. Electrochemical milling is an advanced version of the ECM process, and micro-electrochemical milling is just a scaled-down approach of it. Due to the formation of a passive oxide layer on the machinable surface, electrochemical dissolution of titanium and its alloy is very much difficult and it is much more challenging with this downscaled approach. Since the last few years, researchers are trying to get the best solution for successful machining of different high-strength temperature-resistant (HSTR) alloys, e.g. titanium, nickel and cobalt alloys, etc. In this research, an attempt has been made to identify the issues that arise during micro-electrochemical milling of three different HSTR alloys which are now-a-days extensively used in the modern manufacturing industries. Furthermore, this study also deals with the best possible remedial approaches for minimizing these problems. Finally, considering all the problematic areas and their best possible remedial approaches in this study, high aspect ratio (> 10) 3D micro-features have been successfully fabricated on HSTR alloys, e.g. nickel (Nimonic-263) and cobalt alloy (Haynes-188) at the most suitable parametric combination of 7.5 V of constant DC voltage, 0.4 mm/min of feed rate and 750RPM of tool rotation.

Development and Experimental Study of Electrical Discharge Drilling (EDD) Process for Titanium Alloy

Advanced engineering materials viz. titanium alloy, nickel alloy, heat-treated steels, superalloy, high-strength temperature-resistant alloys (HSTR) and metal matrix composites (MMCs), etc. are extensively being used in defence, automobile, aerospace, missiles and nuclear applications. Drilling holes in such advanced engineering materials are challenging due to their superior mechanical, electrical and thermal properties. Electrical discharge drilling (EDD) process is a very efficient hybrid machining process (HMP) for making holes in advanced engineering materials irrespective of their hardness and toughness. In the present work, an experimental setup of EDD process has been developed, and the effect of input parameters on the process performance of EDD is studied in terms of output parameters namely material removal rate (MRR) and tool wear rate (TWR). It has been observed from experimental study that the copper tool of 2-mm diameter is suitable for machining of small hole on titanium alloy with minimum TWR. Further, when tool rotation is increased from 600 to 900 rpm, about 17% more MRR and 34% reduction in TWR is observed.

Diamond abrasive based cutting tool for processing of advanced engineering materials: a review

Abstract The use of abrasives for grinding was first noticed in ancient Egypt in 2000BC. Quartz was the only abrasive known at that time. Further, emery, garnet and diamond were used as the other abrasive materials for grinding as per the need of various manufacturing processes. Diamond has been recognized as an alternate to various other available abrasive materials for grinding and finishing processes in last two decades. The machining of advanced materials such as titanium alloys, nickel alloys, heat treated steel, high strength temperature resistant alloys etc, resulted in the development of new machining processes, known as hybrid machining processes. These processes involve various new tools such as metal bonded diamond grinding wheel, diamond drill bits etc. and these are most widely accepted nowadays by the researchers in micro-machining field. In present paper, attempts have been made to do an exhaustive review of various advancements in the machining technology using the diamond abrasive based tools. Diamond abrasive tools are widely being used by various researchers in hybrid machining processes such as EDDCG, EDDFG, EDDSG, ECDG, ECDD etc. This paper illustrates the history of diamond abrasives and its use in recent advancements in the manufacturing technology till now with an overview of various manufacturing methods of the diamond abrasive based tools

Effects of process parameters on the performance of electrical discharge machining of AISI M42 high speed tool steel alloy

High strength temperature resistant alloys used in sophisticated applications in the industries are hard to machine owning to their high strength and improved mechanical properties. They are preferably machined by non-conventional machining methods due to their better control and automation. Among these non-conventional machining methods Electrical discharge machining (EDM) is one of the versatile machining process used by the manufacturing and automotive industries. In this study, such alloy AISI M42 tool steel used in aero-space and thermal applications is machined with EDM. The effect of specific machining parameters on MRR (Metal Removal Rate) was investigated during machining.During EDM commercial grade EDM-50 oil was used as di-electric fluid in this study.The present study reveals that maximum MRR was observed at -ve tool polarity. The most influential parameter effecting this process was found to be tool polarity followed by current.

Prediction of Surface Roughness Model for Nano Copper Suspended Electrochemically Machined Inconel 718 using ANFIS

Surface roughness Ra is considered as one of the primary decision making variables in assessing the performance of any machining processes. Electro Chemcial machining (ECM) is widely used advanced manufacturing process to machine the high hardened and poor machinable materials such as Super alloys, Inconel 718, Stainless steel, Die steel, High strength temperature resistant alloys. These materials, if machined by conventional machining processes, resulting in the poor surface finish due to the presence of high cutting forces and the generation of heat. Hence, an attempt is made in this research paper to predict the surface roughness of nano copper suspended electrochemically machined Inconel 718 using Adaptive Neuro Fuzzy Infernce System (ANFIS). Inconel 718 is one of the Super alloys with Nickel-Chromium combination is highly used in aerospace industries, high temperature fasteners, turbocharger rotors and Nuclear reactors. The major influencing parameters of applied voltage (V), tool feed rate (mm/min) and electrolyte discharge rate (lpm) with three levels each is selected as predictor variables (major influencing paramters) to identify its roles on the dependent variable of Surface roughness Ra in micron of machined surface. The predicted surface roughness values are validated using confirmation experiments, the maximum deviation is 8.51%. The developed model is verified with testing data in ANFIS generalized bell membership function (gbellmf) with and the average percentage error is 3.19%. These results prove that ANFIS model with gbellmf is accurate and can be used to predict the surface roughness of the nano copper suspended electrochemically machined Inconel 718.

Nontraditional machining processes selection using evaluation of mixed data method

Traditional edged cutting tool-based machining processes are now being continuously replaced by nontraditional machining (NTM) processes so as to generate complex and intricate shapes on advanced and harder materials, like titanium, stainless steel, high-strength temperature-resistant alloys, fiber-reinforced composites, and engineering ceramics. These NTM processes, while using energy in its direct form for removing materials from the workpiece surfaces, have the capabilities of meeting some higher level requirements, such as low tolerance, high surface finish, higher production rate, automated data transmission, miniaturization, etc., and are also quite suitable in the areas of micro- and nano-machining. Selection of the most appropriate NTM process to generate a desired shape feature on a given work material is often a challenging task as it involves consideration of diverse machining characteristics and performance of the NTM processes. This paper explores in details the applicability, suitability, and potentiality of evaluation of mixed data method for solving the NTM process selection problems. Three illustrative examples are presented, which validate the usefulness of this method. The observed results exactly corroborate with those obtained by the past researchers.