Conventional Machining Processes Research Articles

Experimental investigation of electrochemical machining for involute internal spline using cathode working teeth sheet with circumferential vibration

Manufacturing internal splines with complex structures in high-hardness materials poses challenges in conventional machining processes. This paper introduced a new electrochemical machining (ECM) method using an assembled tool cathode with a cathode working teeth sheet with circumferential vibration for efficient and stable shaping of involute internal splines of intricate high-hardness components. A detailed design of the assembled tool cathode is presented, accompanied by flow field simulations analysing electrolyte distribution during circumferential vibration. A specific experimental system, fixture, and a used electrolyte filter module were constructed for spline ECM experiments. The results show that with the cathode feed rate of 2.7 mm/min, and the circumferential vibration frequency of 80 Hz, the involute internal spline profiles were successfully shaped with higher precision and efficiency with less appropriate allowance.

Diamond machining of additively manufactured Ti6Al4V ELI: Newer mode of material removal challenging the current simulation tools

Single point diamond machining (SPDM) produces smooth machined surfaces that other production methods cannot match. While the mechanics of machining of cast alloys with SPDM is well-explored, the realm of SPDM for additively manufactured parts remains largely uncharted. This work reveals new insights into the surface generation process of an additively manufactured titanium alloy, specifically, a Ti6Al4V Extra Low Interstitials (ELI) alloy workpiece. Our examination of the chip morphology unveiled a distinct mode of chip removal, previously unrecorded in existing literature. During SPDM of additively made Ti6Al4V ELI workpiece, identification of numerous pores and discontinuities in the chips flowing on the tool rake face, indicating periodic intermittent cracking during the material's plastic flow was seen. To examine this phenomenon, a finite element analysis (FEA) model was developed. While the FEA model can well explain the machining mechanics and chip morphology of SPDM of cast Ti6Al4V ELI reported in the literature, it failed to describe the chip morphology that are obtained during machining of additively made workpiece in this work. This disparity underscores the need for innovative simulation approaches tailored for additively manufactured components. The experimental observations in this study highlight a unique form of chip formation in contrast to conventional Ti6Al4V alloy machining processes. At lower feeds, there was a presence of short, discontinuous chip formation with tearing at the outer periphery. Conversely, at higher feeds, a long, continuous ribbon-like chip formation was observed. In addition, some typical additive manufacturing defects appear on the machined surface and chips. Through optimisation of the SPDT parameters, a surface roughness (Ra) value of about 11.8 nm was achieved on additively manufactured Ti6Al4V ELI workpiece. This work provides a fresh perspective on the mechanics of SPDM for additively manufactured components, offering a stepping stone for subsequent studies.

A Concise Review on Materials for Injection Moulds and Their Conventional and Non-Conventional Machining Processes

Injection moulds are crucial to produce plastic and lightweight metal components. One primary associated challenge is that these may suffer from different types of failures, such as wear and/or cracking, due to the extreme temperatures (T), thermal cycles, and pressures involved in the production process. According to the intended geometry and respective needs, mould manufacturing can be performed with conventional or non-conventional processes. This work focuses on three foremost alloys: AMPCO® (CuBe alloy), INVAR-36® (Fe-Ni alloys, Fe-Ni36), and heat-treated (HT) steels. An insight into the manufacturing processes’ limitations of these kinds of materials will be made, and solutions for more effective machining will be presented by reviewing other published works from the last decade. The main objective is to provide a concise and comprehensive review of the most recent investigations of these alloys’ manufacturing processes and present the machinability challenges from other authors, discovering the prospects for future work and contributing to the endeavours of the injection mould industry. This review highlighted the imperative for more extensive research and development in targeted domains.

Effect of grinding parameters and coolants on grindability of Ti grade 9 alloy

For many years, surface roughness has drawn considerable attention, and it is a crucial design element in a variety of circumstances, including components sensitive to fatigue stresses, precision fittings, fastener holes, and aesthetic demands. Grinding the is best conventional machining process to achieve a better surface finish on a given component. Titanium 3Al-2.5 V or Ti grade 9 alloy is primarily utilized in the biomedical, aerospace, and automotive sectors. But because of its low heat conductivity, high chemical reactivity and rapid work hardening, this alloy is known as a difficult-to-cut material. In grinding, the abrasives have low thermal conductivity and small chip sizes, which in turn more heat to being focused in the cutting zone. This heat harms the surface of the workpiece. Therefore, it's crucial to regulate the quantity of heat that the workpiece receives.In this context, this work examines the surface quality of the Titanium 3Al-2.5 V alloy following surface grinding with an Al2O3 wheel under various grinding settings, including surface roughness (Ra), microhardness and material removal rate (MRR) from the workpiece. Techniques for delivering coolant conventionally (flood coolant), dry and using MQL were used and grinding performance were evaluated. The findings demonstrated that the Ra varied from 0.32 to 0.594 µm and hardness on the machine surface varied from 61.7 to 72 HRC. The MRR varied from 0.09 to 0.42 g/minute for the changes in depth of cut, table feed and coolants. Overall, the MQL has produced a good surface finish, high MRR and moderate hardness on the workpiece.

Experimental Study on Pulse Electrochemical Machining of Inconel 718 By IJISRT

The properties of the Inconel 718 super alloy are used in the manufacturing of aircraft components, due to high hardness and toughness machining of such alloy by conventional machining process is difficult. To overcome this, nonconventional techniques are used, from which electrochemical machining (ECM) is good alternative. Electrochemical Machining (ECM) is a nonconventional manufacturing process widely employed in various industries due to its precision in shaping complex, detailed components. This study focuses on exploring and optimizing the key machining parameters in ECM to enhance machining efficiency and surface finish quality of Inconel 718. The research methodology involves a systematic experimental investigation, employing a series of controlled tests on an ECM machine. The controllable process parameters selected which are current, voltage and duty cycle. The aim is to comprehend the nuanced influence of these variables on material removal rate (MRR) and overcut and to investigate optimum level of process parameter. The analysis shows that the duty cycle and current are most influencing factor for controlling depth of overcut and for improving MRR, but excessive high duty cycle can be counterproductive which results in increased heat generation because continuous current flow generates heat in the electrolyte and work piece.

Ultrasonic vibration cutting of advanced aerospace materials: a critical review of in-service functional performance

PurposeUnconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.Design/methodology/approachThe surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.FindingsTo address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.Originality/valueThis study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

A comprehensive review on the machining and joining characteristics of natural fiber‐reinforced polymeric composites

AbstractNatural fiber composites are gaining interest in many industries, particularly the automotive and aerospace industries. One of the critical secondary activities required to produce a near‐perfectly shaped product is machining. The majority of product components had to be assembled before they could be used. This article reviews the various conventional and non‐conventional machining processes and its influential parameters investigated by various researchers. Non‐traditional machining is now proving to be a better choice than traditional machining. Non‐traditional machining has several advantages, including the absence of thrust force, chatter or vibration, minimal tool wear, and lower frictional heat. Delamination is the major issue occurring in the conventional machining processes like drilling. Researchers are more focused on the non‐conventional machining processes like abrasive water jet machining. Joining of natural fiber reinforced composites is another aspect discussed in this article. Adhesive joining is the most preferable joining method in the natural composites. The joining technique is also influenced by the intricacy of the materials to be bonded. Microwave joining and induction joining processes are the new methods of natural composites joining. This review also covers the various joining processes in the natural reinforced composite materials.

Process parameters optimization and performance analysis of micro-complex geometry machining on Ti6Al4V

AbstractThe aeronautical industry relies on high-performance equipment that demands materials with exceptional engineering attributes, such as the Ti6Al4V alloy. However, the complexity of the parts used in these applications presents challenges related to stack-up tolerances during assembly. Therefore, conventional machining processes are not economically viable due to their two-step nature solution (machining and surface treatment) and their inability to achieve the required accuracy level, particularly given the alloy’s hard-to-cut nature. In this matter, wire electric discharge machining (WEDM) emerges as an attractive alternative for fabricating complex geometries. This research focuses on evaluating the potential of the WEDM process for machining micro-complex profiles. A comprehensive range of process parameters, including servo voltage (Vs), pulse on time (Ton), pulse off time (Toff), and wire speed (Ws), are studied using a Taguchi-based design of experiments. The results are analyzed through parametric significance analysis, parametric control analysis, surface morphological analysis using scanning electron microscopy, and modified layer analysis. Additionally, both mono-objective and multi-objective process optimization techniques are employed to achieve superior accuracy and speed. The findings indicate that Ton and Ws have the most significant influence on both cutting speed and spark gap, whereas Vs and Toff play a crucial role in determining the accuracy index. In addition, adequate flushing, reduced wire speed (economically viable), and stability of the spark are recommended to attain a lower spark gap and higher accuracy. The confirmatory experiments show that the optimal parametric conditions of Vs = 60 V, Toff = 30 μs, Ton = 8 μs, and Ws = 6 mm/s provide the highest speed of 3.4 mm/min, minimum spark gap of 0.344 mm, and accuracy index of 98.72%. The findings will contribute to enhancing manufacturing efficiency, precision, and cost-effectiveness in the aeronautical industry, meeting the demand for high-quality components with tight tolerances.

Erosion rate investigation and multi-objective optimisation on electrical discharge machining of Ti64 using deep cryo-treated electrodes

Due to its inherent properties, Ti64 has set many challenges in industries during its machining using conventional machining processes. Traditional machining of Ti64 is not a feasible option considering the economic and environmental perspectives. Non-conventional techniques like electrical discharge machining (EDM) are promising methods for machining Ti64 despite challenges like higher tool wear and lower machining rates. The current paper investigated the erosion rates while machining Ti64 using deep cryo-treated electrodes. The peak current (Ip), pulse on time (Ton) and gap voltage (Vg) were used as control parameters while investigating erosion rates, viz., Material Removal Rate (MRR) and Tool Wear Rate (TWR). Experiments were planned according to the RSM-based Central Composite Design (CCD). Considering the multi-objective nature of the current problem, Grey Relational Analysis (GRA) has been incorporated to find a single parametric condition that will satisfy both MRR and TWR. Peak current, [Formula: see text][Formula: see text]A, Pulse On time, [Formula: see text][Formula: see text][Formula: see text]s and Gap voltage, [Formula: see text][Formula: see text]V was found to be the optimal setting giving [Formula: see text][Formula: see text]mm3/min and [Formula: see text][Formula: see text]mm3/min. Furthermore, topographical analysis of the EDMed surfaces has been performed using SEM, XRD and 3D optical profilometry techniques to characterise the surface and to investigate the possibility of other intermetallic compounds formed on the machined surface.

A New Architecture Paradigm for Tool Wear Prediction during AISI 9840 Drilling Operation

In conventional machining processes based on the chip removal mechanism, the progressive wear of the tool determines a change of the geometric characteristics of the cutting edge. Tool wear is a complex phenomenon and related tool life depends on several factors, such as cutting parameters, lubrication, and tool-workpiece relative trajectories. Tool wear progression affects the quality of the machined parts, making the tool replacement necessary even before its breakage. Moreover, in industrial practice, tool replacement cannot depend on the subjectivity of the operator, thus, the definition of an optimized strategy for cutting tool wear monitoring before tool failure is mandatory. This work compares Random Forest and Neural Network models for predicting tool wear in drilling. For the development of predictive models, tool life tests were performed by drilling through holes on AISI 9840 steel parts, with a coated tungsten carbide drill of 8 mm of diameter and by using constant cutting parameters. Flank wear of the tool was monitored. A set of statistical features computed from the vibration signals, the acoustic emission signals, the power signals and the torque signals constitute the input of the algorithms. The classification accuracy was 88% and 91% for Neural Network and Random Forest models respectively. In order to correctly define the tool replacement policy during production, the developed Random Forest model has been implemented in an industry, through production management software, achieving promising results.

Multi-Objective Optimization in WEDM Process of Titanium Alloy (Ti-3Al-2.5V-2.0WC) using Desirability Approach

Recently, Titanium alloy (Ti-3Al-2.5V) reinforced with Tungsten Carbide (Ti-3Al-2.5V-2.0WC) developed using the microwave sintering process has plays a major role in mining industry. It has very good mechanical property and corrosion resistance. Due to its better strength and high melting point, it is difficult to machine in conventional machining process. Therefore, WEDM process give the solution to cut this high strength material in an effective manner. In this present work, the WEDM input variables such as Servo Voltage (SV), Pulse Time - On (PT-ON), Pulse Time – Off (PT-OFF) and Cutting Speed Percentage (CSP) are considered. L09 Orthogonal Array Matrix (OAM) is considered for experimental work. The output variables like Material Removal Rate (MRR) and Surface Roughness (SR) are considered to obtained the quality machining process. Multi objective optimization technique like Taguchi-Desirability Approach method is implemented to find the optimum input variables. The Improvement of MRR from 0.05978 g/min to 0.06420 g/min and SR from 3.812 μm to 3.452 μm are obtained using Desirability Approach.

Machining of EN19 steel using cryogenically cooled electrode material in electric discharge machining

The machining of high-strength alloys like EN19 steel poses significant challenges due to their inherent hardness and susceptibility to surface defects during conventional machining processes. In this study, we explore a pragmatic approach to address these challenges by investigating the implementation of cryogenically cooled electrode materials in electric discharge machining (EDM) for EN19 steel. Specifically, cryogenic treatment is applied to copper electrodes with varying soaking durations of 12 and 24 h. The research considers input process parameters, including peak current ( IP), pulse on time ( Ton), flushing pressure, and soaking duration. Material removal rates (MRR), electrode wear rate (EWR), and surface roughness (SR) are considered as the response parameters. To optimize these input parameters, the study employs the VIKOR technique. Improving the in-depth analysis, our investigation incorporates using COMSOL Multiphysics simulation to intricately model the mechanical aspects of single-spark EDM, mainly focusing on the thermal and structural characteristics of both electrodes and the workpiece. The simulation lends strong support to the experimental findings in relation to MRR and EWR. The experimental data demonstrates notable enhancements after a 24 h cryogenic soaking period for copper electrodes. Specifically, we observed a remarkable 77.36% increase in electrical conductivity, a substantial 20% augmentation in MRR, a significant 39% reduction in EWR, and a noteworthy 9.8% decrease in SR. The meticulous synergy of experimental exploration, optimization, and simulation offers a transformative outlook toward augmenting machining efficacy and prolonging electrode longevity, particularly in the context of EDM.

State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes

AbstractIn conventional machining, one of the main tasks is to ensure that the required dimensional accuracy and the desired surface quality of a part or product meet the customer needs. The successful accomplishment of these parameters in milling, turning, milling, drilling, grinding and other conventional machining operations directly depends on the current level of tool wear and cutting edge conditions. One of the proven non-contact methods of tool condition monitoring (TCM) is measuring systems based on image processing technologies that allow assessing the current state of the machined surface and the quantitative indicators of tool wear. This review article discusses image processing for tool monitoring in the conventional machining domain. For the first time, a comprehensive review of the application of image processing techniques for tool condition monitoring in conventional machining processes is provided for both direct and indirect measurement methods. Here we consider both applications of image processing in conventional machining processes, for the analysis of the tool cutting edge and for the control of surface images after machining. It also discusses the predominance, limitations and perspectives on the application of imaging systems as a tool for controlling machining processes. The perspectives and trends in the development of image processing in Industry 4.0, namely artificial intelligence, smart manufacturing, the internet of things and big data, were also elaborated and analysed.

Application of Smart Strategies for Sustainable Manufacturing of Conventional Machining Process: A Review

Application of Smart Strategies for Sustainable Manufacturing of Conventional Machining Process: A Review

Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm

Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm

Evaluation of Machinability Performance MRR and SR of AA7050-7.5% B4C-T6 Composite through EDM

The Al-B4C composites are exclusively applicable in the structural components of automobiles and aerospace. It is also termed a hard-to-cut material. Hence, the Machining of such materials with firm design and close tolerance is a key concern in the present era of the manufacturing world, particularly through the conventional machining process. The prime motive of this paper is to evaluate the Machinability performance in terms of MRR and SR of the AA7050-7.5% B4C-T6 Composite using die-sinking EDM. The Taguchi L9 DOE was employed for the execution of experiments to assess the machinability performance of the composite in terms of MRR and Ra, with the simultaneous effect of input parameters such as Ip, Ton, and sand parking voltage (Sv). Further, the parameters were optimized using the Taguchi Analysis. ANOVA analysis was performed to evaluate the significance and contribution of the input parameters toward performance measures.

Life Cycle Assessment of Cold Spray Additive Manufacturing and Conventional Machining of Aluminum Alloy Flange

Cold spray additive manufacturing (CSAM) is generally used to repair worn components and build complex on-demand parts by depositing metal powder layer-wise using compressed air. Previous studies on CSAM were focused on printing parameters, materials properties, and printed part mechanical performance. However, the energy consumption and environmental impacts of CSAM processes have not yet been investigated, which are essential factors for sustainable manufacturing. This study aims to investigate the carbon footprint of the CSAM process and compare it with conventional machining processes and other additive manufacturing. The life cycle assessment methodology was followed to calculate the carbon footprint of a pipe flange, considering rod or tube as a feedstock. Results revealed that the machined flange from the tube had the lowest CO2-eq emissions of 31 kg CO2-eq due to low rough machining energy consumption and scrap production, compared to the machined flange from a rod and a printed flange from powder. Moreover, the life cycle carbon emissions increased by 8% and 19% in case of the printed and machined flanges, with uncertainties of 4% and 9%, respectively, when changing feedstock CO2 emissions. From a regional perspective, the CSAM process was responsible for the lowest CO2-eq emissions in Tasmania and South Australia.

Dry machining of alloy steels – A review

Dry machining has been used as an alternative method in place of the conventional machining process as the usage of cutting fluid has many side effects and adds more cost to the manufacturing process. Handling and recycling of chips is the main feature of the dry machining process. The heat generated and the tool life are the major concerns in dry machining. There is an alternative method called the minimum quantity lubrication technique where the cutting fluid is sprayed in the form of a mist. Alloy steels are extensively used for the manufacturing of various components in automobile, aircraft, military, and medical industries. It’s crucial to study the dry machining of alloy steels to achieve high-quality finished products at economical costs. In turning operation cutting speed, feed rate, depth of cut and cutting environment are the controllable machining parameters which affect the outputs like surface roughness, tool wear, tool life, MRR, power consumption, etc. In practice when machining is done there are some desired characteristics for the output parameters, that can’t be achieved with some random input parameters combination, the combination of input parameters where desired characteristic of output parameters is achieved is called optimal parameter condition. Optimal conditions for the machining process can be found using the Taguchi technique based on means method or S/N ratio method and ANOVA is used to find the significance of input dimension on outputs. This optimal condition can be found for each individual output parameter or else for all output parameters at once which is called multi-objective optimization. Machine learning model can be developed using experimental data for prediction. The model will be trained based on the training data that has been provided to it. Machine learning models can avoid the waste of time of manual experimentation and directly predict the values.

Optimization of process conditions of Powder Mixed Electric Discharge Machining of Nickel based Superalloy using Grey Wolf Optimizer algorithm

The Powder-Mixed Electric Discharge Machining (PMEDM) process has been drawing the attention of researchers since it could successfully overcome some of the limitations of the conventional Electric Discharge Machining (EDM) process. There was extensive research carried out on the PMEDM process to explore various possibilities for enhancing the process output. In the present work, an experimental study has been undertaken to investigate the influence of three conductive powders: zinc, cobalt, and molybdenum, while machining Nimonic 75, a nickel-based superalloy, by the EDM process. The work material considered in the study has been widely used in the aerospace industries and in applications involving higher temperatures. The experiments were carried out based on the Response Surface Methodology using the Box–Behnken design. Using the experimental data, regression equations were formed to predict the outcome of the process, which are Material Removal Rate (MRR), Electrode Wear Ratio (EWR), and Surface Roughness (SR). This work attempted to optimize the EDM machining parameters using the Grey Wolf Optimizer (GWO) algorithm combined with the Utility concept. The optimum conditions obtained by the GWO algorithm were: Powder – Molybdenum; Concentration of powder – 5.4 g/L; Discharge current – 4 A; Duty factor – 0.8.

The influence of the hardened matrix machining parameters on the process and production cost of the polypropylene cutting jacket

The turning process is a type of conventional machining process that uses tools with defined geometry, intended to obtain surfaces per revolution with the aid of one or more single-cutting tools. The study focuses on the evaluation of the machining process through the experimental analysis in the machining process of the polypropylene sheet cutting sleeve, for the optimization of the process. The research had been carried out through the analysis of two processes, one in the existing process with the necessary machining parameters and the other by experiment analysis, for testing hypotheses, which establish relationships between cause and effect between variables. It was found that the conventional machining took a total processing time of 116 h, while the new machining design proposed in this study reduced the production time by 61%.