Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning.

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 µs, Toff = 12 µs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems.

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

A comparative evaluation of nature-inspired algorithms for feature selection problems.

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Mechanical property analysis and dry sand three-body abrasive wear behaviour of AZ31/ZrO2 composites produced by stir casting.

An experimental study of three body abrasive wear behaviour of AZ31/15 vol.% Zirconium dioxide (ZrO2) reinforced composites prepared by stir casting has been carried out. Microstructural analysis of the developed composites was carried out and found out that the microstructure of the composites revealed a uniform distribution of ZrO2 particles with refinement in the grain size of the matrix from 70 to 20 µm. The alterations in the microstructure led to an enhancement in both hardness (68-104 HV) and tensile strength (156-236 MPa) due to Orowan strengthening, quench hardening effect and better bonding. Response surface methodology was applied to formulate the three-body abrasive wear test characteristics such as load, speed, and time. Three body abrasive test results were utilized to generate surface graphs for different combinations of wear test parameters revealed an increase in specific wear rate. The specific wear rate was observed to increase with increase in speed up to a certain level and then started to decrease. The lowest possible specific wear rate was obtained for an optimized load of 20 N and a speed of 190 ms-1. Scanning electron microscopic examination of wear-tested samples showed higher specific wear rate at higher loads with predominantly abrasion type material removal. In conclusion, this study makes a substantial contribution to the field by elucidating the complex relationships among microstructure, mechanical properties, and the three-body abrasive wear behavior of AZ31/ZrO2 composites. The determination of optimal wear conditions and the insights gained into wear mechanisms provide valuable information for designing materials, implementing engineering solutions, and advancing the creation of wear-resistant components across a range of industries.

Microstructure, hardness and wear behavior of ZrC particle reinforced AZ31 surface composites synthesized via friction stir processing.

Dry sliding wear behaviour of friction stir processed (FSP) AZ31 and AZ31/ZrC particles (5, 10, and 15 vol%) reinforced surface composite was investigated at different sliding speeds and loads. The samples were tested using a pin-on-disc apparatus with EN31 steel as the counter body to determine the role of FSP and ZrC reinforcement on the microstructure, hardness, and wear behaviour of AZ31. Base metal AZ31 alloy exhibits a hardness of 60 HV, whereas the 15 vol% ZrC-reinforced composites had the highest hardness of 108 HV. It was also identified that 15 vol% ZrC-reinforced composites exhibited lowest wear rate and friction coefficient under all testing conditions. Abrasion, delamination, oxidation, material softening, and plastic deformation are the primary wear mechanisms viewed from the wear tracks of the samples. Higher volume fraction of ZrC particles exhibited better wear resistance at all speeds and loads than AZ31 alloy. A wear map has been generated for different material compositions and wear conditions to identify the main wear mechanisms easily.

Experimental Investigation into the Influence of the Process Parameters of Wire Electric Discharge Machining Using Nimonic-263 Superalloy.

Nimonic alloy is difficult to machine using traditional metal cutting techniques because of the high cutting forces required, poor surface integrity, and tool wear. Wire electrical discharge machining (WEDM) is used in a number of sectors to precisely machine complex forms of nickel-based alloy in order to attempt to overcome these challenges and provide high-quality products. The Taguchi-based design of experiments is utilized in this study to conduct the tests and analyses. The gap voltage (GV), pulse-on time (Ton), pulse-off time (Toff), and wire feed (WF), are considered as the variable process factors. GRA is used for the WEDM process optimization for the Nimonic-263 superalloy, which has multiple performance qualities including the material removal rate (MRR), surface roughness (SR), and kerf width (KW). ANOVA analysis was conducted to determine the factors' importance and influence on the output variables. Multi objective optimization techniques were employed for assessing the machining performances of WEDM using GRA. The ideal input parameter combinations were determined to be a gap voltage (GV) of 40 V, a pulse-on time (Ton) of 8 µs, a pulse-off time (Toff) of 16 µs, and a wire feed (WF) of 4 m/min. A material removal rate of 8.238 mm3/min, surface roughness of 2.83 µm, and kerf width of 0.343 mm were obtained. The validation experiments conducted also demonstrated that the predicted and experimental values could accurately forecast the responses.

Influence of Deposition Time on Titanium Nitride (TiN) Thin Film Coating Synthesis Using Chemical Vapour Deposition.

Titanium nitride (TiN) thin film coatings were grown over silicon (p-type) substrate using the atmospheric pressure chemical vapour deposition (APCVD) technique. The synthesis process was carried out to evaluate the effect of deposition time on the physical and mechanical characteristics of TiN coating. Thin films grown over Si substrate were further characterised to evaluate the morphological properties, surface roughness and mechanical properties using a scanning electrode microscope (SEM), atomic force microscopy (AFM) and nanoindentation, respectively. EDS equipped with SEM showed the presence of Ti and N elements in considerable amounts. TiN morphology obtained from the SEM test showed small-sized particles on the surface along with cracks and pores. AFM results revealed that by increasing the deposition time, the surface roughness of the coating also increased. The nanomechanical properties such as nanohardness (H) and Young's modulus (E), etc., evaluated using the nanoindentation technique showed that higher deposition time led to an increase in H and E. Overall, it was observed that deposition time plays a vital role in the TiN coating deposition using the CVD technique.

Effect of Cryo-Treated Cutting Tool End Milling on Custom 450 Stainless Steel.

Custom 450 stainless steel is the most desirable material across industries due to its widespread application in the aerospace, defense and marine industries. Stainless-steel materials are challenging to deal with and fall into the list of hard-to-process materials due to their low heat conduction coefficient and high mechanical properties. In this research work, end milling was carried out on Custom 450 stainless steel machined using TiAlN coated with tungsten carbide inserts that have been cryo-treated (CT) for 24 h (24 h) and 36 h (36 h), as well as untreated (UT) inserts. The inserts were evaluated in terms of feed force, feed rate and consistent depth of cut (ap) at various spindle speeds (S). Also examined were the tool morphology, chip anatomy and surface morphology of cryo-treated material compared to untreated inserts at various responses to cutting force (Fx, Fy, Fz), cutting temperature (Tc), vibration and surface abrasion. For inserts that have been cryo-treated for 36 h, the feed force (Fx) value was 44% and 5% less compared to inserts treated for 24 h and in UT inserts, respectively. Furthermore, for 24-h and 36-h CT inserts, feed force (Fx) was 12% and 20% less compared to a UT insert. Using 24-h cryo-treated inserts as opposed to UT inserts significantly reduced the surface roughness by 20%. Cutting inserts that have undergone cryogenic treatment have been observed to exhibit longer cutting tool life due to less wear and friction on the cutting edges.

Multi-Objective Optimization of Machining Parameters for Drilling LM5/ZrO2 Composites Using Grey Relational Analysis.

In today's world, engineering materials have changed dramatically. Traditional materials are failing to satisfy the demands of present applications, so several composites are being used to address these issues. Drilling is the most vital manufacturing process in most applications, and the drilled holes serve as maximum stress areas that need to be treated with extreme caution. The issue of selecting optimal parameters for drilling novel composite materials has fascinated researchers and professional engineers for a long time. In this work, LM5/ZrO2 composites are manufactured by stir casting using 3, 6, and 9 wt% zirconium dioxide (ZrO2) as reinforcement and LM5 aluminium alloy as matrix. Fabricated composites were drilled using the L27 OA to determine the optimum machining parameters by varying the input parameters. The purpose of this research is to find the optimal cutting parameters while simultaneously addressing the thrust force (TF), surface roughness (SR), and burr height (BH) of drilled holes for the novel composite LM5/ZrO2 using grey relational analysis (GRA). The significance of machining variables on the standard characteristics of the drilling as well as the contribution of machining parameters were found using GRA. However, to obtain the optimum values, a confirmation experiment was conducted as a last step. The experimental results and GRA reveal that a feed rate (F) of 50 m/s, a spindle speed (S) of 3000 rpm, Carbide drill material, and 6% reinforcement are the optimum process parameters for accomplishing maximum grey relational grade (GRG). Analysis of variance (ANOVA) reveals that drill material (29.08%) has the highest influence on GRG, followed by feed rate (24.24%) and spindle speed (19.52%). The interaction of feed rate and drill material has a minor impact on GRG; the variable reinforcement percentage and its interactions with all other variables were pooled up to the error term. The predicted GRG is 0.824, and the experimental value is 0.856. The predicted and experimental values match each other well. The error is 3.7%, which is very minimal. Mathematical models were also developed for all responses based on the drill bits used.

Optimization and Experimental Investigation of 3D Printed Micro Wind Turbine Blade Made of PLA Material.

This paper presents the design, development, and optimization of a 3D printed micro horizontal axis wind turbine blade made of PLA material. The objective of the study was to produce 100 watts of power for low-wind-speed applications. The design process involved the selection of SD7080 airfoil and the determination of the material properties of PLA and ABS. A structural analysis of the blade was carried out using ANSYS software under different wind speeds, and Taguchi's L16 orthogonal array was used for the experiments. The deformation and equivalent stress of the PLA material were identified, and the infill percentage and wind speed velocity were optimized using the moth-flame optimization (MFO) algorithm. The results demonstrate that PLA material has better structural characteristics compared to ABS material. The optimized parameters were used to fabricate the turbine blades using the fusion deposition modeling (FDM) technique, and they were tested in a wind tunnel.

Influence of Filler Material on the Microstructural and Mechanical Properties of 430 Ferritic Stainless Steel Weld Joints.

Tungsten Inert Gas (TIG) welding is a commonly used welding technique for ferritic stainless steel, due to its ability to produce high-quality, clean, and precise welds. This welding method provides excellent control over the heat input, making it suitable for thin-walled, high-alloy materials such as ferritic stainless steel. The purpose of this study was to investigate the effect of using two different filler materials, 310 (austenitic) and 410 (ferritic), on the microstructural and mechanical properties of Tungsten Inert Gas (TIG) weld butt joints of 430 ferritic stainless steel (FSS). The results showed that the choice of filler material significantly impacted the dilution percentage, the chromium-nickel equivalent ratio, microstructure, microhardness, and tensile characteristics of the welded joint. The use of 310 filler resulted in a columnar microstructure, whereas the use of 410 filler resulted in a ferritic (acicular ferrite) microstructure with the presence of martensite and austenite. The sample welded with 410 filler demonstrated superior mechanical properties compared to the sample welded with 310 filler. These findings emphasize the importance of selecting the appropriate filler material in order to achieve the desired microstructural and mechanical properties in 430 FSS welded joints.

Study of Residual Stresses and Austenite Gradients in the Surface after Hard Turning as a Function of Flank Wear and Cutting Speed.

This paper investigates the influence of cutting speed and flank wear on the depth profile of residual stresses, as well as the fraction of retained austenite after hard turning of quenched bearing steel 100Cr6. Residual stress and retained austenite profiles were studied for the white layer, heat-affected zone thickness, and XRD sensing depth. It was found that the influence of flank wear on the white layer and heat-affected zone thickness predominates. On the other hand, residual stresses are affected the cutting speed and the superimposing contribution of flank wear. Moreover, these aspects also alter microhardness in the affected regions. The study also demonstrates that information concerning residual stresses and the austenite fraction is integrated into the white layer, and the heat-affected zone in the surface is produced by the insert of low flank wear since the XRD sensing depth is more than the thickness of the white layer. On the other hand, the pure contribution of the white layer or the heat-affected zone to residual stress and the austenite fraction can be investigated when the affected surface region is thick enough.

Influence of Stacking Sequence on Mechanical Properties of Basalt/Ramie Biodegradable Hybrid Polymer Composites.

In this study, the mechanical properties of basalt/ramie/polyester hybrid composite laminates were investigated. A matrix of 45% polyester was used, as it has good bonding properties between fibers. The composite laminates were fabricated using a hand layup technique, with seven layers stacked in different sequences and impregnated in the polyester matrix to create a hybrid configuration. Tensile, flexural, impact, compression, and hardness tests were conducted according to ASTM standards for mechanical characterization. The results showed that the overall stacking sequence of sample number seven (BRBRBRB) had the highest tensile strength at 120 MPa, impact energy at 8 J, flexural strength at 115 MPa, compression strength at 70 MPa, and hardness of 77. Natural fiber-reinforced composites are being used in current automotive industry applications, such as in electric vehicles.

Enhancement of Thermal Behaviour of Flax with a Ramie Fibre-Reinforced Polymer Composite.

Plant-derived fibres, called lignocellulosic fibres, are a natural alternative to synthetic fibres in polymer composite reinforcement. Utilizing renewable resources, such as fibre-reinforced polymeric composites made from plant and animal sources, has become a crucial design requirement for developing and producing parts for all industrial goods. Natural-fibre-based composites are used for door panels, trays, glove boxes, etc. This study involves developing and thermal analysing a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre by way of a vacuum infusion process. As per ASTM Standard, eight different sequences were fabricated and thermally characterized. In the present study, three stages of weight loss (%) are shown by the thermogravimetric analysis (TGA). The sample loses less weight during the first stage, more during the second, and more during the third. The sample's overall maximum temperature was recorded at 630 °C. It was discovered that sample D (80.1 °C) had the highest heat deflection temperature, and sample B had the lowest (86.0 °C). Sample C had a low thermal expansion coefficient, while sample G had a high thermal expansion coefficient. Sample E had the highest thermal conductivity, measured at 0.213 W/mK, whereas sample A had the lowest conductivity, at 0.182 W/mK. From the present study, it was found that sample H had better thermal characteristics. The result of the present investigation would generate thermal data regarding hybrid ramie and flax composites, which would be helpful for researchers and practitioners involved in the field of biocomposites.

The Effect of Powder and Emulsion Binders on the Tribological Properties of Particulate Filled Glass Fiber Reinforced Polymer Composites.

Investigations into polymer composites are mainly focused on properties dependent on glass fiber reinforcement and particulate fillers. In the present study, the effect of the binder was examined. The specimens were produced with two types of epoxy resin, with similar numbers of glass mat layers and similar proportions of quartz powder added. However, one group was fabricated with an emulsion binder in the glass mats and another group with a powder binder. Attention was concentrated on the tribological properties of the as-prepared composites, though their strength was examined as well. The hardness of the Sikafloor matrix was found to be much more sensitive to the applied binder than that of the MC-DUR matrix. No direct correlation between the microhardness and the specific wear rate was observed and increasing the particulate filler proportion did not cause a direct increase of the specific wear rate. In particular, the highest specific wear rate, around 350 J/g, was reached for both matrices with a 1% quartz addition when the emulsion binder was applied, while in the case of the powder binder it was with 6% quartz with the MC-DUR matrix, and there was no quartz addition with the Sikafloor matrix. The highest microhardness, HV0.5 = 25, in turn, was reached for the mats with the emulsion binder in the Sikafloor matrix with an addition of 10% quartz powder, while the highest friction coefficient was exhibited in the composite with the MC-DUR matrix, when 1% of the quartz powder and the emulsion binder were applied.

Tribological Properties of Cu-MoS2-WS2-Ag-CNT Sintered Composite Materials.

In this work, in order to produce Cu-MoS2-WS2-Ag-CNT self-lubricating materials, powder metallurgy was used. Several different compositions containing single solid lubricant MoS2, WS2, Ag and CNTs as well as multi-component lubricants in the copper matrix were prepared. Friction and wear tests were carried out using the pin-on-disc method at room temperature. Light microscopy (LM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) were used to characterize the wear mechanism of sintered materials. The tribofilm on the worn surfaces of sintered materials and counter-specimens was observed. The influence of single solid lubricants and the synergistic interaction of two, three or four solid lubricants on tribological properties of sintered composite materials were determined.

Experimental Investigation of Bi-Directional Flax with Ramie Fibre-Reinforced Phenol-Formaldehyde Hybrid Composites.

Modern research focuses on natural, green, and sustainable materials that can be used to replace conventional materials. Because of their beneficial qualities, natural fibre composites are being thoroughly researched. This research focuses on the development of a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre through a vacuum infusion process. Eight different sequences were fabricated using a core-sheath structure and were mechanically characterized as per ASTM standards. The fabrication technique influences the adhesion of the matrix with reinforcement. The results also reveal that composite having ramie as a sheath layer and flax as a core delivers good mechanical characteristics compared to vice versa. The laminate H exhibited highest mechanical properties among all the eight laminates produced for this study. It exhibited a tensile strength of 54 MPa, tensile modulus of 0.98 Gpa, elongation of 7.1%, flexural strength of 143 Mpa, and compressive strength of 63.65 Mpa. The stress strain curves revealed that all the laminates exhibited ductile behaviour before failing during the tensile test and flexural test, respectively. The stacking sequence of the laminate H influenced the mechanical properties exhibited by it and its counterparts. A morphological study was carried out to analyse the failure surfaces. Morphological analysis exhibited few defects in the laminate after the tests. The composites developed delivers better mechanical properties than commercial composites available on the market, which can be used in lightweight structural applications.

Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles.

This paper presents the development and evaluation of neural network models using a small input-output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water.

Thermo-Mechanical Behavior of Aluminum Matrix Nano-Composite Automobile Disc Brake Rotor Using Finite Element Method.

Analysis of mechanical and thermal behaviors during braking has become an increasingly important issue in many transport sectors for different modes of transportation. Brake failure generated during braking is a complex phenomenon confronting automobile manufacturers and designers. During braking, kinetic energy is transferred to thermal energy, resulting in the intense heating of disc brake rotors that increases proportionally with vehicle speed, mass, and braking frequency. It is essential to look into and improve strategies to make versatile, thermally resistant, lightweight, high-performance discs. As a result, this study uses the finite element method to conduct a thermo-mechanical analysis of aluminum alloy and aluminum matrix nano-composite disc brake rotors to address the abovementioned issues. The FEA method is used for the thermo-mechanical analysis of AMNCs for vented disc brake rotor during emergency braking at 70 km/h. From the results obtained, aluminum base metal matrix nano-composites have an excellent strength-to-weight ratio when used as disc brake rotor materials, significantly improving the discs' thermal and mechanical performance. From the result of transient thermal analysis, the maximum value of heat flux obtained for aluminum alloy disc is about 8 W/mm2, whereas for AMNCs, the value is increased to 16.28 W/mm2. The result from static analysis shows that the maximum deformation observed is 0.19 mm for aluminum alloy disc and 0.05 mm for AMNCs disc. In addition, the maximum von Mises stress value of AMNC disc is about 184 MPa. The maximum von Mises stress value of aluminum alloy disc is about 180 MPa. Therefore, according to the results, the proposed aluminum base metal matrix nano-composites are valid for replacing existing materials for disc brake rotor applications.

Identification of Fibre Content in Edible Flours Using Microwave Dielectric Cell: Concise Review and Experimental Insights.

The quality of edible intake decides the health of the human body and is also responsible for building a healthy immune system in the body. A healthy immune system can protect the body even from invisible attacks of viral or bacterial infections. The assessment of the quality of edible items is not well defined and standardized in many developing countries due to quality assessment difficulties in practice. An alternative well-defined quality assessment approach for edible flours is presented in this paper. Every edible substance has dielectric properties, and it varies from material to material in nature. Edible flours and liquid have different microwave absorption capabilities, based on their natural molecular structure. Based on the microwave energy absorption characteristics of materials, the attenuation constant of edible flours is derived by the waveguide method in this work. In this approach, microwave energy absorption of the edible samples of different types of wheat, rice and millets are observed, and the attenuation constant factors of the samples are then calculated from the tabulated values. The work focuses on the identification of fibre content present in the edible flours. Inferences are made based on the attenuation and its variations with the number of samples, dielectric loss and dielectric constant of the samples. A systematic and concise review of the topic is also included for the benefit of future researchers.