Set Of Machining Parameters Research Articles

Machining of Ti-6Al-4V biomedical alloy by WEDM: investigation and optimization of MRR and Rz using grey-harmony search

PurposeThe purpose of this paper is to investigate the optimized setting of wire-cut electrical discharge machining (WEDM) parameters at which material removal rate (MRR) and mean roughness depth (Rz) set a compromise. The problem in the processing of Ti-6Al-4V by conventional processes is a high strength, high hardness, high tool wear. Due to which WEDM is adopted to machine Ti-6Al-4V biomedical alloy. Ti-6Al-4V alloy has a number of applications in the engineering and medical industries due to its high strength biocompatibility.Design/methodology/approachThe effect of control factors (i.e. pulse on-time: Pon; pulse off-time: Poff; servo voltage: SV) on the MRR and Rz is investigated in the present research. The planning of experiments is done using a Taguchi-based L9 orthogonal array. The percentage influence of each factor on responses is also evaluated. The multi-objective optimization is done using the grey approach initially. After that, the results were also calculated using harmony search (HS). Therefore, a hybrid approach of grey and HS is used to find the optimized values of MRR and Rz.FindingsThe maximum value of grade calculated by grey-HS is 0.7879, while in the case of the experimental run the maximum value of grey grade is 0.7239. The optimized setting after improvisation at this grade value is Pon: 130 µs; Poff: 45 µs and SV: 70 V for MRR and Rz collectively. The validation of the suggested setting is completed by experimentation. The values of MRR and Rz are coming out to be 6.4 mm3/min and 13.84 µm, which represents improvised results after the implementation of the HS algorithm.Originality/valueThe integration of the grey approach with the HS principle in the manufacturing domain is yet to be explored. Therefore, in the present research hybrid approach of grey-HS is implemented in the manufacturing domain having applications in medical industries.

Defining an Optimized Machine Process Sequence to Address Broken Wafer Phenomenon on Semiconductor Products

Silicon wafer as a direct material is one of the vital parts of a semiconductor product. Wastages on manufacturing plants that pulls the yield down should be addressed innovatively and accurately. This paper focused on the phenomenon of broken wafers at wafer taping process during wafer preparation. Using a wafer taper machine, silicon wafers are covered by an industrial tape as preparation for the next process. During processing and wafers are placed on wafer boat, unexpected phenomenon of broken wafers due to unwanted falling was encountered. Findings was due to the unintentional dragging of the machine’s robot arm after wafer processing. The problem is resolved through simulation and experiments using statistical analysis. As a result, an optimized machine parameter setting was defined to eliminate the said rejection. Statistical analysis was of a big help in resolving the said phenomenon and improved the process yield of the manufacturing.

Fabrication of high aspect-ratio tungsten microtools through controlled electrochemical etching

ABSTRACT High-aspect-ratio cylindrical tungsten microtools were fabricated through the electrochemical micro-machining (µECM) process. The effects of eight different electrolytes in combination with the different major machining parameters were investigated and reported in this article. KOH(aq) electrolyte was found to be the most suitable electrolyte for the fabrication of tungsten microelectrodes. The Taguchi technique was utilized for optimizing the major process control parameters to minimize the taper angle of the fabricated micro-tools. During the tool fabrication process, the deposited electrochemical reaction on the tool surface played an important role to achieve controlled dissolution of the tool materials and helped in producing a uniform surface. The microtools of diameter 40 µm with aspect-ratio of about 175 were fabricated with the optimal settings of dominant machining parameters. The fabricated microtools were used to drill through drill-holes on a nickel plate of thickness 1050 µm using the same µECM setup. The detailed experimental investigations are carried out and conclusive remarks are drawn based on the experimental results.

Penentuan Alternatif Material Part ABS Untuk Menjamin Kesinambungan Pasokan Pada Perusahaan Manufaktur Sepeda Motor

<p><em>Ensuring the continuous supply of materials in the manufacturing system is a very important factor in ensuring the smooth production process and product fulfillment to consumers. </em><em>A</em><em> motorcycle manufacturing company that has several plastic parts injection suppliers, but the problem is that some plastic parts suppliers only take from one material supplier with the trademark ABS-J. This single supplier is very risky in terms of the ability to supply materials and also the possibility of a price monopoly that can be done by material suppliers because they have no competitors. With the PDCA approach, research is conducted to determine the addition of alternative ABS materials to ensure material supply availability and also obtain potential benefits that can be obtained by implementing multi-ABS material suppliers. With the trial and error method of using other alternative materials, namely ABS Star and also fish bone analysis, can get the same quality results as ordinary ABS-J material, by changing the injection machine parameter settings. The parameter setting is oriented towards reducing the temperature of the material by lowering the temperature of the barrel to (230-225-220-200 ° C), reducing the injection speed (45-55-45-35) and using Holding Pressure (70 Pa) for ABS Star material.</em></p><p> </p><p align="center"><em> </em></p>

Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA

Purpose In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA). Design/methodology/approach Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing. Findings It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand. Originality/value The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.

Surface Morphology Analysis Using Convolutional Autoencoder in Additive Manufacturing with Laser Engineered Net Shaping

Additive manufacturing (AM) has gained increasing popularity in various quality critical applications such as aerospace and healthcare due to its high flexibility in fabricating complex geometries with novel materials. However, the relatively poor layer-wise surface quality may lead to compromised property and functionality of AM products. The experimental studies have demonstrated that the AM machine parameters could have significant effects on the resulting surface morphology. Therefore, it is critically needed to analyze the surface morphology in AM and further correlate the morphology features with machine parameters. In practice, there are two major challenges: (1) the surface profiles are usually highly nonlinear; and (2) a large fraction of outliers and missing regions may commonly exist in the measured surface profile. To address these challenges, this paper applies a convolutional autoencoder-based approach to extract informative features from surface profile. With the application of regularization, the autoencoder model is capable of discovering effective low dimensional representations from the high dimensional surface profile data with a large fraction of corruptions. Through supervised machine learning, the correlation between surface morphology and machine parameters can be further quantified. To validate the effectiveness of the proposed method, a case study in laser engineered net shaping (LENS) is conducted to identify the variation of the surfaces fabricated under similar settings of machine parameters. The result shows that the classification accuracy based on the proposed method could achieve 70%, which outperforms benchmark methods. Therefore, the developed convolutional autoencoder-based surface morphology analysis approach has promising potential for surface morphology feature extraction in AM.

RETRACTED: Performance of Ti-6242 production using nano powder mixed with different dielectrics

The recent scenario of modern manufacturing is tremendously improved in the sense of precision machining and abstaining from environmental pollution and hazard issues. In the present work, Ti-6242 is machined through wire EDM (WEDM) process with powder mixed dielectric and analyzed the influence of input parameters and inherent hazard issues. WEDM has different parameters such as peak current, pulse on time, pulse off time, gap voltage, wire-speed, wire tension, and so on, as well as dielectrics with powder mixed. These are playing an essential role in WEDM performances to improve the process efficiency by developing the metal removal rate. Even though the parameter’s influencing, the study of the nano dielectric effect in the WEDM process is very essential during the machining process due to the high discharge energy. In the present study, two different dielectric fluids were used, including deionised water, and nanopowder dielectrically, and analyzed the data by taking the response surface method to use program design expert 10. From this study, it is established that dielectric types and powder significantly improve performances with a proper set of machining parameters and find out the risk factor associated with the WEDM process.

Influence of machining parameters on micro-motion platform displacement during grinding Al-Mg alloys workpiece assisted by two-dimensional low-frequency vibration

The main factors affecting the displacement of micro-motion platform during the grinding process are spindle speed, cutting force, and piezoelectric ceramic input voltage model. This article, using the orthogonal test method, found a set of machining parameters which lead to less displacement deviation between practical test and theoretic analysis. First of all, single-factor experiments were carried out to study how spindle speed, cutting force, and piezoelectric ceramic input voltage model affect the experimental results, and then the orthogonal test was conducted. The experimental datum shows that voltage model was the most influential factor, followed by spindle speed and cutting force. The optimum combination of grinding parameters was obtained as spindle speed of 800 r/min, cutting force of 18 N, and voltage model radius of 12 µm. At this time, the average unit error of displacement of micro-motion platform was 9.13%.

Studies on Process Parameter Optimization of Inconel 718 Turning Operation using Taguchi based Grey Relational Analysis

Accompanying the development of mechanical industry, the demand for alloy materials having high hardness, toughness and impact resistance are increasing. Nevertheless, such materials are difficult to be machined by traditional machining method. Hence CNC machines are used to machine such materials, which are capable of producing fine, precise, corrosion and wear resistance surfaces. The problem of arriving at optimal levels of operating parameters has attracted the attention of the researchers and practicing engineers, for a very long time. Thus, this paper demonstrates the optimization of the process parameter of machining Inconel 718 super alloy material via., the Taguchi methodbased grey analysis. The modified algorithm adopted here was successfully used for both detraining the optimum settings of machine parameters and for combining multiple quality characteristics into one integrated numerical value called grey relational grade. An attempt has been made to identify the influence of various cutting parameters i.e., speed, feed rate and depth of cut on physical part characteristics i.e., metal removal rate and surface roughness. The predictions of optimal process parameter with respect to the response are the end results of the paper.

Optimization of cutting tool geometry and machining parameters in turning process

Vibrations play a major role in increasing the cost of production as they lead to a poor surface finish of the machined product and can also damage the cutting and machine tool. In this work, the influence of cutting tool geometry of a turning tool and machining parameters used in turning operations on regenerative chatter vibration during orthogonal cutting was studied. The optimum values of orthogonal rake angle, clearance angle, cutting tool overhang, feed rate and depth of cut are found by developing a model using design of experiments and the model was analysed using a statistical technique known as response surface method (RSM). The objective is to optimize cutting tool geometry and machining parameters of turning process by minimizing the real part of frequency response function (FRF) which is a response of a cutting tool subjected to a harmonic force. The harmonic analysis, carried out using software ANSYS, was used to get the peak value of real part of frequency response function (RFRF) of the cutting tool and its magnitude has to be minimized to improve the machining stability against regenerative chatter during machining. It may be inferred from the results that the tool rake angle, clearance angle, overhang of the cutting tool, feed rate and depth of cut are interacting with each other and influence the machining stability simultaneously. Hence, it may be advisable to pick an optimum set of machining parameters for the specific combination of cutting tool geometry to have vibration-free machining.

WEDM Used for Machining High Entropy Alloys.

Unconventional wire electrical discharge machining technology (WEDM) is a key machining process, especially for machining newly emerging materials, as there are almost no restrictions (only at least minimal electrical conductivity) in terms of demands on the mechanical properties of the workpiece or the need to develop new tool geometry. This study is the first to present an analysis of the machinability of newly developed high entropy alloys (HEAs), namely FeCoCrMnNi and FeCoCrMnNiC0.2, using WEDM. The aim of this study was to find the optimal setting of machine parameters for the efficient production of parts with the required surface quality without defects. For this reason, an extensive design of experiments consisting of 66 rounds was performed, which took into account the influence of five input factors in the form of pulse off time, gap voltage, discharge current, pulse on time, and wire speed on cutting speed and the quality of the machined surface and its subsurface layer. The analysis of topography, morphology, subsurface layers, chemical composition analysis (EDX), and lamella analysis using a transmission electron microscope (TEM) were performed. An optimal setting of the machine parameters was found, which enables machining of FeCoCrMnNi and FeCoCrMnNiC0.2 with the required surface quality without defects.

Performance Analysis on Eco-friendly Machining of Ti6Al4V using Powder Mixed with Different Dielectrics in WEDM

The recent scenario of modern manufacturing is tremendously improved in the sense of precision machining and abstaining from environmental pollution and hazard issues. In the present work, Ti6Al4V is machined through wire EDM (WEDM) process with powder mixed dielectric and analyzed the influence of input parameters and inherent hazard issues. WEDM has different parameters such as peak current, pulse on time, pulse off time, gap voltage, wire speed, wire tension and so on, as well as dielectrics with powder mixed. These are playing an essential role in WEDM performances to improve the process efficiency by developing the surface texture, microhardness, and metal removal rate. Even though the parameter’s influencing, the study of environmental effect in the WEDM process is very essential during the machining process due to the high emission of toxic vapour by the high discharge energy. In the present study, three different dielectric fluids were used, including deionised water, kerosene, and surfactant added deionised water and analysed the data by taking one factor at a time (OFAT) approach. From this study, it is established that dielectric types and powder significantly improve performances with proper set of machining parameters and find out the risk factor associated with the PMWEDM process.

Corrosion Resistance of Ti-6Al-4V Machined Surfaces Improved by Thermal Oxidation

Ti-6A-l4V alloy is widely used in implants and prosthesis applications. Although machining is a fast and economical process, the roughness generated can compromise corrosion resistance. Thus, the goal of this study was to overcome this limitation using thermal oxidation in machined surfaces. Samples with polished surfaces were used for comparison purposes. Two sets of machining parameters were used to generate different roughness, property evaluated in polished and machined samples before and after thermal oxidation. Vickers microhardness and polarization tests using simulated body fluid (SBF) were also performed. Thermal oxidation generated similar microhardness for polished and machined samples, higher than for polished and non-oxidized condition. On the other hand, oxidation increased the roughness only for polished condition. The corrosion resistance was improved in all oxidized samples, and the best result was found to the intermediate roughness (Ra = 0.76 um), in a machined sample. The results demonstrated that thermal oxidation can be used to overcome machining limitations regarding corrosion resistance, achieving behavior even better than polished samples.

Performance Evaluation of Textured Inserts with MQL in Machining of PH Stainless Steel

This present investigation presents the method of texture fabrication and performance analysis of machinability of a textured and conventional Tungsten Carbide (WC) tool inserts. An endeavour has been taken to increase the performance of machining with different type of textured inserts. Texturing on the rake face of the inserts was fabricated using Wire Electrical Discharge Machine (WEDM). Experiments were conducted with different set of machining parameters on PH Stainless Steel (PHSS) with conventional and textured inserts and machinability analysis were evaluated. Tangential cutting force, surface roughness, and tool wear have been opted as the responses. Scanning electron microscope and optical microscopic image were used to evaluate the tool wear for different type of texture inserts. Based upon the outcomes attained, it is observed that the cutting tool with diamond shape texture exhibits the better performance in reduction of cutting force, tool wear and also improves the surface finish.

Optimization of machining parameters when machining beyond recommended cutting speed

Purpose Even though austenitic stainless steels have been extensively used in industries, owing to some of the characteristics of the material, its performance in machining is difficult to understand, in particular at high cutting speeds. There is no availability of dependable and in-depth studies pertinent to this matter. In this work, performance of AISI 304 austenitic stainless steel was studied in terms of surface roughness (Ra) and material removal rate (MRR) at high cutting speeds. Subsequently, parametric optimization and prediction for responses were carried out. Design/methodology/approach Turning operations were conducted using L9 orthogonal array and the outcomes were analyzed to attain optimal set of machining parameters for the responses using signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA). In the present work, the cutting speed values were considered beyond the recommended range as designated by tool manufacturers. Finally, multiple regression models were developed to predict responses. Findings From the results, 350 m/min was found to be a significant speed. The investigation reveals that even though the speeds are taken beyond the recommended values, the results are favorable. The optimal machining parameters values for surface quality obtained were cutting speed of 350 m/min, feed of 0.15 mm/rev and depth of cut of 2.0 mm. In case of MRR, the optimal values were: cutting speed of 400 m/min, feed of 0.25 mm/rev and depth of cut of 2.0 mm. It was found out that there was an improvement in Ra and MRR (around 15 and 4%) due to optimization. The results indicate that Pareto ANOVA is easier than S/N ratio. This revealed that the feed rate and depth of cut were mostly affected parameters for Ra and MRR. The developed models are capable of predicting the responses accurately. Practical implications The outcome of the work reveals that even though the speeds were taken beyond the recommended value, the results are favorable for manufacturing industries when the tool cost is considered insignificant. Originality/value No work was reported on machining of the chosen material beyond the recommended cutting speed. Moreover, it was observed from the past works that cutting speeds were limited to 100–300 m/min.

Role of tool rotational speed on the tribological characteristics of magnesium based AZ61A/TiC composite developed via friction stir processing route

Purpose: A new composite material was prepared and Different properties such as hardness and tribological behaviour of the fabricated metal matrix composite (MMC) was investigated and compared with the base AZ61A magnesium alloy. Design/methodology/approach: For the current research work, state-of-the-art technology, Friction stir processing (FSP) was performed to develop magnesium based AZ61A/TiC composite at optimized set of machine parameters. Findings: Increasing tool rotational speed ultimately leads in enhanced hardness, which further gives superior tribological properties as compared to base AZ61A alloy. Wear observations suggests a combination of abrasive and adhesive wear mechanism. Research limitations/implications: More microstructural and mechanical properties can be examined. Practical implications: The idea behind selecting AZ61A is mainly due to its increasing use in bicycle pedals and military equipment’s where at certain places it needs to encounter friction. In this current work, microhardness study and wear behaviour of AZ61A/TiC composite processed via FSP were examined. Originality/value: Paper is completely new and no work has been done till date considering this material and preparing composite with nanoparticles TiC.

Modeling and optimization for surface roughness and tool flank wear in hard turning of AISI 4340 steel (35 HRC) using TiSiN-TiAlN nanolaminate coated insert

PurposeThe present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.Design/methodology/approachIn this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.FindingsOptimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.Originality/valueThis work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

Parametric optimization in wire electric discharge machining of DC53 steel using gamma phase coated wire

DC53 is considered as superior steel grade by die and mold making industries as compared to D2 and D3 steel due to its excellent mechanical characteristics like high hardness, wear resistance and strength. DC53 steel finds applications in making stamping dies, punches and cutting tools. In wire electric discharge machining (WEDM) process, gamma phase coated wire offers superior machining performance over uncoated brass and zinc coated electrode wires. This work was performed to study the effect of machining variables like sparks on duration, sparks off duration, arc-on time, and servo voltage on response variables, namely kerf width (KW), cutting speed (CS), material removal rate (MRR), and surface roughness (Ra) for WEDM of DC53 steel. Taguchi L18 orthogonal array was used for experimental design. Analysis of variance showed that sparks on duration is the most significant factor affecting KW, CS, and MRR with percentage contribution of 48.25 %, 80.21 %, and 45.21 %, respectively. Optimal set of machining parameters resulted in 14.02 % improvement in MRR and 19.24 % reduction in workpiece surface roughness. Moreover, gamma phase coated wire was found to have 30–35 % faster cutting rate as compared to uncoated brass wire.

Machining and Characterization of Multidirectional Hybrid Silica Glass Fiber Reinforced Composite Laminates Using Abrasive Jet Machining

Machining of multidirectional Hybrid Fiber Reinforced Composite (HFRC) is a challenging task because of defects like matrix cracking, fiber pullouts, delamination, burr formation, and heat affected zones. The current study is focused on machining and characterization of in-house fabricated HFRC made of carbon and silica glass fiber using Abrasive Jet Machining (AJM). Machining of holes in fabricated multidirectional HFRC has been carried out using a set of input machining parameters -air pressure, abrasive grit size, and standoff distance. The output responses selected for the experiment are material removal rate, circularity, Depth Averaged Radial Overcut (DAROC), Taper Angle (TA), and Surface Roughness (Ra.) A novel technique has been contrived for measuring DAROC by using Computerized Tomography images. At the same time, the microstructure, surface roughness, and burr height of the machined samples are carried out using High-resolution Scanning Electron Microscope (HRSEM) images and 3D-profilometer images. Subsequently, the machining parameters are optimized using multi-objective GRA to strike a balance between the output parameters. The obtained results indicated the absence of defects like delamination, burr formation, heat affected zones in the machined samples. The outcome of the experiment corroborates the application suitability of the AJM process for machining multidirectional HFRC used in industrial and commercial products.