Experimental Study on Hybrid Cryogenic and Plasma-Enhanced Turning of 17-4PH Stainless Steel

This article presents machinability of 17-4PH stainless steel using a hybrid technique composed of plasma-enhanced turning and cryogenic turning. First of all, using some primary experimental tests and nonlinear regression, a mathematical model was developed for surface temperature of uncut chip as a function of plasma current and cutting parameters. Then, the influence of cutting speed (V_c), feed (f), and surface temperature of uncut chip (T_sm) was studied on surface roughness (R_a), cutting force (F_z), and tool flank wear (V_B). The results show that hybrid turning (HYT) is able to lower the main cutting force and tool flank wear in comparison with conventional turning. In addition, surface roughness was improved except for high level of surface temperature of uncut chip. However, hardness measurement of machined workpiece showed that HYT does not change the hardness of machined surface.     

Performance evaluation of different carbide inserts in turning of newly developed Al-12Si-0.1Sr alloy

An Al-12Si-0.1Sr alloy ingot was manufactured using a permanent mold casting technique. The microstructure and mechanical properties of this alloy were researched. Effects of different cutting conditions (cutting speed-V: 200 m/min, 300 m/min, and 400 m/min and feed rate-f: 0.05 mm/rev, 0.1 mm/rev, and 0.15 mm/rev) on the cutting force (F) and surface roughness (R_a) during machining using uncoated and physical vapor deposition- titanium aluminum nitride coated carbide inserts were also revealed. Microstructure of the alloys consists of α phase, intermetallic δ and Al₄Sr phases, thin spherical eutectic, and irregular coarse-shaped primary silicon particles. Cutting force and surface roughness decreased with the increased cutting speed during turning with uncoated, and titanium aluminum nitride coated inserts while they increased feed rate. A built-up edge and built-up layer were formed in both cutting inserts. The built-up edge and built-up layer decreased with increasing cutting speed and increased feed rate. The cutting force, surface roughness, built-up edge, and built-up layer were lower in uncoated inserts compared to the titanium aluminum nitride coated inserts.     

Machinability studies on 17-4 PH stainless steel under cryogenic cooling environment

Under higher cutting conditions, machining of 17-4 precipitation hardenable stainless steel (PH SS) is a difficult task due to the high cutting temperatures as well as accumulation of chips at the machining zone, which causes tool damage and impairment of machined surface finish. Cryogenic machining is an efficient, eco-friendly manufacturing process. In the current work, cutting temperature, tool wear (flank wear (V_b) and rake wear), chip morphology, and surface integrity (surface topography, surface finish (R_a), white layer thickness (WLT)) were considered as investigative machinability characteristics under the cryogenic (liquid nitrogen), minimum quantity lubrication (MQL), wet and dry environments at varying cutting velocities while machining 17-4 PH SS. The results show that the maximum cutting temperature drop found in cryogenic machining was 72%, 62%, and 61%, respectively, in contrast to dry, wet, and MQL machining conditions. Similarly, the maximum tool wear reduction was found to be 60%, 55%, and 50% in cryogenic machining over the dry, wet, and MQL machining conditions, respectively. Among all the machining environments, better surface integrity was obtained by cryogenic machining, which could produce the functionally superior products.     

Tool life model for Al2O3 particle reinforced MMCs using genetic expression programming

Abstract

In the present work, the wear of cutting tools in the machining of 7075Al alloy composites reinforced with 10 and 25 wt-% and 16 μm average size Al₂O₃ particles was investigated. Machining tests were performed on both as cast and heat treated composites by turning process using a physical vapour deposition coated carbide tool CP500 at different cutting conditions. A new model was developed to predict the tool life by genetic expression programming. The training and validation data sets were obtained from the well established machining test results. The weight fraction of particle P_w, cutting speed V, feedrate f and heat treatment Ht were used as independent input variables, while tool life T was used as a dependent output variable. Different models for tool life were predicted on the basis of the training data set using genetic programming, and the accuracy of the best model was proved with validation data set. The test results showed that the genetic expression programming model has produced correlation coefficient R values of ～0·958 for the training data and 0·952 for the validation data. The predicted tool life results were compared with experimental results and found to be in good agreement with the experimentally observed ones.     

Cryogenic Drilling of Ti–6Al–4V Alloy Under Liquid Nitrogen Cooling

This article is focused on experimental study of the effects of cryogenic liquid nitrogen (LN₂) coolant during drilling of Ti–6Al–4V alloy material with three different levels of cutting speed (V_c) and feed rate (f) at a constant depth. Cutting temperature (T), thrust force (F_z), torque (M_z), surface roughness (R_a), and hole quality are the output responses investigated by using cryogenic LN₂ coolant compared with a wet coolant. Tool wear and chip morphology were examined with the changes in cryogenic LN₂ coolant. It is found that cryogenic LN₂ coolant results in lowering cutting zone temperature which helps more removal of heat from the cutting zone. Lower thrust forces and surface roughness were observed due to less friction and better chip breaking in cryogenic LN₂ condition. Also better chipping results in improvement in hole quality, viz., circularity and cylindricity in cryogenic LN₂ condition. Less serration and uniform segmentation results in better chip morphology and no damage to the cutting inserts resulted in improved tool life in cryogenic LN₂ condition. The main application of cryogenic LN₂ coolant in the cutting zone provides better lubrication and is more effective than wet coolant. The effects of this investigation show that cryogenic LN₂ coolant is an alternative approach for a wet coolant in the drilling process.     

Mechanical micro-drilling of nimonic 80A superalloy using uncoated and TiAlN-coated micro-drills

Micro-drilling is a complex mechanical machining process. Micro-drilling experiences an early tool damage which is a major drawback for nickel-based superalloy. This paper examines the wear condition on the micro-tool cutting edge, surface roughness of machined holes, and hole diameter analysis in micro-drilling of Nimonic 80A, using two types of micro-drills (uncoated and TiAlN coated) with 0.79?mm diameter. Micro-drilling tests, using cutting speed (V_c), feed rate (f_z), and the micro-drill diameter as experimental parameters were carried out to bring out the best optimized machining conditions in micro-drilling of Nimonic 80A. Wear on the tool cutting edge and burr height occurring at the entrance of drilled holes were measured at constant period to give the lastingness of micro-drill. Quality of holes were analyzed in terms of surface roughness inside the hole and the hole diameter after every five drilled holes. The result obtained from the above analysis showed that TiAlN-coated micro-drill performs way better than the uncoated micro-drill in terms of wear, surface roughness, hole quality, and burr. Thus, the above performed study gives the knowledge to select micro-tool for machining of Nimonic 80A which could be useful in the aerospace industry.     

Investigation on performance of abrasive water jet in machining hybrid composites

For machining of composites, abrasive water jet machining is widely employed. For assembly of the machine tool structure, production of slots is essential. In this paper, abrasive water jet machining of composite laminates was experimentally investigated for various cutting parameters in terms of average surface roughness (R_a) and kerf taper (K_t). By generating a response surface model, the experimental values obtained for quality characteristics (R_a and K_t) were empirically related to cutting parameters. The effects of cutting parameters on quality characteristics were analyzed by utilizing empirical models and also optimized within the tested range based on desirability approach. The optimum parameter levels were also validated by confirmation test. From this investigation, it is evident that for obtaining a minimum kerf taper, traverse speed, water pressure, and abrasive mass flow rate are significant parameters and for obtaining less surface roughness traverse speed is the significant parameter.     

Development of ANFIS model and machinability study on dry turning of cryo-treated PH stainless steel with various inserts

This present investigation deals about the machinability comparison of cryogenically treated 15-5 PH stainless steel with various cutting tools such as uncoated tungsten carbide, cryogenic-treated tungsten carbide and wiper geometry inserts. Cryo-treated PH stainless steel is considered as the work material in this investigation and experimental trials were performed under dry turning condition. The machinability aspects considered for evaluation are cutting force (F_z), surface roughness (R_a) and tool wear. The outcomes of experimentation reveal that the tungsten carbide inserts which are cryogenically treated provide improved performance in machining while comparing with conventional and wiper geometry inserts at all machining conditions. The measured cutting force and the observed flank wear were less for the cryo-treated inserts. However, wiper tool produces a better surface finish during machining. An artificial intelligence decision-making tool named Adaptive Neuro Fuzzy Inference System has been evolved to determine the relation among the considered input machining variables and output measures, namely cutting force and surface roughness of the machined surface. An analysis has been performed to compare the results obtained from developed models and experimental results.     

Integrated ANN–GA for estimating the minimum value for machining performance

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R _a). The minimum R _a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R _a can be estimated to be 0.139?µm, at the optimal cutting conditions of f?=?167.029?m/min, v?=?0.025?mm/tooth and γ?=?14.769°. Consequently, the ANN–GA integration system has reduced the R _a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R _a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.     

Prediction of cutting speed interval of diamond-coated tools with residual stress

This article deals with finite element (FE) analysis incorporating deposition stress effects to determine the optimal cutting speed interval, V_interval, and evaluate the effects of cutting speed on the interface behavior of diamond-coated tools in machining of AA356-T6 aluminum alloy. A model for predicting the V_interval limited through the lower, V_lower, and upper, V_upper, cutting speeds is also presented. The results show that the deposition process causes high residual stress around the round edge on the coating side. When machining load corresponding to the speed interval is applied, the residual stress on the coating side is decreased at the V_upper.     

Experimental Investigations on Cryogenic Cooling in the Drilling of Titanium Alloy

In this study, a drilling experiment was conducted on titanium ASTM B265 Grade 2 material using PVD coated carbide inserts. Two types of coolants (Wet and LN₂) were used. The variables in the experiment were feed rate (f) and cutting speed (V_c). The depth of the drilling was constant. Cutting temperature (T), thrust force (F_t), surface roughness (R_a), and the hole quality (circularity, cylindricity, and perpendicularity) were analyzed. The tool wear and chip morphology were studied. The result of the experiment indicates that there is 6–59% reduction in cutting temperature when LN₂ is used, high thrust force values were recorded for LN₂ coolant condition, surface roughness (R_a) values were higher for LN₂ coolants. Hole quality is not favorable in LN₂ coolant supply.     

An Investigation into Turning of ASSAB-705 Steel Using Multiple Sensors

The complex behavior of various occurrences in turning has made the tool condition and process monitoring with a conventional tool-sensor setup difficult. An additional passive tool arrangement has been adopted to circumvent the multifaceted mechanism of different occurrences and thus to investigate them by measuring the acoustic emission (AE), and vibration signals produced thereof. The investigation shows that both the AE and the radial vibration component, V_y can independently assess the chip formation effect on cutting process and tool state. The tangential vibration component, V_z can effectively evaluate the rate of flank wear progression whereas the resultant vibration components are efficient in measuring the surface roughness of workpiece in turning. The feed directional vibration, V_x is always maximal regardless of cutting variables, tool wear, surface roughness, and chip formation type. The application of vibration sensor can eliminate the necessity of the additional passive tool setup, and jointly with the AE sensor can investigate the process and cutting tool condition more promisingly.     

Optimization of interior roller burnishing process for improving surface quality

Improving the surface characteristics of roller burnishing processes is one of effective approaches to decrease the machining costs and time. This paper systematically investigates the nonlinear relationships between machining parameters and surface characteristics, including surface roughness (R_a), surface hardness (H), and hardness depth (HD) of the interior roller burnishing using response surface method (RSM) model. Three process parameters considered include spindle speed S, feed rate F, and burnishing depth D. A set of physical experiments was carried out with AISI 1045 steel on a computer numerical control (CNC) milling machine using the roller burnishing tool. The target of the current complex optimization is to enhance the surface hardness and hardness depth, while the surface roughness is considered as the constraint. Finally, an evolutionary algorithm entitled archive-based micro genetic algorithm (AMGA) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low surface roughness and high the surface hardness as well as hardness depth. Furthermore, the integration of RSM model and AMGA can be considered as a powerful approach for modeling and optimizing interior roller burnishing processes.     

Experimental investigation into the effects of nozzle position,workpiece hardness,and tool type in MQL turning of AISI 1045 steel

Minimum quantity lubrication (MQL) is a replacement for dry machining in which a minimum quantity of lubricant fluid is mixed up with compressed air and sprayed periodically on the machining area. In this research the effects of different parameters on the MQL turning of AISI 1045 steel have been investigated to evaluate the cutting force, surface roughness, and tool wear in comparison with the wet and dry machining. The research is aimed to study the effect of the MQL nozzle position, workpiece hardness and tool type on the output parameters. During MQL machining experiments, the nozzles were placed in three different arrangements relative to the tool to investigate the effect of the nozzle position. The effect of workpiece hardness and tool type were also studied experimentally for different lubrication conditions. The results indicated that the MQL system significantly increases the cutting efficiency in AISI 1045 steel machining. The experiments results have also confirmed a significant influence of the nozzle position, workpiece hardness, and tool type on the outputs. Machining with MQL is also beneficial to the environment and machine tool operator health as lubricant consumption during operation with MQL is 7-fold lower than in the conventional system.     

Tool wear and tool life of PCBN,binderless cBN and wBN-cBN tools in continuous finish hard turning of cold work tool steel

The paper presents the results of comparative study of performance of cutting tools made of ceramic-bound, binderless cBN, and wBN-cBN tool materials. The tool performance was assessed by tool wear-resistance, values of cutting forces, parameters of machined surface quality, and the state of sub-surface layer generated in continuous turning of hardened cold work tool steel. The tests were carried out under conditions of high speed machining (v _c = 120–180 m/min) both with and without a coolant. The best tool performance by the above-mentioned criteria is provided by a low-cBN material with ceramic binder.     

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a _p), radial immersion (a _e), feed rate (f _t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.     

Study of deformation zone effects of low,conventional, high and very high speed machining

The paper deals with cutting speed in range 3 m?min^‐1 up to 2200 m?min^‐1 and its complex impact mainly on chip macroscopic shape, chip microstructure, chip compression, tool wear, tool life and machined surface quality and interprets and compares the effects regarding low, conventional, high and very high speed machining based on the dry turning of carbon steel by sintered carbide coated by titanium nitride and ceramic cutting inserts. The deformation zone response for different cutting speeds at the tool‐chip‐workpiece interfaces and their effect on tool wear were studied. The extensive (so called complete) experiments within wide range of values and large number of measurements were carried out. The formation of secondary chip occurring in high speed turning is reported. Moreover, the paper analyses the total machining time involving tool replacement time in terms of high speed machining regarding the obtained experimental results.     

Optimization of photochemical machining process parameters for manufacturing microfluidic channel

In the present study, the investigation on photochemical machining (PCM) of stainless steel (SS-304) by ferric chloride as etchant is reported. SS-304 is machined by PCM process to obtain accurate dimensions and better geometrical features. Weighted grey relational analysis (WGRA) technique is used in optimization of PCM process parameters. DoE (L₂₇) orthogonal array is applied to evaluate machining parameters, such as concentration of etchant, etching time, and temperature of etchant. The multiobjective optimization technique is used to optimize material removal rate (MRR), surface roughness (R_a), undercut (U_c) and etch factor (EF). Weighted grey relational grade is calculated to minimize U_c and surface roughness and to maximize MRR and EF. The quality characteristics MRR, EF, U_c, and R_a are reporting the improvement after the confirmatory test. The optimum machining parameters are processed to manufacture the microfluidic channel used in biomedical applications. The microfluidic channels and its assembly with Y-type for mixing of fluid with a size of 100 µm, 200 µm, and 300 µm are developed and investigated.     

AWJM Performance of jute/polyester composite using MOORA and analytical models

This work addresses the machinability performance of jute/polyester composites with variable laminate thickness using Abrasive water jet machining (AWJM) process. A hybrid objective function was developed using surface roughness (R_a) and kerf taper angle (T_a) and studied using a cost-effective Multi Objective Optimization by Ratio Analysis named as MOORA. The influence of machining parameters such as hydraulic pressure (P), feed rate (V_f) and standoff distance (S_d) on quality characteristics were considered for this analysis. Among all, V_f was found to be a strong influencing factor on T_a and R_a. The deviation in the magnitude of T_a and R_a was observed in the case of varying laminate thicknesses without affecting the optimum condition. Besides, a mathematical regression model was developed for both T_a and R_a based on the correlation between the dependent variables. Furthermore, two other models of R_a available in the literature were considered for comparison with experimental results. The results revealed the suitability of these models for the polymer-based fiber-reinforced composite materials, but limited to the maximum thickness of 3?mm. The good agreement of the models with two different sets of experimental values was also found.     

Studies on Machining Parameters While Milling Particle Reinforced Hybrid (Al6061/Al2O3/Gr) MMC

In this article, response surface methodology has been used for finding the optimal machining parameters values for cutting force, surface roughness, and tool wear while milling aluminum hybrid composites. In order to perform the experiment, various machining parameters such as feed, cutting speed, depth of cut, and weight (wt) fraction of alumina (Al₂O₃) were planned based on face-centered, central composite design. Stir casting method is used to fabricate the composites with various wt fractions (5%, 10%, and 15%) of Al₂O₃. The multiple regression analysis is used to develop mathematical models, and the models are tested using analysis of variance (ANOVA). Evaluation on the effects and interactions of the machining parameters on the cutting force, surface roughness, and tool wear was carried out using ANOVA. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. The optimum machining parameters obtained from the experimental results showed that lower cutting force, surface roughness, and tool wear can be obtained by employing the combination of higher cutting speed, low feed, lower depth of cut, and higher wt fraction of alumina when face milling hybrid composites using polycrystalline diamond insert.