Machining Parameters Research Articles

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.

Assessment of the Functional Properties of the Surfaces of Ductile Cast Iron Parts

Modern technology allows ductile cast iron parts to be efficiently machined while ensuring a relatively long tool life. One of the basic indices describing the susceptibility of ductile cast irons to change in volume, shape, and dimensions under machining conditions is their machinability. Machinability can be expressed directly in terms of the values of basic quantities such as periodic cutting speed and roughness. At the same time, machinability is a relative quantity evaluated alternatively. This means that the machinability of ductile cast iron can be good, allowing high cutting speeds to be achieved, but it can also be poor, expressed in terms of poor surface quality. In the experimental research carried out, an attempt was made to determine the limit values of the cutting speed, beyond which one should not exceed, in order to increase the efficiency of the machining process. The surface roughness, unlike the periodic cutting speed, is a quantity defined in the product design documentation, so its limits must be observed. In addition to the usual indices of surface geometric texture, the research analysed alternative indices for determining the condition of surface geometric texture and the influence of periodic cutting speed on their values. In the conclusions, valuable recommendations are given for designers and technologists on the purpose and functionality of product surfaces and how to define them. Methods of specifying tribological characteristics, hydrophobic or hydrophilic properties, as well as the ability to retain fluids and maintain protective coatings of ductile cast iron parts after machining are described, for which relative values, depending on the machining parameters used, can vary from about 10 to even 30%.

Mechanical characterization and performance evaluation of twisted Jute-Kevlar hybrid composites with varied fiber orientations

Abstract Composite materials play a vital role in developing new materials in engineering and technology. Composites show how the properties of the matrix and reinforcement work together to create more robust, more rigid materials than would be possible from the individual components working alone. They consist of two or more component materials combined with notably dissimilar physical or chemical characteristics. Two categories of composite coupons have been developed in this research work: the first category (C1) is made up of jute twisted-Kevlar twisted jute fiber (0/90 degree), and the second category (C2) is made up of jute twisted-Kevlar twisted jute fiber (0/45 degree). The nano-silica is reinforced with the matrix with a weight percentage of 0%,5%,10% and 15%. The necessary interfacial adhesion and bonding between fiber layers have been accomplished using a 10:1 mixture of epoxy resin (LY556) and hardener (HY951). According to ASTM guidelines, the tensile, flexural, impact, double shear, wear, and hardness tests were carried out. This study uses two natural and synthetic fibers—twisted Jute and Kevlar fibers—as reinforcement. The Jute fibers have a high strength since they are high in cellulose. Efforts have been made to create a composite material with completely different twisted fiber orientations and to assess the mechanical properties of the composites. This involved various mechanical tests, analysis of wear surfaces, as well as DMA, DSC, FEA testing, and ultimately, the machining of the composites studied. The machining parameters used in waterjet machining have been carefully analyzed.

Impacts of Material and Machine on the Variation of Additively Manufactured Cooling Channels

Abstract While additive manufacturing (AM) can reduce component development time and create unique internal cooling designs, the AM process also introduces several sources of variability, such as the selection of machine, material, and print parameters. Because of these sources, wide variations in a part's geometrical accuracy and surface roughness levels can occur, especially for small internal cooling features that are difficult to post-process. This study investigates how the selection of machine and material in the AM process influences variations in surface quality and deviations from the design intent. Two microscale cooling geometries were tested: wavy channels and diamond-shaped pin fins. Test coupons were fabricated with five different additive machines and four materials using process parameters recommended by the manufacturers. The as-built geometry was measured non-destructively with computed tomography scans. To evaluate surface roughness, the coupons were cut open and examined using a laser microscope. Three distinct roughness profiles on the coupon surfaces were captured including upskin, downskin, and channel walls built at 90 deg to the build plate. Results indicated that both material and machine contribute to producing different roughness levels and very different surface morphologies. The roughness levels on the downskin surfaces are significantly greater than on the upskin or sidewall surfaces. Geometric analysis revealed that while the hydraulic diameter of all coupons was well captured, the pin cross section varied considerably. Along with characterizing the coupon surfaces, cooling performance was investigated by experimentally measuring friction factor and heat transfer. The variations in surface morphology as a function of material and machine resulted in heat transfer fluctuating by up to 50% between coupons featuring wavy channels and 26% for coupons with pin fin arrays. Increased arithmetic mean surface roughness led to increased heat transfer and pressure drop; however, a secondary driver in the performance of the wavy channels was found to be the roughness morphology, which could be described using the surface skewness and kurtosis.

Evaluating productivity characteristics of laser engineered net shaping titanium alloy

Advances in Additive Manufacturing (AM) technologies have made it possible to reduce the design and prototyping costs to a minimum especially for a low-productivity material like titanium. Titanium alloys are commonly and widely used alloys in the aerospace and biomedical sector due to their advantageous material properties. This paper is an evaluation study of factors affecting the productivity characteristics of Laser Engineered Net Shaping (LENS) titanium alloy (Ti-6Al-4 V) using face milling. Some of the productivity challenges associated with titanium such as rapid tool wear, poor surface finish, and high-power consumption are explored in this paper. All materials processed using AM face the same critical problem that the manufactured part requires a post machining since AM produces relatively poor surface finish. Machining trials are conducted using the combinations of machining parameters such as spindle speed of 800 and 1600 rev/min; feed rate of 50 and 100 mm/min; and a constant depth of cut of 1 mm, respectively. Titanium being a poor thermal conductivity material, the effect of coolant was investigated using wet/dry machining. Data related to the productivity factors and material behavior under a milling trial was recorded and analyzed. The obtained data from the trials include productivity factors such as Metal Removal Rate (MRR), power consumed, and the surface finish for each plate/trial. The power consumed in dry milling was observed to be lower than that in wet milling which is contrary to the observations from conventional wet milling. The paper concludes the trends observed for LENS titanium are opposed to the trends in conventional machining such as increasing cutting speed will result in lower cutting force and power consumed.

COMPARATIVE STUDY OF DIFFERENT CUTTING FLUIDS ON TOOL-WORK INTERFACE TEMPERATURE DURING TURNING OPERATION ON 6061 ALUMINUM ALLOY

This study investigated the impact of tool-work interface temperature during the turning process of a cylindrical workpiece made of 6061 aluminum alloy. The experiment utilized four different cutting fluids: palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), orange seed oil-based cutting fluid (OSOBCF), and mineral oil-based cutting fluid (MOBCF) using uncoated carbide cutting tool of grade H13A. Various machining parameters were considered, including spindle speed (180, 250, 355, 500 rpm), feed rate (0.105, 0.116, 1.4, 1.6 mm/rev), and depth of cut (0.5, 1.0, 1.5, 2.0 mm). The experimental design followed the Taguchi specified L16 (43) orthogonal array and was conducted on a Lathe Machine XL 400. To measure the tool-work interface temperature, an infrared thermometer (KM 690) was employed during the aluminum alloy machining process. Subsequently, a mathematical model for the tool-work interface temperature values was developed through regression analysis using Minitab 16. The significance of the chosen machining parameters and their respective levels on the tool-work interface temperature was determined using analysis of variance (ANOVA) and F-test. The findings indicated that machining under orange seed oil-based cutting fluid (OSOBCF) conditions resulted in a 9.02%, 16.4%, and 21.7% lower temperature at the tool-work interface compared to palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), and conventional oil-based cutting fluid (MOBCF), respectively. This suggests a potential for producing higher-quality products under orange seed oil cutting fluid conditions compared to other wet conditions.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Maximizing efficiency in C45 steel machining: an integrated AI-based approach to coated insert optimization

AbstractMachining involves the subtraction of the material from the sample workpiece to achieve the desired shape or surface. This versatile method is capable of producing a wide range of parts, varying from simple to intricate profiles. Coating materials are increasingly being utilized in tool inserts in the production industry owing to their superior thermal properties and wear resistance. The shielding of hard coatings, with thicknesses of only a few microns, enhances performance and durability. In this study, machining of C45 steel using distinct coated inserts was explored. The experimental trials employed PVD and CVD methods for coated tungsten carbide (WC) tools/inserts and PVD-coated cermet tools/inserts with different machining parameters. Performance metrics, such as the surface finish and reliability of the tool, were considered for the evaluation. The average tool life variation between the PVD-coated cermet and PVD-coated WC was 178.86%, and 30.11% between the PVD-coated cermet and CVD-coated WC at 1 mm DOC. ANOVA was performed using Response Surface Methodology to explore the influence of input parameters on output. The results indicate that the depth of machining and spindle speed significantly influence Ra, whereas spindle speed and type of tool insert have a considerable impact on the life span of the tool. The developed mathematical model for the prediction of tool life and Ra indicates its potential for performance forecasting during C45 steel machining. Grey relation analysis was employed to optimize the process parameters. Optimal results were achieved with a spindle speed of 400 m/min, 0.5 mm depth of cut, and cermet tool inserts. PVD-coated WC inserts performed better. ANFIS was applied for the prediction and optimization of the machining parameters.

Sensorless DFIG System Control via an Electromagnetic Torque Based on MRAS Speed Estimator

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink.

Temperature-dependent dynamics and dislocation behavior in nanoscale machining of FeCoNiCrAl high-entropy alloys: Molecular dynamics simulation

This study investigates the impact of machining parameters on forces, thermal dynamics, dislocation behavior, and crystalline structure changes in FeCoNiCrAl high-entropy alloys during nanoscale material removal using molecular dynamics (MD) simulations. Utilizing Embedded Atom Method (EAM) and Tersoff interaction potentials, simulations were performed at cryogenic (73 K) and room (293 K) temperatures. Novel findings reveal that at 73 K, cutting velocities below 200 m/s produced the highest forces along the [001] direction, whereas at 200 m/s, the peak force shifted to [100]. Increasing velocity decreased the force along [001], while [100] exhibited an inverse relationship. At 293 K, the force remained highest along [001] across all velocities. Notably, forces at 73 K were 1.82, 1.79, and 1.58 times higher than at 293 K for velocities of 100, 200, and 300 m/s, respectively. Dislocation density, particularly 1/6<112> (Shockley) dislocations, peaked under all machining conditions, with a slight initial decrease at 293 K before a significant drop with higher cutting speeds. At 293 K and a cutting speed of 100 m/s, dislocation densities for depths of 5, 10, 15, and 20 Å were approximately 1.04, 1.54, 1.17, and 1.14 times greater than those at 73 K, respectively.

Impact of Drawing Machine Parameters on Force and Energy Consumption in Borosilicate Glass Tube Production

Middle borosilicate glass tubes are critical materials for medical packaging, with the drawing machine playing a pivotal role in their production process. However, the effects of the drawing wheel’s motion parameters on the glass tube remain underexplored. Therefore, based on the finite element method simulation and validation experiment, the effects of experimental factors (angle of upper and lower drawing wheel, AUD; friction factor of glass and rubber, FFGR; and distance of upper and lower drawing wheel, DUD) on experimental indexes (maximum effective stress of glass tube, MES; rotational speed of glass tube, RSG; maximum and average forward driving force, MFDF and AFDF; maximum and average rotational driving force, MRDF and ARDF; total energy consumption and power, TEC and TPD; maximum and average forward driving power, MFDP and AFDP; and maximum and average rotational driving power, MRDP and ARDP) were analyzed. The results indicated that compared to FFGR and AUD, the influence of DUD on experimental indexes was the highest. The positive influence of AUD on RSG, ARDP, and MRDP, the positive influence of FFGR and the negative influence of DUD on MRDF, ARDF, TEC, TPD, ARDP, and MRDP were found, respectively. These findings will provide a theoretical reference for the optimization of drawing machines.

Performance evaluation of machine learning algorithms in predicting machining responses of superalloys

This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN)—to predict machining responses during the milling of Inconel 690, a superalloy known for its exceptional mechanical properties and oxidation resistance. Machining Inconel 690 presents significant challenges due to its toughness and work-hardening tendencies, which can lead to rapid tool wear and poor surface finish. Traditional optimization methods often rely on empirical models and trial-and-error approaches, which are time-consuming and costly. In contrast, machine learning techniques can effectively model complex, nonlinear relationships between machining parameters and performance outcomes, such as surface roughness, cutting force, and cutting temperature. This study employs statistical metrics, including Root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), to determine the predictive performance of the models. The results show that the GEP model achieved an R2 ranging from 0.944 572 to 0.992 999, with an RMSE between 0.015 527% and 0.694 523% and a MAPE ranging from 1.452 397% to 4.947 892%. ANFIS and ANN also demonstrated strong predictive capabilities, although GEP outperformed them. The importance of this study lies in its demonstration of advanced AI techniques as effective tools for optimizing machining processes, ultimately contributing to improved efficiency and quality in manufacturing superalloys.

Design and application of visualized quick-change molds

Abstract In the process of plastic injection moulding, the flow behaviour of plastic within the mould is a critical factor in product formation. Achieving stable mass production requires iterative experimentation to adjust mould and machine parameters. This process incurs significant material, time, and labour costs. Therefore, this study focuses on the design of mould visualization and rapid mould exchange. By visually observing the plastic flow during product formation, this study can make preliminary and intuitive judgments for real-time adjustments. Leveraging the convenience of rapid mould exchange, observations and experiments on multiple product iterations are made, effectively reducing the time and cost associated with mould replacement.

Effect of a quantum thermal machine on an entangled atomic system

In this contribution, we examine the effect of a quantum thermal machine on the behavior of two entangled atoms. It is shown that sudden changes (death/rebirth) in entanglement are predicted when increasing either the average rate of interaction coupling or the relative coupling. The maximum bounds of re-birthed entanglement decrease as machine parameters increase. Entanglement survival time can be increased as one reduces the average rate of interaction coupling. Due to the interaction, there will be an exchange of energies from the baths which may be a cool or hot flux. Therefore, the machine parameters can be used as controllers to switch between heating and refrigeration processes.

An Experimental Investigation of Surface Roughness And Cutting Forces On GFRP

In the current study, the emphasis was on finding experimental results on machinability of Glass fibre reinforced plastic composite with milling machine. Study is carried out with objectives of examination of cutting parameter on GFRP materials. Speed , feed, depth of cut &amp; number of flute are consider for analyzing effect on surface finish &amp; cutting forces of composites. Design of experiment is carry out by Taguchi’s L18 orthogonal array. To find out more about the machining parameters on GFRP composite using end mill of cemented carbide, an analysis of variances (ANOVA) utilizing Minitab 15 software was finally carried out.

Batch-sizing and machinability data systems for milling operations: An optimal sustainable cost of quality approach

Nowadays, manufacturers make every effort to achieve a higher quality of their products at an attractive cost. With all the introduced legislation and incentives in the developed world to address global warming, machining shops in the West also strive to cut greenhouse emissions. This article offers an optimal approach to the micro Computer-Aided Process Planning (CAPP) problem to optimize the internal quality cost and buffering effect while keeping the environmental impact low. To optimize the machining parameters, the mathematical model is developed for different milling operations, face, side, and peripheral. cutting speed, feed rate, axial depth of cut, radial depth of cut, nose radius, and batch sizing while maximizing profit and meeting customer demand. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved using Classical Constrained Nonlinear Optimization (CCNO) and Genetic Algorithms (GAs). Surface roughness, used as a metric to evaluate the desired quality level of a finished machined part type, is modeled as a Gaussian random variable to model the surface roughness of the machined part utilizing a cumulative normal distribution. The ratio of rework and scrap is calculated in terms of the surface roughness of the machined part shifting away from the target and exceeding upper and lower specification limits. The internal failure cost model, addressing both scrap and rework, is developed based on Taguchi’s quadratic loss function. CCNO is employed to validate the results obtained by GAs, relaxing the lot-sizing integrality constraint and, thus, the convexity of the produced relaxed model. An iterative method employing a developed multi-regression model is used to solve for the expended power consumption (an inherent highly nonlinear environmental criterion of the developed model) within both GAs and CCNO. This study reveals that the machining parameters substantially impact the cost components of the objective function as well as the scrap and rework quantities. A stringent quality cost target can force the model to optimize the feed rate and nose radius to minimize the internal failure quality cost while improving the environmental impact, including direct and indirect power consumption and CO2 emissions considerations.

Design of Prototype Phantom to Measure Quality Control for Conventional X-ray Machine Using Quantitative Image Parameters

A variety of phantoms that measure image quality parameters are commercially expensive and often complicated to use as they measure only one parameter of a particular x-ray machine, which leads to the difficulty of measuring the quality of x-ray image and thus increase the risk of radiation to the patient and society. The main objective of this study was to design a prototype phantom to measure quality control for conventional x-ray machine using quantitative image parameters. In this study, 5 conventional x-ray machines were investigated using wire phantom designed by the researcher, which consisted of variable thicknesses used to test variable exposure factors. This study introduced a more reliable method of measuring the resolution, which is modulation transfer function, which gives a complete description of the resolution instead of using full width at half maximum or the visibility method, which is more qualitative where modulation transfer function is a real quantitative method, by designing a prototype phantom that consisted of 5 wires with different thicknesses and kVp for 5 x-ray units and assessing the diagnostic x-ray machine resolution by the optimum kV found relative to machine type. This study showed that the one with the best machine resolution was Shimadzu (2011) for Khartoum Emergency Hospital, which had a high resolution of 97% at 46 kV for thickness of 1.4 mm compared with Toshiba 2011 for Almotkamil Hospital, which had 92% at 46 kVp. Also, the result showed that for the 2 types of x-ray machines the x-ray tubes do not produce the same exposure, and the output decreased with age of the x-ray unit; also, the resolution reduced when the thickness of the wires decreased.

Study on the forging process of artificial bone plates of SUS 316L

Abstract SUS 316L stainless steel, valued for its strength and biocompatibility, is a popular choice for artificial bone plates. Forged SUS 316L plates offer advantages like enhanced strength, improved fatigue resistance, reduced material waste, and increased production efficiency. Consequently, the forging technology and die design for SUS 316L is crucial in the manufacturing of high quality artificial bone plates. Finite element analysis simulates the forging process of SUS 316L bone plates, guiding the selection of the appropriate forging machine, die design, and process parameters based on the resulting forging load, stress distribution, and filling condition. Ultimately, this approach facilitates the creation of high-quality SUS 316L bone plates with precise dimensional accuracy.

Co-Kriging model-based multi-fidelity regression for refining milling stability predictions of variable tool overhang length using limited experimental data

Determining chatter-free machining parameters is critical in process planning since chatter vibrations significantly affect machining quality and production efficiency. Stability lobe diagram is essential in selecting chatter-free machining parameters, but its predictions confront challenges in both theoretical accuracy and experimental efficiency. To address this, the Co-Kriging modelling is introduced to predict the tool overhang length-dependent stability limits with limited experiments. First, tool tip dynamics and cutting force coefficients of a source overhang length are roughly identified to obtain sufficient low-fidelity (LF) theoretical stability limits. The entropy-based sampling and affine transformation are used to sequentially obtain high-fidelity (HF) experimental stability limits and update LF stability limits. HF and updated LF datasets are combined to train a multi-fidelity (MF) Co-Kriging model. For a target overhang length, MF stability limits of the source overhang length serve as LF data, and a minimum mean absolute percentage error (MAPE)-based sampling guides the acquisition of HF stability limits. Combining HF data and LF data processed by affine transformation to train a target Co-Kriging model. Case studies reveal that the proposed method exhibits lower MAPE values and significantly outperforms other comparison methods in predicting experimental stability limits, particularly when confronted with limited experimental data.

AISI D2 steel machining and manufacturing process optimization for tooling applications in biomedical industry

Tool steels such as AISI D2 are famous in the manufacturing industry because of their engineering applications. The precise interplay of improved hardness and toughness makes the machining of complex geometries challenging through conventional machining options. Therefore, non-conventional processes such as wire electric discharge machining (WEDM) are preferred because of their simultaneous machining and surface modification actions. To investigate the complex process parameters and their sensitivity, material removal rate (MRR) and cutting surface roughness (SR) are the corresponding performance measure characteristics for WEDM machining on AISI D2 tool steel. The L18 mixed-level Taguchi technique has been used for obtaining combinations of experiments on two levels of thickness and three levels of other remaining factors (21 × 33). Analysis of variance (ANOVA) and signal-to-noise ratio have been applied to measure the magnitude of effects on each control factor, to investigate the optimum levels of input process parameters on machining characteristics, and to identify their significance. ANOVA analysis revealed that, for both responses, all main effect variables are highly significant, with p-values equal to zero. Moreover, the coefficient of determination (R2) value in the ANOVA findings for both responses is above 97%, indicating the high reliability of the model. In addition, the composite desirability (dG) is considered to maximize MRR and minimize the SR during WEDM of D2; the better combination of optimum levels of machining parameters (T = 25.4 mm, Pon = 4 µs, SV = 95 V, and WT = 5 kg-f) has a dG of 0.5614.