Minimum Surface Roughness Research Articles

Parametric analysis of electrical discharge machining on AA6061 with a WC–Cu composite electrode

The AA6061 aluminium alloy surface characteristics presented in this study were obtained after it was machined using die sinking electro discharge machining (EDM) with a composite electrode developed through powder metallurgy. Machining was performed using negative polarity. In order to obtain optimum material removal rate (MRR) and surface roughness (SR), the research has been carried out, and EDM parameters like voltage, current and pulse on time were optimized using the response surface methodology that integrates the central composite design. The parameter interaction effects on EDM machined surface characteristics were studied with the aid of ANOVA. It was determined from model that the regression for MRR and SR were 98.6% and 98.3% respectively, means empirical model is considered sufficient. It was observed that increased MRR and SR were observed in the condition of increased current, but MRR and SR decreased at increased voltage and pulse on time. This could be due to strengthened ionization temperature that increased MRR with acceptable SR. The microstructure of the machine was assessed using microscopic evaluation. The increased current contributed to the formation of a recast layer and debris which hinders the machining process and reduces MRR. The experimental results show that the maximum MRR of 0.087 mg/min and minimum SR of 1.893 µm were achieved. Additionally, the use of WC–Cu composite electrodes led to an increase in hardness from 65 HV to 78 HV in the machined region. It is concluded that WC–Cu composite electrodes typically result in higher hardness, improved MRR and better SR for the AA6061 alloy compared to conventional Cu electrodes.

Enhancing two-dimensional growth of high-temperature AlN buffer to improve the quality of GaN on Si grown by ex situ two-step method

GaN epitaxial films were grown on Si substrates by ex situ two-step method combining the technologies of pulsed laser deposition (PLD) and metal organic chemical vapor deposition (MOCVD). The N/Al ratio of high-temperature AlN (HT-AlN) buffer layer was optimized, and its influence on the HT-AlN growth mode as well as the quality of GaN epitaxial films was investigated. When the N/Al ratio was 500, the two-dimensional growth of HT-AlN was greatly enhanced, and it obtained a coalesced and smooth surface with the minimum RMS surface roughness as 1.63 nm. The as-grown GaN epitaxial film had the best crystalline quality with the minimum full-width at half maximums of GaN(0002) and GaN(10 1¯ 2) as 0.14° and 0.22°, respectively. Owing to the high energy of PLD, the ex situ low-temperature AlN template had an abrupt Si/AlN interface and flat surface, which was of significance to improve the quality of HT-AlN buffer and GaN film.

Removal-Free and Multicellular Suspension Bath-Based 3D Bioprinting.

Suspension bath-based 3D bioprinting (SUB3BP) is effective in creating engineered vascular structures. The transfer of oxygen and nutrients via engineered vascular networks is necessary for tissue or organ survival and integration following transplantation. Existing SUB3BP techniques face challenges in fabricating hierarchical structures with multicellular organization, including issues related to suspension bath removal, restricted material choices, and low accuracy. A next-generation SUB3BP technique that is removal-free and multicellular is presented. A simple, storable, stable, and scalable starch hydrogel design leverages the diverse spectrum of hydrogels available for use in SUB3BP. Starch granules (8.1µm) create vascular structures with minimal surface roughness (2.5µm) that simulate more natural vessel walls compared to prior research. The development of cells and organoids, as well as the bioprinting of multicellular skin models with vasculature, demonstrates that starch suspension baths eliminate the removal process and have the potential for fabricating artificial tissue with a hierarchical structure and multicellular distribution.

Investigation of Titanium Nitride as an Effective Interphase for Carbon-Fiber-Reinforced Silicon Carbide Ceramic Matrix Composites.

Ceramic matrix composites (CMCs) have played a significant role in increasing the efficiency of gas turbine engines. CMCs combine the high temperature resistance of ceramics with the high mechanical strength of ceramic fibers into a single unit. Interphase layers are a crucial component in CMCs, as they prevent ceramic fibers from oxidation and introduce strengthening mechanisms into the composite. Hexagonal boron nitride and pyrolytic carbon are the most commonly used interphase layers in the aerospace industry. Other than that, very few materials have been evaluated as interphase layers. In this study, we explore the possibilities of using titanium nitride as an interphase layer in single-tow CMCs (mini composite) representative of a unidirectional composite at a smaller scale. T-300 carbon fibers were coated with TiN by atmospheric pressure chemical vapor infiltration using TiCl4, N2, and H2. The deposition temperature, precursor flow rate ratio, total precursor flow rate, and deposition time were optimized to obtain high-quality coatings. The best coating was produced at 800 °C, 4:1 H2 [TiCl4]/N2 ratio, 125 standard cubic centimeters per minute (N2 + H2 [TiCl4]) total flow precursor flow rate, and 2 h of deposition time. At these conditions, the coatings displayed good fiber coverage, good fiber adhesion, minimum fiber linkage, and minimum surface roughness. There was minimum fiber degradation after TiN coating, with a retention of 95% of the initial Young's modulus and 26% of the ultimate tensile strength of the carbon fiber. Adding the TiN interphase coating to the Cf/SiC CMC increased the ultimate tensile strength of the composite by 1122% and Young's modulus by 150%.

Amorphous In–Al–Sn–O Thin Film Transistors and Their Application in Optoelectronic Artificial Synapses

AbstractIn‐Al‐Sn‐O (IATO) is a very promising novel amorphous oxide as the active layer of thin film transistors (TFTs). Herein, IATO TFTs are first fabricated with the effects of annealing on IATO films and TFTs being studied. The IATO films possessed amorphous structure, flat surface morphology, high visible light transmittance, and wide optical bandgap ≈4.20 eV before and after annealing even at 400 °C. The minimal surface roughness and internal defects are obtained for the 300 °C annealed IATO film. Correspondingly, the 300 °C annealed TFTs demonstrated the best overall performance including high saturation mobility (8.55 ± 0.62 cm2 V−1 s−1), low subthreshold swing (0.40 ± 0.07 V dec−1), ideal on/off current ratio (1.25 ± 0.09 × 108), and negligible hysteresis (0.23 ± 0.03 V) values. The 300 °C annealed TFTs are then applied in optoelectronic artificial synapses and exhibit typical synaptic properties, including excitatory postsynaptic current, paired‐pulse facilitation, and short‐term plasticity to long‐term plasticity conversion in response to light stimulation. The international Morse code and repetitive learning‐forgetting behavior of the human brain are also successfully simulated. In particular, an emotion‐memory efficiency model is proposed and the emotion effect on human memory efficiency is successfully imitated via the regulation of gate voltage.

Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R2) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality.

Microcrystalline and nanocrystalline structure of diamond films grown by MPCVD with nitrogen additions: Study of transitional synthesis conditions

This research explores the complex influence of nitrogen concentration and substrate temperature on the resultant properties of polycrystalline diamond (PCD) films grown by microwave plasma chemical vapor deposition (CVD). In particular, we investigated the growth parameters to find the exact conditions for changing the morphology of the resulting film from microcrystalline (MCD) to nanocrystalline (NCD). A series of 2 µm-thick polycrystalline diamond films was grown on Si substrates in methane-hydrogen–nitrogen gas mixtures with varied: (i) nitrogen concentration (0–2 %) and (ii) substrate temperature (700–1050 °C). Comprehensive characterization techniques, including scanning electron microscopy (SEM), micro-Raman spectroscopy, and X-ray diffraction, were employed to assess the morphological, structural, and spectroscopic properties of the films. The study reveals that even minor additions of nitrogen significantly modulate secondary nucleation, impacting both film morphology and phase composition. Furthermore, substrate temperature emerges as another critical parameter, influencing the growth kinetics and crystallite size. The comparison of the characteristics of grown PCD films allowed us to find conditions for the formation of NCD films with minimal surface roughness and maximal growth rate: nitrogen concentration [N2] ≈ 0.2–0.5 % and temperature T ≈ 850–900 °C. These findings can be used to optimize the CVD synthesis parameters of PCD films for applications as protective or friction-reducing layers, as well as for superhard cutting tools and optical devices.

Development of ANN model for predicting mechanical properties of 3D printed PEEK polymer using FDM and optimization of process parameters for better mechanical properties

Additive manufacturing (AM) has evolved from a proven technology to a rapid prototyping tool with great potential. This technology is widely used in many industries such as automotive, aerospace, and medical; fused deposition modeling (FDM) is a popular 3D printing technique for producing PEEK (polyether ether ketone) parts, including implant prosthetic teeth. In this study, artificial neural network (ANN) modeling, parametric optimization, and experimental examination of PEEK 3D printing were conducted to enhance 3D printing processes. In this study, four critical process factors (infill density, layer height, printing speed, and infill pattern) influence the surface roughness, mechanical strength, and elastic modulus of the printed samples. Utilizing a 4–12–3 network design, this study demonstrates that an ANN model with an average error of less than 5% is optimal for all three responses. Furthermore, the study employed a teaching and learning based optimization algorithm (TLBO) and a non-dominated sorting genetic algorithm (NSGA) to optimize the printing process to obtain improved mechanical properties. The findings highlight TLBO’s ability to minimize surface roughness to 6.01 μm and NSGA’s capability to maximize the elastic modulus to 1253.35 MPa and ultimate tensile strength to 65.55 MPa. Microstructural studies supported the results obtained through parametric analysis and optimization.

Dynamics optimization of coupling atomization process in an injector achieved by novel micro laser powder bed fusion

This study employed large eddy simulation and a volume-of-fluid with discrete phase model to evaluate a swirl fuel injector's atomization performance. Addressing non-uniform atomization at low flow rates, the study optimized a swirl injector structure based on additive manufacturing advantages during re-modeling. Computational analysis revealed that vortex downstream and capillary bubbles caused non-uniformity in the prototype injector, mitigated by the optimized injector's three-dimensional flow channel. Comparative analysis showed similar parameters between the optimized and prototype injectors, except for significant improvement in circumferential uniformity at low flow rates (from 41.48% to 14.69%). The optimized injector's swirl structure, produced via micro laser powder bed fusion, exhibited precise dimensions and minimal surface roughness. Validation experiments without air inflow confirmed the computational results' reliability, with a minor discrepancy in circumferential uniformity (2.98%) and atomization cone angle (2.3°) for the prototype swirl structure. At low flow rates, the optimized structure showcased reduced circumferential non-uniformity (from 42.31% to 28.76%), underscoring its benefits. This interdisciplinary investigation underscores additive manufacturing's application in structural optimization and manufacturing.

Effect of Mg on the Microstructure, Mechanical Properties, and Surface Roughness of Functionally Graded A413 Composite: Machine Learning Approach at Wire‐Cut Electric Discharge Machining Zone

The present study assists the industrial sectors by implementing low‐weight high‐strength aluminium composite. The pristine magnesium as a reinforcement in three different weight percentages (3.5, 7, and 10.5) is utilized to fabricate the A 413 composite using centrifugal casting. The density analysis concludes a higher level of Mg inclusion prompts the material to save 3.9% of weight. Microstructural study of centrifugal casting specimens reveal a rich existence of hard phase Mg2Si in the outer zone, with its richness decreasing toward the inner zone. The tensile test result affirms the functionally graded nature of the material with the increasing trend of tensile strength from the inner to the outer. Fractured surface analysis concludes the existence of cracks and their propagation for the inner region specimen. The influence of magnesium on the surface finish of functionally graded composite at the wire cut electric discharge machining (WEDM) zone is predicted using a machine learning‐based random forest regressor (RFR) algorithm. The supervised learning algorithm declares that 10.5 wt% of Mg facilitates the minimal surface roughness of 0.229 µm with the optimal combination of parameters 6 A of current, 115 µs of pulse‐on time, 60 µs of pulse‐off time, and 8 N of wire tension.

Research on a novel non-uniform and asymmetric ultrasonic vibration system

Abstract A new type of non-uniform and asymmetric ultrasonic vibration system is proposed and applied to the turning process. The research results show that the design theory of the non-uniform and non-symmetric ultrasonic vibration system is accurate and reliable.The theoretical design frequency and modal simulation frequency error of Ultrasonic Vibration Turning System (UVTS) is only 2.5%. The designed non-uniform and non-symmetric ultrasonic vibration system has excellent vibration performance and stability, and the established simulation model can predict surface microtexture accurately. The vibration mode output by the non-uniform and non-symmetric ultrasonic vibration system is longitudinal-bending vibration, and the actual vibration performance is excellent. Compared with conventional cutting, the addition of longitudinal-bending ultrasonic vibration under the same conditions can give the machined surface a microscopic textured structure, reduce surface roughness and water contact angle, and improve surface wettability.The minimum surface roughness obtained in the experiment is 1.005 μm.The orthogonal experiment results show that there is a correlation between the machining parameters, surface micro-structure parameters, surface wettability, and surface roughness.

Experimental Study on Dry Milling of Stir-Casted and Heat-Treated Mg-Gd-Y-Er Alloy Using TOPSIS

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results.

Optimizing the diamond wire sawing of polycrystalline silicon: An experimental approach

This study aims to determine the optimized cutting conditions for diamond wire sawing of polycrystalline silicon (poly-Si) wafers using a laboratory machine-tool that employs continuous cutting movement and reach cutting conditions similar to those in the industry. The experimental cutting was carried out following a central composite design, varying the wire cutting speed (vc) and feed rate (vf). A response surface methodology was employed to find the optimized cutting conditions for minimum surface roughness and subsurface micro-crack depth of the as-sawn poly-Si wafer. The results showed that the as-sawn poly-Si exhibited ductile scratches and fragile fractures in its surface morphology, with high vc and low vf resulting in a smooth surface. The response surfaces indicated that increasing vf led to increased Ra and Rq values, while increasing vc reduced them. Median micro-cracks were slightly inclined in the subsurface region, with the response surface indicating that their depth reduced with increasing vc and decreasing vf. The vf/vc ratio significantly affected surface integrity, with a lower vf/vc ratio being more suitable for reaching a smoother surface and shallower subsurface damage. The following material removal mechanisms was identified: optimal ductile region, ductile-to-brittle transition, brittle-ductile mixture region, and brittle region. Simulations using the response surface models indicated minimum values of Ra = 0.35 μm, Rq = 0.48 μm and SSD = 3.29 μm. Thus, this study provides a processing parameter window for diamond wire sawing of poly-Si wafers, which could be useful for both the photovoltaic and microelectronic industries.

Моделирование и оптимизация процесса накатывания роликом AL6061-T6 для достижения минимального отклонения от круглости, минимальной шероховатости поверхности и повышения ее микротвердости

Introduction. Roller burnishing is one of the most common methods of improving the surface quality of parts, wear resistance, microhardness, and corrosion resistance. The process involves compressing and smoothing the workpiece using the pressure of a hardened roller. It is often used to improve part performance and lifespan in sectors including automotive, aerospace, and medical equipment manufacturing. The literature reviewed shows that the roller burnishing process effectively improves the overall surface quality and hardness of the workpiece. In addition, roller burnishing is considered as an affordable method to enhance the functionality and robustness of machined parts by reducing the likelihood of surface defects such as like scratches and cracks. However, very few studies have been reported on the modeling and optimization of roller burnishing of Al6061-T6 for minimum surface roughness, better microhardness, and roundness. The methods of investigation. In the current work, roller burnishing of Al6061-T6 is modeled and optimized for superior microhardness, roundness, and minimal surface roughness. Under dry-cutting conditions, the performance of roller burnishing of Al6061 specimens is assessed in terms of process factors such as cutting speed, feed, and number of passes. Mathematical models to predict the surface roughness, microhardness, and deviation in roundness are developed based on the experimental results. Results and Discussion. The coefficient of correlation for the developed models is found to be close to 0.9, which indicates that it can be reliably used to predict and optimize the roller burnishing of the Al6061-T6. According to this study, the use of the following cutting parameters leads to the lowest variation in roundness (4.282 µm), the better microhardness (119.2 Hv), and the lowest surface roughness (0.802 µm): cutting speed 344 rpm, feed 0.25 mm/rpm and four passes. Further, the study reveals that increasing the number of passes (beyond four) does not significantly improve the surface roughness or microhardness. However, it does lead to a slight increase in the roundness deviation. Therefore, in order to achieve optimal results, it is recommended to use a maximum of four passes during roller burnishing of Al6061 specimens under dry cutting conditions. These results imply that roller burnishing can effectively improve the overall quality and hardness of the workpiece surface. In addition, roller burnishing is considered as an affordable method to increase the functionality and robustness of machined parts by reducing the likelihood of surface defects like scratches and cracks.

Control factor optimization for friction stir processing of AA8090/SiC surface composites

Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and surface composites fabrication. This investigation concentrates on AA8090/SiC surface composites produced via friction stir processing. Experiments were conducted by varying the following friction stir processing parameters: Tool rotational speed (800–1400 rpm), Tool traverse speed (25–75 mm/min), and Groove width (1.0–1.8 mm). Response measures encompassed Ultimate Tensile Strength and surface roughness. Central Composite Design of Response Surface Methodology designed the experiments and mathematical relationships established between input parameters and ultimate tensile strength and surface roughness. Analysis of variance was used to test the model's adequacy. The models examined individual and interaction effects of input factors on ultimate tensile strength and surface roughness of surface composites. A combinations of input parameters was identified that yields the maximum ultimate tensile strength and minimum surface roughness. The current work employs the friction stir processing approach to synthesis near-net-shaped surface composites without additional machining by systematically optimizing process parameters. Results indicate that increasing tool rotational speed produces well-finished AA8090/SiC surface composites with decreased strength. In contrast, increased tool traverse speed and groove width generate surface composites with rougher surfaces and higher strength. Surface and contour plots further explored the influence of parameter interactions on responses.

COMPARATIVE ANALYSIS OF MACHINING AISI 1020 MILD STEEL USING MINERAL OIL BASED AND NEEM SEED OIL BASED CUTTING FLUIDS

Retaining workpiece dimensional accuracy and quality, elongated tool life, minimal surface roughness, minimum tool wear and overall high productivity in metal cutting (machining) operations are areas the cutting fluids plays pivotal role. This study focuses on surface roughness, cutting temperature and tool wear by evaluating their optimal factor effectiveness during turning of AISI 1020 Mild Steel using an environmentally friendly cutting fluid. Serious concern has been raised about the non-biodegradable and non-recyclable of the conventional (mineral based) cutting fluid and this prompted the renewal of research interest in environmentally friendly cutting fluids such as the neem seed oil based cutting fluid. The locally sourced neem seed oil (NSO) was characterized by investigating the physiochemical properties and the fatty acid composition (FAC). Surface roughness, cutting temperature and tool wear were minimized during turning of AISI Mild Steel using coated carbide insert with the evaluation compared with the mineral oil based cutting fluid. Taguchi experimental design was employed during the optimization. The formulated fluid showed 8.35 pH value, 0.896 m 2/s viscosity, good resistance to corrosion, stable and milkfish colouration. The S/N ratio for NBCF and MBCF are 800 CS, 0.4 FR and 0.6 doc; and 800 CS, 0.4 FR and 0.6 DOC respectively with R-sq and R-sq(pred) values as 98.37% and 97.38 under the NBCF and lastly 94.53% and 91.25% under the MBCF for surface roughness respectively. For cutting temperature and tool wear, S/N ratios show A 1B 1C 1 under both NBCF and MBCF. The findings shows that the neem seed oil based cutting fluid outperform the mineral oil-based fluid under the same cutting parameters and this has contributed to the science of machining.

Consequence of coherent Cu5Zr precipitates and Cu2O passive layer formation on the corrosive behaviour of additively processed Cu-Cr-Zr alloy in simulated seawater

Abstract The Cu-Cr-Zr copper alloy is known for its outstanding electrical conductivity and fatigue strength. However, the corrosion behaviour of the copper alloy should also be taken into account when adopting it in industrial applications, especially in the marine environments. This research aims to fabricate Cu alloy coupons using the Laser Powder Bed Fusion (LPBF) technique and subsequently test their corrosive performance in simulated seawater. This research confirms that the Cu5Zr precipitate formation during the LPBF process and the Cu2O passive layer formation were the main reason for the enhanced corrosive behavior of the LPBFed copper alloy. The OM (Optical Microscope), FESEM (Field Emission Scanning Electron Microscope) images supported in evaluating melt pool formations and irregularities, and also confirmed the polycrystalline structure. The diffraction pattern from the TEM (Transmission Electron Microscope) analysis confirmed the formation of Cu5Zr precipitate and grain size distribution, while their orientations were obtained from the EBSD (Electron Based Scattered Diffraction) EBSD analysis. Micro hardness was executed on the scanning and building directions, and it was found that the building direction possessed higher hardness of 54 HV0.3 which was 5% higher than in the scanning direction. This significant fluctuation in the hardness value is due to the closely packed equiaxed and columnar grains along the outer and inner regions of the melt pools. Potentio-dynamic polarization (PD) and electrochemical impedance spectroscopy (EIS) tests were performed on the printed copper alloy parts for various immersion periods of 0, 9, 18 and 38 h. Further, the XRD (x-ray Diffraction) analysis was performed on the corroded surface and it confirmed the Cu2O passive layer and the occurrence of Cu5Zr precipitate. The occurrence of Cu5Zr precipitates and Cu2O passive layer formation helped attain the maximum polarization resistance of 2033.8 ohm and minimum current density of 5.928 × 10−6 A cm−2 with minimum surface roughness of 3.447 μm.

Influence of HSS drill coatings on surface finish and cylindricity of AA6061/C/ZrO2 composite in CNC drilling operations under dry conditions

Abstract This study aims to evaluate the effect of HSS drill tool coatings, namely single-layer TiN, single-layer AlCrN, and double-layer AlTiN + TiSiXN (Durana), on the surface roughness and cylindricity of AA6061 (90 wt%)/C (5 wt%)/ZrO2 (5 wt%) hybrid composite material processed by the stir casting method. The fabricated sample was examined for the uniform particle distribution of reinforcements using Scanning Electron Microscope. The drilling operation was carried out on the fabricated in a CNC machining center by setting the spindle speeds of 800, 1200, and 1600 rpm, depths of cut of 0.5, 1, and 1.5 mm, and feeds of 50, 100, and 150 mm/rev. An orthogonal array (L27) designed by Taguchi’s method was used as the design of the experiment for the optimization of the best cutting parameters and coating. Surface roughness and cylindricity errors were determined for the 27 experimental runs. Field emission scanning electron microscopic (FESEM) examination and Energy Dispersive Spectroscopy (EDS) were used to analyze the surface integrity and elemental composition, respectively. The results revealed that Durana had a significant effect on the surface substrate with minimum surface roughness (Ra) of 1.666 μm and obtained the minimum cylindricity error for the parameters of depth of cut 0.5 mm, spindle speed of 1200 and feed of 100 mm rev−1.

Optimizing Selective Laser Melting of Inconel 625 Superalloy Through Statistical Analysis of Surface and Volumetric Defects

This article delves into optimizing and modeling the input parameters for the selective laser melting (SLM) process on Inconel 625. The primary aim is to investigate the microstructure within the interlayer regions post-process optimization. For this study, 100 layers with a thickness of 40 µm each were produced. Utilizing the design of experiments (DOE) methodology and employing the Response Surface Method (RSM), the SLM process was optimized. Input parameters such as laser power (LP) and hatch distance (HD) were considered, while changes in microhardness and roughness, Ra, were taken as the responses. Sample microstructure and surface alterations were assessed via scanning electron microscopy (SEM) analysis to ascertain how many defects and properties of Inconel 625 can be controlled using DOE. Porosity and lack of fusion, which were due to rapid post-powder melting solidification, prompted detailed analysis of the flaws both on the surfaces of and in terms of the internal aspects of the samples. An understanding of the formation of these imperfections can help refine the process for enhanced integrity and performance of Inconel 625 printed material. Even slight directional changes in the columnar dendrite structures are discernible within the layers. The microstructural characteristics observed in these samples are directly related to the parameters of the SLM process. In this study, the bulk samples achieved a microhardness of 452 HV, with the minimum surface roughness recorded at 9.9 µm. The objective of this research was to use the Response Surface Method (RSM) to optimize the parameters to result in the minimum surface roughness and maximum microhardness of the samples.

QUALITY AND PRODUCTIVITY IMPROVEMENT IN THE MILLING OF POLYOXYMETHYLENE CO-POLYMER (POM C) USING RSM, SVM AND GENETIC ALGORITHM

This paper aims to conduct an economic and environmental machining procedure (dry condition) during face milling of the Polyoxymethylene Co-polymer. In this context, an experimental study based on Taguchi’s design was conducted to develop empirical models using Response Surface Methodology (RSM) on one hand and Support Vector Machine (SVM) for regression on the other hand. The ANalysis of VAriance (ANOVA) is conducted to determine the contribution and the significance of each cutting parameter on the surface quality and productivity (MRR). The obtained models are being compared to determine the most efficient approach. The last part is to find the optimum cutting combination by using Genetic Algorithm (GA) optimization based on SVM models, whether to minimize surface roughness Ra, or for composite objective to improve the quality and to increase the productivity. The results show that feed per tooth (fz) is the most affecting parameter on Ra followed by depth of cut (ap) and then the cutting velocity (Vc). SVM was more robust than RSM with less deviation error.