Minimum Surface Roughness Research Articles

Enhancing Machining performance in Stainless Steel Machining using MXene Coolant: A Detailed Examination

Metal cutting, a complex process in manufacturing, involves various factors that significantly affect the quality of the final product. Notably, the turning process is crucial, with outcomes that heavily depend on multiple machining parameters. These parameters encompass speed, depth of cut, feed rate, the type of coolant used (specifically, high heat transfer MXene coolant), and insert types, among others. The material of the workpiece is also a critical factor in the metal-cutting operation. This study focuses on achieving optimal surface quality and minimizing cutting forces in the turning process. It recognizes the substantial impact of numerous process parameters, directly or indirectly affecting the product's surface roughness and cutting forces. Understanding these optimal parameters can lower machining costs and improve product quality. Our research concentrates on turning a stainless-steel alloy workpiece using a carbide insert tool. We employ the Response Surface Method (RSM) to optimize cutting parameters within a set range of cutting speed (100, 125, 150 m/min), feed rate (0.1, 0.2, 0.3 mm/rev), and depth of cut (0.4, 0.8, 1.2 mm). Additionally, we use various tool geometries and the RSM design of experiments to enhance and analyze the multi-response parameters of surface roughness and tool life. Optimal machining parameters for MXene-NFC involve a cutting speed of 140 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.5 mm. These settings ensure minimal surface roughness, maximum tool life, and the greatest total length of cut, achieving a composite desirability of 0.695.

Implementing machine learning in robotic wire arc additive manufacturing for minimizing surface roughness

ABSTRACT Wire arc additive manufacturing (WAAM) has emerged as an important type of metal additive manufacturing technique in recent years. The most critical challenge in WAAM is to achieve the desired surface roughness of deposited layers. The improvement of surface roughness can play an important role in improving the quality of parts made using WAAM. In this study, three machine learning (ML) algorithms, such as artificial neural network (ANN), random forest (RF) and XGboost algorithm, have been used to predict the surface roughness of deposited beads. A dataset for training and testing the ML models has been obtained using a 3D optical profilometer by measuring the surface roughness of a single-layer deposited bead of aluminium alloy. The performance of ML algorithms was evaluated using statistical metrics such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). Tuning of hyperparameters helped in improving the performance of ML models. Among the three ML algorithms, the XGBoost and ANN algorithms outperformed RF in predicting the roughness of WAAM deposited parts. This novel approach can help industries in the modelling of process parameters and easy implementation of WAAM with minimal resources, just with the use of real-time data.

Interaction of electric current with burnishing parameters in surface integrity assessment of additively manufactured Inconel 718

One of main problem associated with surface sever plastic deformation (SSPD) techniques like low plasticity burnishing (LPB) of metallic materials is limitation of surface integrity improvement at further loads (i.e. static force or pass number). It is due to interlocking of dislocation motion that limits further plastic deformation. As a plausible solution, in the present work, electric current assisted burnishing (ECAB) is introduced to use the concept of electroplasticity in surface integrity (roughness, hardness and residual stress) enhancement of Inconel 718 by additive manufacturing. Nevertheless, there are some researches which use this method to enhance performance of SSPD, the interaction effect of electrical parameter like current density with other parameters haven’t been studied well. Thus, to make the process optimized in terms of surface integrity aspects, parametric study of ECAB process with focus on interaction of current density with other LPB parameters merits further investigations. Thus, in the present work, an experimental study was carried out to identify the interaction effect of current density (as main parameter of ECAB) with other burnishing parameters (spindle speed, feed rate, current density, pass number and static force) on surface integrity aspects. Analysis of variances was further carried out to identify the contribution of each parameter on performance measures. Then, the interaction plots based on polynomial regression were developed, analyzed and discussed based on the physic of plastic deformation and some microscopic analysis like surface topography, scanning electron microscopy and X-ray diffraction analysis. It was found from the results that electric current density has the greatest effect on surface integrity aspect with contribution of 60 % on surface roughness, 68.5 % on hardness and 30 % on surface compressive residual stress. On the other hand it was found that this parameter has high interaction with process factors while applying different current density changes the optimum burnishing setting for each performance measures. Application of LPB at optimum parameter setting causes improvement of surface roughness of as-built material about 54 %; further improvement of 70 % and 85 % in roughness values were also achieved when the electric current with densities of 5A/mm2 and 10A/mm2, respectively were applied to burnishing process at optimum setting. It was also found that application of optimum LPB as well as optimum ECAB with densities of 5A/mm2 and 10A/mm2, results in improving the surface hardness about 17.5 %, 42 % and 68 %, respectively. In addition, under foresaid post-processing sequence, the surface compressive residual stress enhanced from 121 MPa (tensile type) for as-built material to −208 MPa, −345 MPa and −488 MPa (compressive type). Finally, the most optimum solution results in minimum surface roughness as well as maximum hardness and residual stress were identified by desirability approach function as 300RPM spindle speed, 0.08 mm/rev feed rate, 10A/mm2 current density, 3 pass number and 300 N force. To show the application of this optimization in a real life problem, fatigue resistance of optimum samples for high cycle and low cycle testing were obtained and compared with as-built material. It was found that following this post-treatment, the life cycle of samples improves from 160 % to 270 %, respectively that affirms practical application and manufacturing efficiency of proposed approach.

Justicia brandegeeana as a green corrosion inhibitor for carbon steel in sulfuric acid

ABSTRACT The anti-corrosive effect of the extract in ethanol of Justicia brandegeeana (Acanthaceae) against carbon steel AISI 1020 in sulfuric acid medium 1 mol L−1 was investigated. In addition to the effect of seasonality, several techniques were used to investigate the anti-corrosive activity, such as electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PP), linear polarization resistance (LPR), and weight loss. The chelating effect was analyzed by UV-Vis spectrophotometry and the surface was analyzed by scanning electron microscopy (SEM). The results showed that the inhibition efficiency is dependent on the concentration of the extract, reaching its maximum at 1500 ppm and despite having good thermal stability (82.41% inhibition at 70°C), it decreases with increasing temperature, indicating a physisorption behavior. The extract obeyed the Langmuir isotherm and the Tafel curves characterized it as a mixed inhibitor, with a tendency to cathodic region. It also presented a 52% chelating effect against Fe+2 ions, and the SEM analysis indicated a protection with the presence of plant extract, thus minimizing surface roughness. These results indicated the J. brangeeana ethanol extract as a green anti-corrosive agent.

Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters

Cardiovascular diseases, particularly coronary artery disease, pose big challenges to human life. Deployment of the stent is a preferable treatment for the above-mentioned disease. However, stents are usually made up of shape memory alloy called Nitinol. The poorer surface finish on the machined nitinol stents accelerates the migration of Nickel ions from the implanted nitinol stent, which is considered toxic and can lead to stenosis. The current study deals with controlling surface quality by minimising surface roughness and improving corrosion resistance. Femtosecond laser (fs-laser 10−15 s) micromachining was employed to machine the Nitinol surface to achieve sub-micron surface roughness. The Grey relational analysis (GRA)-coupled design of the experimental technique was implemented to determine optimal levels of four micromachining parameters (laser power, pulse frequency, scanning speed, and scanning pattern) varied at three levels to achieve minimum surface roughness and to maximise the volume ablation. The results show that to yield minimum surface roughness and maximum volume ablation, laser power and scanning speed are in a higher range. In contrast, the pulse frequency is lower, and the scanning pattern is in a zig-zag manner. ANOVA results manifest that scanning speed is the predominant factor in minimising surface roughness, followed by pulse frequency. Furthermore, the corrosion behaviour of the machined nitinol specimens was evaluated, and the results show that specimens with lower surface roughness had lower corrosion rates.

Assessment of machinability behaviour of LM24 – nano Al2O3 – gr hybrid composites through stir casting technique

Abstract This study is focused on developing a novel combination of a hybrid MMC made of LM24 aluminium alloy, nano Al2O3 and Graphite for enhanced machinability. The study conducts a series of experimental runs and evaluates surface roughness and material removal rate. The process parameters of spindle speed, feed, and depth of cut have been varied, while different weight percentages of nano Al2O3 (1%, 3%, and 5%) were incorporated into the LM24 metal matrix alloy along with 1 wt% of Gr. Response Surface Methodology (RSM) is used to optimize the turning parameters and enhance the machining performance for better quality and productivity. The derived optimal machining parameters have been verified through confirmatory tests. An analysis of variance (ANOVA) was used to determine the individual contributions of each parameter to the machinability characteristics. Surface morphology analysis revealed the uniform distribution of nanoparticle reinforcements in the metal matrix. The surface roughness and material removal rate of the machined nanocomposites were studied. The optimized outputs are a minimum surface roughness Ra 0.522 μm and maximum material removal rate MRR 110.2 mm3s–1 The maximum hardness of the MMC composites has been 60.5 HRB.

Analisis Pengaruh Kecepatan Pemotongan dan Kedalaman Pemotongan Terhadap Kekasaran Permukaan Baja SKD11 Dengan Metode RSM

In the current development of the manufacturing industry, the quality of each product must be met. One machining process, namely CNC lathe, can meet these demands. Surface roughness is one of the values that must be considered again. The minimum surface roughness indicates a very good CNC machining process, because the smaller the surface roughness value of a product, the better the resulting product value. To achieve that all need to du the turning in the right process variables. The process variables that are varied are cutting speed (m/mnt) and depth of cut (mm). Each variable has three levels, mnamely cutting speed 240, 250, and 260. For depth 0,1, 0,15, and 0,2. The research method used to obtain surface roughness values is the response surface methodology (RSM) with a central composite design. The material used in this research is SKD 11 steel. The minimum roughnees is at acutting speed of 240 m/min and a depth of cut of 0,325 mm, the roughness value abtained is an average of 1,627 (µm). for maximum roughness at a cutting speed of 230 m/min and depth of cut of 0,225 mm, the roughness value obtained of 1,975 (µm).

Pengaruh Kedalaman Pemakanan dan Kecepatan Potong pada Pembubutan Finishing Material Baja SKD 11 terhadap Kekasaran Permukaan Benda Bubutan

One important factor in achieving good quality value for a product in the machining process is to look at the level of roughness produced in the machining process. This research aims to determine the effect of feed depth and cutting speed on surface roughness. The process variables used in this research are feeding depth (mm) and cutting speed (m/min). Each variable has 3 levels, namely feeding depth of 0.5, 0.6 and 0.7 mm. Cutting speed 65, 70 and 75 m/min. In this research method, researchers used the full factorial method. The material used in this research is SKD 11 steel. The conclusion obtained from the research is that the minimum surface roughness is at a cutting depth of 0.6 mm with a cutting speed of 65 m/minute, with the average surface roughness value obtained being 0.883 (µm ). The maximum surface roughness is at a feed depth of 0.6 mm with a cutting speed of 70 m/minute with an average surface roughness value of 1.254 (µm).

The new Iridium-Hafnium-Aluminum alloy thin films with excellent mechanical properties and oxidation resistance

Improving oxidation resistance has long been a challenge for hot end components in high temperature environments. We have fabricated a series of IraHfbAlc (at. %) alloy thin films by multi-targeted direct current magnetron sputtering (DC-MS) and have thoroughly investigated the microscopic mechanism of the films. More importantly, we for the first time prepared IraHfbAlc alloy thin films with a preferred (111) orientation substitution solid solution, which exhibits excellent mechanical properties and oxidation resistance. The minimum surface roughness (Ra) value of the IraHfbAlc alloy films was 0.7 nm, while the maximum hardness (H) and elastic modulus (E) exceeded 20 GPa and 330 GPa, respectively. The oxidized volatility of the Ir in the films was only 0.11 μm/h at 1300 °C (lower than most previously reported values, such as 0.5 μm/h), which greatly improved the lifetime of the film. Confirmation that lattice distortion induces the refinement of crystal-like order (CLO) in thin films, and solves the mystery of enhanced oxidation resistance. The findings provide important insights into the design of Ir-containing alloy coatings and offer new ideas in the field of high-temperature protection.

Improving the novel white coconut oil-based metalworking fluid using nano particles for minimum surface roughness and tool tip temperature

At present, nearly 85 % all of the requirement for MWFs are satisfied by the use of mixtures of petroleum by-products and synthetic substances with supplementary additives to enhance their properties. The demand for easily biodegradable, environmental friendly MWFs is a current requirement. COCOTP, a novel biodegradable MWF based on white (refined) coconut oil, developed by authors, had previously shown promising tribological properties for machining mild steel (MS) and stainless steel (SS) when used with the flooding method, but had fallen short of the performance of commercially available, non-biodegradable alternatives with MQL. Therefore in the current investigation, nano-particles were added to improve the performance of novel COCOTP MWF to use it with MQL conditions. Two nanomaterials nano-graphite and nano-Al2O3 were separately added to the base fluid in different concentrations as a monodispersed suspension. These nano enhanced fluids (NEFs) were subsequently used in straight turning experiments of two work materials AISI304 and SS400. Both the nano-enhanced fluids show convincing improvements over both COCOTP and mineral-oil based fluids in terms of surface roughness of the specimens regardless of the material being turned. However when machining SS 400, NEFs perform better only in lower speeds in terms of temperature. SS400 has a much higher thermal conductivity than AISI304 means that the quantity of residual heat remaining at the point of material removal which can be absorbed by the cutting fluid is lower in SS400. During machining SS400 under MQL lubrication 9.8 %, 26.8 % and 24 % reduction of surface roughness values (with respect to soluble oil) and during machining AISI304 55.3 %, 73.7 % and 70.4 % reduction of surface roughness values were obtained at 1175 rpm with COCOTP, NEF A and NEF G respectively. Based on the experimental results, the best-performing nano-enhanced fluids under MQL are 0.3 % (w/w) Al2O3 and 0.3 % (w/w) graphite.

Experimental analysis for optimization of process parameters in machining using coated tools

Manufacturers are facing challenges in achieving high productivity and quality in manufacturing through machining. PVD-coated tools can control several machining challenges by enhancing hardness and abrasion resistance of the cutting tool. These tools facilitate turning operations in terms of efficiency, accuracy, and productivity by extending cutting performance and tool life. Aluminum bronze, a copper alloy valued for its mechanical, thermal, corrosion, and wear-resistant properties, finds application in diverse industries such as aerospace, automobile, marine, and electrical engineering, as well as in the creation of sculptures, decorative elements, and thermal devices. However, machining aluminum bronze presents common challenges, including achieving a smooth surface finish and minimizing high cutting force due to its inherent strength and abrasiveness. This research aims at identifying the optimal levels of cutting velocity, feed, and depth of cut to minimize surface roughness and cutting force during dry turning of wear-resistant high-strength CuAl10Fe5Ni5-C. PVD AlTiN-coated tools were utilized, which offer many advantages over others. Experiments were conducted through Taguchi’s L27 OA (orthogonal array) of factors. The results indicate that coated tools have superior performance in reducing surface roughness and cutting force. When it comes to designing and optimizing experiments, integrating PCA with Taguchi method is a potent strategy. Again, it was observed that feed is the most influential factor affecting responses.

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(34) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm3/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence.

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

AbstractThe aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

Minimizing surface roughness and back wall dross for fiber laser micro-cutting on AISI 316 L tubes using response surface methodology

A response surface methodology (RSM) was performed to study the influence of spot overlapping and pulse energy on back wall dross and surface roughness for fiber laser cutting of AISI 316 L stainless steel minitubes. Three treatments were compared to expel molten material (argon gas, compressed air, and a control test). Our results indicated that back wall dross and dross height reduction is observed when argon gas or compressed air is used through tubes compared with the control test. Additionally, a higher value of spot overlap (87.49%) and a lower value of pulse energy (30.31 mJ) resulted as the optimal parameters to cut minitubes.

Investigation of machining rate and surface roughness in wire EDM of Al6063/WC/ZrO2 composite using response surface methodology

The aim of the current work is to explore the machinability behavior of Al6063/WC/ZrO2 metal matrix composites (MMC) produced through a stir casting process through wire electrical discharge machining (WEDM) process. In order to examine the effects of process parameters such as voltage (V), pulse on time (Ton) and pulse off time (Toff) on material removal rate (MRR) and surface roughness (SR), the experiments were conducted by adapting Response Surface Methodology (RSM) in conjunction with the central composite design (CCD). A second-order regression model has been developed to predict the response parameters and an analysis of variance (ANOVA) was performed to validate the significance of the models. Using the desirability function approach, the parameters were set for the highest MRR and the minimum SR value. The prediction was within a tolerable average error range. A total of 19 sets of tests were developed to obtain six possible solutions. The most suitable solution among these six has been found by a confirmatory experiment. The results indicate that voltage and its interactions have significantly affected both the responses than Ton and Toff. Overall, it can be said that the study of the Al6063/WC/ZrO2 MMCs using WEDM process parameters demonstrated improved casting and machining qualities.

Pre-mixed abrasive waterjet peening with post-low stress milling process and the effect of process parameters on the surface integrity of TC11 titanium alloy

Pre-mixed abrasive waterjet peening (PAWJP) is progressively gaining traction in enhancing metal materials’ surface integrity and mechanical behavior. To avoid the disadvantage of PAWJP in the application, a hybrid manufacturing method of pre-mixed abrasive waterjet peening with post-low stress milling (PAWJP-LSM) is proposed and studied experimentally. The principle of PAWJP-LSM was first introduced. Subsequently, the effect of the PAWJP-LSM parameters (i.e., abrasive type, traverse speed, nozzle inlet pressure, and removal amount) on the surface integrity of TC11 titanium alloy and its variation trend were systematically investigated. Results show that the specimen treated with PAWJP using different process parameters formed a PAWJP treatment area with a strengthening depth of 6–275 μm. The minimum surface roughness obtained by PAWJP was 0.727 μm. The maximum surface microhardness significantly increased to approximately 516.3 HV from 402.8 HV, with a maximum micro-hardness rate of 28.2 % and a maximum work hardening depth of 225 μm. The surface compressive residual stress (CRS) induced by PAWJP ranged from −97 to −322 MPa, with a maximum CRS exceeding −890 MPa and a maximum CRS layer thickness of 305 μm. The surface integrity of PAWJP-LSM is further enhanced compared to PAWJP, resulting in a 98.9 % reduction in minimum surface roughness and a 73.7 % reduction compared to the as-received surface roughness. Additionally, PAWJP-LSM exhibited a 98.9 % increase in surface CRS and a 19.4 % increase in maximum CRS. Moreover, the fatigue life was significantly improved by 69.4 %, which was 294.5 % higher than that of the as-received specimen. Nanocrystals with an average size of 27.3 nm were formed in the topmost surface. Overall, the PAWJP-LSM method demonstrated significant potential for engineering applications and exhibited substantial value in the surface treatment of TC11 alloy. This study validated the effectiveness of the proposed PAWJP-LSM method and provided a valuable reference for selecting optimized process parameters.

Effects of hexagonal boron nitride content on forming quality and performance of laser powder bed fusion manufactured nickel-based hastelloy X composites

Regulating the composition of nickel-based composites, especially those containing crack-sensitive Hastelloy X (HX) matrix, significantly influences the forming quality and performance of parts manufactured using laser powder bed fusion (LPBF). Ray tracing and temperature field simulations were conducted to analyze the non-equilibrium solidification mechanism of the laser processed hexagonal boron nitride (h-BN)-HX system, resulting in an optimized h-BN content range. The optimized composition, with 0.2 wt% h-BN in HX, showcased the best forming quality and performance. It displayed negligible cracks or unmelted particles, minimal surface roughness of 9.29 μm, and superior tensile properties with a tensile strength of 1246.49 MPa and an elongation of 18.67 %. Furthermore, the samples showcased increased hardness (371.21 HV5) and reduced friction coefficient (0.54). Interestingly, with a further increase in h-BN content, a gradual improvement in strength and hardness was observed, accompanied by a reduction in the friction coefficient. Nevertheless, the ductility of the samples noticeably decreased. The 1 wt% h-BN/HX sample exhibited a tensile strength of 1503.14 MPa, hardness of 502.13 HV5, and friction coefficient of 0.48 while displaying an elongation of only 3.76 %. These findings emphasize the significance of optimizing the composition of Ni-based composites to enhance the forming quality and performance of parts manufactured through LPBF, particularly in aerospace and engineering applications.

Study of machining performance for electrochemical grinding of difficult-to-cut alloy U71Mn

PurposeThis study aims to apply an electrochemical grinding (ECG) technology to improve the material removal rate (MRR) under the premise of certain surface roughness in machining U71Mn alloy.Design/methodology/approachThe effects of machining parameters (electrolyte type, grinding wheel granularity, applied voltage, grinding wheel speed and machining time) on the MRR and surface roughness are investigated with experiments.FindingsThe experiment results show that an electroplated diamond grinding wheel of 46# and 15 Wt.% NaNO3 + 10 Wt.% NaCl electrolyte is more suitable to be applied in U71Mn ECG. And the MRR and surface roughness are affected by machining parameters such as applied voltage, grinding wheel speed and machining time. In addition, the maximum MRR of 0.194 g/min is obtained with the 15 Wt.% NaCl electrolyte, 17 V applied voltage, 1,500 rpm grinding wheel speed and 60 s machining time. The minimum surface roughness of Ra 0.312 µm is obtained by the 15 Wt.% NaNO3 + 10 Wt.% NaCl electrolyte, 13 V applied voltage, 2,000 rpm grinding wheel speed and 60 s machining time.Originality/valueUnder the electrolyte scouring effect, the products and the heat generated in the machining can be better discharged. ECG has the potential to improve MRR and reduce surface roughness in machining U71Mn.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0341/

Experimental investigation of 3D taper profile machining of SS304 using WEDM

ABSTRACT Wire electrical discharge machining (WEDM) is an outstanding variety of EDM processes in which the tool is a wire electrode. The medical, electronics, tool, and die industries use stainless steel 304 (SS304). However, there are traditionally many complications that manufacturers face, while machining SS304. Also, the creation of 3D shapes using WEDM is challenging. Due to difficulties in normal machining and producing 3D profiles using conventional machining of SS304, this research aims to utilize the application of WEDM for creating complex shapes or products with top as square and bottom as rectangular shape to get a rectangular frustum. A ϕ 0.25 mm brass wire was used, and experiments were conducted as per L9 array. Further, the determination of the best process parameter (Ton, Toff, Voltage, current) combination for achieving the highest MRR, hardness, and minimal surface roughness (Ra) and kerf with analysis of machined surfaces using an SEM microstructure image was tried. The results indicated that MRR and Ra values varied from 7.05 to 47.29 mm3/min and 1.251 to 2.019 µm, respectively. Furthermore, the kerf ranged from 0.294 to 0.475 mm and hardness started at 164 to 233 HV. To optimize WEDM results Grey relational analysis (GRA) was utilized.

Maximizing performance and efficiency in 3D printing of polylactic acid biomaterials: Unveiling of microstructural morphology, and implications of process parameters and modeling of the mechanical strength, surface roughness, print time, and print energy for fused filament fabricated (FFF) bioparts

Medical stents, artificial teeth, and grafts are just some of the many applications for additive manufacturing techniques like bio-degradable polylactic acid 3D printing. However, there are drawbacks associated with fused filament fabrication-fabricated objects, including poor surface quality, insufficient mechanical strength, and a lengthy construction time for even a relatively small object. Thus, this study aims to identify the finest polylactic acid 3D printing parameters to maximize print quality while minimizing energy use, print time, flexural and tensile strengths, average surface roughness, and print time, respectively. Specifically, the infill density, printing speed, and layer thickness are all variables that were selected. A full-central-composite design generated 20 samples to test the prediction models' experimental procedures. Validation trial tests were used to show that the experimental findings agreed with the predictions, and analysis of variance was used to verify the importance of the performance characteristics (ANOVA). At layer thickness = 0.26 mm, infill density = 84 %, and print speed = 68.87 mm/s, the following optimized values were measured for PLA: flexural strength = 70.1 MPa, tensile strength = 39.2 MPa, minimum surface roughness = 7.8 μm, print time = 47 min, and print energy = 0.18 kwh. Firms and clinicians may benefit from utilizing the developed, model to better predict the required surface characteristic for various aspects afore trials.