Optimal Combination Of Process Parameters Research Articles

Reinforcement learning as data-driven optimization technique for GMAW process

Welding optimization is a significant task that contributes to enhancing the final welding quality. However, the selection of an optimal combination of various process parameters poses different challenges. The welding geometry and quality are influenced differently by several process parameters, with some exhibiting opposite effects. Consequently, multiple experiments are typically required to obtain an optimal welding procedure specification (WPS), resulting in the waste of material and costs. To address this challenge, we developed a machine learning model that correlates the process parameters with the final bead geometry, utilizing experimental data. Additionally, we employed a reinforcement learning algorithm, namely stochastic policy optimization (SPO), with the aim to solve different optimization tasks. The first task is a setpoint‐based optimization problem that aims to find the process parameters that minimize the amount of deposited material while achieving the desired minimum level of penetration depth. The second task is an optimization problem without setpoint in which the agent aims to maximize the penetration depth and reduce the bead area. The proposed artificial intelligence-based method offers a viable means of reducing the number of experiments necessary to develop a WPS, consequently reducing costs and emissions. Notably, the proposed approach achieves better results with respect to other state-of-art metaheuristic data-driven optimization methods such as genetic algorithm. In particular, the setpoint‐based optimization problem is solved in 8 min and with a final mean percentage absolute error (MPAE) of 2.48% with respect to the 42 min and the final 3.42% of the genetic algorithm. The second optimization problem is also solved in less time, 30 s with respect to 6 min of GA, with a higher final reward of 5.8 from the proposed SPO algorithm with respect to the 3.6 obtained from GA.

Experimental Study on Chemical-Mechanical Synergistic Preparation for Cemented Carbide Insert Cutting Edge.

Typical edge defects in the edge region of a new cemented carbide insert without edge preparation include burrs, poor surface quality, micro-breakages, and irregularities along the edge. To address the problems in new cemented carbide inserts without edge preparations, a chemical-mechanical synergistic preparation (CMSP) method for the cemented carbide insert cutting edge was proposed. Firstly, the CMSP device for the insert cutting edge was constructed. Then, the polishing slurry of the CMSP for the insert cutting edge was optimized using the Taguchi method combined with a grey relation analysis and fuzzy inference. Finally, orthogonal experiments, the Taguchi method, and analysis of variance (ANOVA) were used to investigate the effect of the polishing plate's rotational speed, swing angle, and input frequency of the controller on the edge preparation process, and the parameters were optimized. The results showed that the best parameter combination for the polishing slurry for the cemented carbide inserts was the mass concentration of the abrasive particle of 10 wt%, the mass concentration of the oxidant of 10 wt%, the mass concentration of the dispersant of 2 wt%, and the pH of 8. The CMSP process parameter combination for the linear edge had the polishing plate's rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 5000 Hz. The optimum CMSP process parameter combination for the circular edge had the polishing plate's rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 7000 Hz. The polishing plate's rotational speed had the most significant impact on the edge preparation process, followed by the swing angle, and the effect of the input frequency of the controller was the smallest. This study demonstrated that CMSP is a potential way to treat the cemented carbide insert cutting edge in a tool enterprise.

Parametric Modeling for Optimization of Deposition Rate in Wire Arc Additive Manufacturing of AISI 410 Stainless Steel Using Bead-on-Plate Specimens

The deposition rate is a critical parameter that has a significant influence in Wire Arc Additive Manufacturing (WAAM) of critical aerospace components made of AISI 410 Stainless steel. In this work, Taguchi parametric modeling for wire arc additive manufacturing is carried out using the input parameters of the welding current (WC), the travel speed of the nozzle (TSN), and the distance between the nozzle and the workpiece (DNW). Each factor is varied at 3 levels and the experiments are carried out based on Taguchi L9 orthogonal array. The response variable considered is the deposition rate (DR). Based on the results from experiments, the optimal combination of process parameters obtained is 140 A of welding current, 3mm/s of the travel speed of the nozzle, and 15 mm of distance between the nozzle and workpiece. The optimum deposition rate achieved is 8.545 g/s. The percentage of contribution of various factors is found to be TSN at 43.65% and WC at 16.6% and DNW at 4.9%.

Ultrasound assisted aqueous two-phase extraction of polysaccharides from corn stigma: Process optimization, structure characterization, and immunomodulatory activity

The two models of ultrasound assisted aqueous two-phase extraction (UATPE) of polysaccharides from corn stigma were established by response surface methodology (RSM) and artificial neural network (ANN), and compared the prediction ability of RSM and ANN models. Subsequently, the extraction process of corn stigma polysaccharides (CSPs) was optimized by the combination of genetic algorithm (GA) and ANN. The results show that two models could precisely predict the CSPs yield, whereas the prediction ability of ANN was better than RSM. The optimum combination of process parameters was as follows: the ultrasound power of 466 W, extraction temperature of 54 °C, liquid-to-solid ratio of 47 mL/g, and extraction time of 31 min. The highest CSPs yield was 9.89% ± 0.14% under the above parameters, and then the crude CSPs were further purified by DEAE-52 and Sephadex G − 100 chromatography to obtain a homogenous fraction (CSPs-2-SG with a molecular weight of 2.86 × 106 Da) that consisted of Man, GlcA, GalA, Glc, Gal, and Xyl with a molar ratio of 9.14:16.35:11.27:91.08:6.49:4.71. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) demonstrated that CSPs-2-SG showed irregular structures including uneven fragments, some holes, and spherical aggregates on the surface. Finally, the immunomodulatory activity of CSPs-2-SG was evaluated by RAW264.7 cells. CSPs-2-SG could improve viability, phagocytic rate, and NO content of RAW264.7 cells. Moreover, CSPs-2-SG could increase the levels of IL − 1β, IL-6, and TNF-α in RAW264.7 cells. The findings provide important references for utilizing corn stigma as a potential natural immune regulator.

Optimization of Process Parameters to Minimize the Surface Roughness of Abrasive Water Jet Machined Jute/Epoxy Composites for Different Fiber Inclinations

Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

A Study to Investigate the Influence of Machining Parameters on Delamination in the Abrasive Waterjet Machining of Jute-Fiber-Reinforced Polymer Composites: An Integrated Taguchi and Response Surface Methodology (RSM) Optimization to Minimize Delamination

In recent years, abrasive waterjet machining has emerged as a promising machining technique for the machining of composites because of its non-thermal nature. In the present study, the effect of machining parameters on the quality of machining has been studied and the process parameters have been optimized to machine jute-fiber-reinforced polymer composites. The design of the experiment was used to predict the combination of the input parameters for L27 experiments. Taguchi and response surface methodology (RSM) techniques were employed to analyze the experimental data and identify the optimum combination of process parameters to achieve as little delamination as possible. The results indicate that an increase in the values of the parameters traverse speed and abrasive mass flow rate leads to an increase in the damage obtained. However, an increase in the value of the parameter stand-off distance minimizes the damage produced. To achieve minimum delamination, the optimum combination of input parameters is obtained through Taguchi and RSM. For the present experimental condition, to achieve minimum delamination, the parameter traverse speed should be set at 20 mm/min, the stand-off distance should be 4 mm, and the abrasive flow rate should be set at 0.25 kg/min. The results confirm that the optimum combination of parameters obtained through both approaches is similar. This investigation results indicated a significant improvement in the cutting quality with reduced damage, achieved through the optimized process parameters. For the considered range of parameters, graphs are plotted such that any intermediate values can be anticipated within the considered range without performing any further experiments. The present work signifies the effect of fiber orientation on delamination.

Preparation process of shear thickening gel based on constrained uniform mixture design and orthogonal experimental design

The relative shear thickening effect (RSTE) of shear thickening gel (STG) is influenced by nine factors, including the mass ratio of four components and five process parameters. To obtain high RSTE, the constrained uniform mixture design and orthogonal experimental design were implemented to obtain the optimal ratio of each component and optimal process parameter combination, respectively. Then, validation experiments were carried out to verify the accuracy of the obtained results. The optimal mass ratio is m(polydimethylsiloxane, PDMS):m(pyroboric acid, PA):m(anhydrous ethanol, AE):m(benzoyl peroxide, BPO) = 66.42:21.48:10.66:1.45. The impact degrees and optimal process parameter combinations are PDMS viscosity (500 mm2/s)>reaction temperature (240 °C)>curing time (60min)>reaction time (600min)>curing temperature (120min). The RSTE averages obtained from the validation experiments were all above 300000 %, with a maximum of 345843 %. STG with high RSTE can be obtained through experimental analysis, shedding light on improving the subsequent preparation process of STG.

Multi objective optimization of process parameters in hard turning of AISI 52100 steel with surface irregularities using GRA-PCA

Dry hard-turning is a cost-effective, efficient manufacturing method for AISI 52100 hardened bearing steel. Surface Defect Machining (SDM) is a novel approach to address surface roughness, deteriorations, residual stresses, and metallurgical changes on machined steel. SDM involves exposing workpieces to surface irregularities, reducing cutting resistance, and enhancing surface integrity and finish. In the present work, surface irregularities are formed on the surface of the workpiece in the form of indentations. Using the response surface method’s central composite design (CCD), 32 experimental runs were conducted to determine the optimal process parameters by varying the cutting and tool geometry parameters while AISI52100 steel hard turning (HT). Due to its complexity, multi-objective optimization is more challenging to study. The present work aims to evaluate the effects of input parameters on maching force, surface roughness, and workpiece surface temperature. Further, machining parameters optimization is performed employing the Grey relational analysis integrated with principal component analysis (GRA-PCA). Analysis of variance (ANOVA) was used to examine the impact of cutting and tool geometry parameters on grey relational grade (GRG). ANOVA revealed that feed has the highest influence on GRG, followed by depth of cut, nose radius, cutting speed, and negative rake angle. Cutting speed of 800 rpm, feed rate of 0.04 mm/rev, depth of cut of 0.5 mm, nose radius of 1 mm, and negative rake angle of 15° are the optimum combination of process parameters.

Co-Optimization of the Preparation Process of Ni-Based Self-Lubricating Coatings by Magneto-Thermal-Assisted Laser Cladding

To reduce the metallurgical defects that are prone to occur in the preparation of nickel-based self-lubricating coatings, a method of process co-optimization for magneto-thermal-assisted laser cladding of nickel-based self-lubricating coatings is proposed in this paper. The laser energy density, preheating temperature, and electromagnetic intensity are selected as input factors; the prediction models of coating dilution rate, porosity and microhardness are established by the CCD test method; the interactive effects of the magnetic-thermal-assisted cladding process on the coating response are analyzed, and the optimal process parameter combinations are obtained by using the optimization method of MOPSO-AE-TOPSIS. Finally, the coatings under the parameters are successfully prepared. The results show that the optimal process parameter combinations obtained are laser energy density of 56.8 J/mm2, preheating temperature of 350 °C, electromagnetic intensity of 49.1 mT, and the error of the experimental results with this parameter is less than 3% from the algorithm optimization results. When the microstructure of unassisted and magneto-thermal-assisted fields are analyzed by comparison, it is found that the tissues are more homogeneous and finer, and the distribution of graphite is more homogeneous, which proves the effectiveness of the optimization method.

Effects of process parameters on mechanical properties and interface of 3D printed bamboo‐inspired CCFR‐PLA/TPU composites

AbstractInspired by the exceptional strength and toughness of fresh bamboo, a low‐cost method for preparing continuous fiber‐reinforced multi‐matrix composites based on 3D printing was proposed in this paper. Through the design of a special extrusion nozzle, the controllable feed of polylactic‐acid (PLA) and thermoplastic polyurethane (TPU) filaments was achieved, allowing for the preparation of continuous carbon fiber reinforced PLA/TPU composites. The effects of various process parameters on the tensile and flexural properties of the CCFR‐PLA/TPU composites were investigated. Additionally, the relationship between process parameters and the formation of the matrix‐fiber interface and PLA‐TPU interface was analyzed. The results show that layer height has the greatest influence on mechanical properties, while the printing speed has the least influence. The mechanical properties of the CCFR‐PLA/TPU composites and the unreinforced blend matrix, prepared using the optimal combination of process parameters, were tested. The results demonstrated that the tensile strength and flexural strength of the CCFR‐PLA/TPU composites increased by 16.95 and 1.80 times, respectively, compared to the unreinforced blend matrix. Further, inspired by the overall structure of bamboo, a stiffness gradient CCFR composite material was designed and prepared. The methodology employed in this study holds significant implications for the advancement of novel multi‐matrix hybrid continuous fiber reinforced composites, particularly in aerospace and related industries.Highlights Multi‐matrix continuous fiber reinforced composites were prepared by 3D printing inspired by fresh bamboo. The effects of process parameters (nozzle temperature, printing speed, extrusion width, layer height) on mechanical properties were investigated. The optimal printing parameter combination was determined by range analysis. Stiffness gradient composites were prepared inspired by the overall structure of bamboo.

Optimization of process parameters for preparing the hydrophobic surface of aluminum alloy by laser-assisted electrochemical composite etching based on response surface method

To explore the influence of electrolyte concentration, single pulse energy, current density and scan times on the hydrophobic properties of laser-assisted electrochemical composite etching 2024 aluminum alloy surface, the experimental platform of laser-assisted electrochemical composite etching is built. Based on the results of the single-factor experiments, the response surface method (RSM) is chosen to optimize the process parameters. The optimum combination of process parameters is obtained, and the accuracy of the regression equation is verified by experiments. Through residual analysis, the established regression model fits well. By analysis of variance (ANOVA), the sequence of influence of the factors on apparent contact angle (from large to small) is single pulse energy, scan times, electrolyte concentration, and current density. With the maximum apparent contact angle as the goal, the combination of process parameters adjusted after optimization is: electrolyte concentration 2.0 mol/L, single pulse energy 20µJ, current density 18 mA, and scan times 15. Under these conditions, the experimental values of apparent contact angle is 157°, which is close to the predicted value (158°) by the models. The relative errors is 0.6%. It indicates that the regression model is accurate and reliable. This study shows that RSM is an effective method to optimize the process parameters and obtain the ideal experimental results.

Preparation and Characterization of Nano-Silver-Loaded Antibacterial Membrane via Coaxial Electrospinning.

The coaxial electrospinning process has been widely used in the biomedical field, and its process parameters affect product quality seriously. In this paper, the influence of key process parameters of coaxial electrostatic spinning (solution concentration, electrospinning voltage, acceptance distance and liquid supply velocity) on the preparation of a membrane with Chitosan, Polyethylene oxide and nano-silver as the core layer and Polycaprolactone as the shell layer was studied. The optimal combination of key process parameters was obtained by using an orthogonal test, scanning electron microscope, transmission electron microscope and macro-characterization diagram. The results showed that the coaxial electrospun membrane had good mechanical properties (tensile strength is about 2.945 Mpa), hydrophilicity (the water contact angle is about 72.28°) and non-cytotoxicity, which was conducive to cell adhesion and proliferation. The coaxial electrospun membrane with nano-silver has an obvious inhibitory effect on Escherichia coli and Staphylococcus aureus. In summary, the coaxial electrospun membrane that we produced is expected to be used in clinical medicine, such as vascular stent membranes and bionic blood vessels.

Investigation on the performance of wire electrical discharge machining (WEDM) using aluminium matrix composites (AMCs) micro-channel

This study focuses on microchannel fabrication of Al-10% SiC composite using Wire-EDM process with the applications of single variable and multi variable, optimization. This work was targeted keeping in considerations the wider scope of microchannel applications in the fields such as medical, aerospace, automobile, etc. In this research work, the experiment was conducted to achieve high precision and quality surface finish in terms of minimum surface roughness (SR), high material removal rate (MRR), low tool wear rate (TWR) and optimum spark gap (SG). The used experiments were design on the basis of Taguchi design of experiment approach. Four input process parameters were considered namely, pulse on time (Ton), sparking voltage (SV), wire tension (WT), and wire feed rate (WFR). For the experimentation task, the workpeice (Aluminum and silicon carbide (10% Wt SiCmicro)) material, in a desired shape was fabricated through stir casting process. The tool electrode applied for the experiment purpose had specification as, 0.25 mm diameter brass alloy wire (copper 63 percent and zinc 37 percent). For single variable optimization ANOVA (analysis of variance) was applied while as, for multi variables optimization, grey relational analysis (GRA) was applied. The results of the study indicated that, on the basis of single variable optimization, the highest contribution of Ton resulted in case of the performance measures, MRR and SR while as, for TWR and SG, the highest contribution of sparking voltage, observed. Furthermore, the findings of the study indicated that, on the basis of multi variables optimization, the optimal combination of input process parameters is, pulse on time 125 μs, sparking voltage 10 volt, wire tension 12 kgf, and wire feed rate 4 m min−1. These results also get support from the previous research works. The findings of this study be useful for microchannel manufacturing and other related industrial applications.

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.

Experimental Study on Carbon Fiber-Reinforced Polymer Groove Machining by High-Power Water-Jet-Guided Laser.

Due to the excellent properties of carbon fiber-reinforced polymers (CFRPs), such as high strength and strong corrosion resistance, the traditional water-jet-guided laser (WJGL) technology has problems with fiber pull-out and has a small cutting depth when processing CFRPs. Therefore, in this study, we used high-power water-jet-guided laser (HPWJGL) technology to perform groove processing experiments on CFRPs. The effects of four key process parameters, high laser power, pulse frequency, feed rate, and water-jet pressure, on the cutting depth were investigated by a single-factor experiment. The formation mechanism of groove cross-section morphology and the processing advantages of high-power water-jet-guided lasers were analyzed. On this basis, the mathematical prediction model of cutting depth was established by using the response surface method (RSM), and the optimal combination of process parameters was obtained. The mathematical prediction model was verified by experiments, and the error was only 1.84%, indicating that the model had a high reference value. This study provides a reference for the precision machining of HPWJGL technology.

Multi-Objective Optimization in Ultrasonic Polishing of Silicon Carbide via Taguchi Method and Grey Relational Analysis.

As high-level equipment and advanced technologies continue toward sophistication, ultrasonic technology is extensively used in the polishing process of difficult-to-process materials to achieve efficiently smooth surfaces with nanometer roughness. The polishing of silicon carbide, an indispensable difficult-to-machine optical material, is extremely challenging due to its high hardness and good wear resistance. To overcome the current silicon carbide (SiC) ultrasonic polishing (UP) process deficiencies and strengthen the competitiveness of the UP industry, the multi-objective optimization based on the Taguchi-GRA method for the UP process with SiC ceramic to obtain the optimal process parameter combination is a vital and urgently demanded task. The orthogonal experiment, analysis of variance, grey relational analysis (GRA), and validation were performed to optimize the UP schemes. For a single objective of roughness and removal rate, the influence degree is abrasive size > preloading force > abrasive content > spindle speed > feed rate, and spindle speed > abrasive size > feed rate > preloading force > abrasive content, respectively. Moreover, the optimal process combination integrating these two objectives is an abrasive content of 14 wt%, abrasive size of 2.5 μm, preloading force of 80 N, spindle speed of 8000 rpm, and feed rate of 1 mm/s. The optimized workpiece surface morphology is better, and the roughness and removal rate are increased by 7.14% and 28.34%, respectively, compared to the best orthogonal group. The Taguchi-GRA method provides a more scientific approach for evaluating the comprehensive performance of polishing. The optimized process parameters have essential relevance for the ultrasonic polishing of SiC materials.

Longitudinal-Torsional Ultrasonic Grinding of GCr15: Development of Longitudinal-Torsional Ultrasonic System and Prediction of Surface Topography.

The common material of bearing rings is GCr15 bearing steel which is a typical difficult-to-machine material. As an important working surface of the bearing, the inner surface of the raceway plays a vital role in the performance of the bearing. As an important means to solve the high-performance manufacturing of difficult-to-machine materials, longitudinal-torsional ultrasonic processing is widely used in various types of processing. In the presented work, the basic size of the horn is obtained from the wave equation of the forced vibration, and the modal analysis and amplitude test are carried out to verify the rationality of the LUTG structure. Then, according to the probability density function of cutting thickness and the overlapping effect of adjacent abrasive trajectories, the LUTG surface topography prediction model is established by using the height formula of the surface residual material, and the model reliability is verified by using the orthogonal test. The error between the test results and the prediction model is within 13.2%. Finally, based on the response surface method, the optimal process parameters that can meet the requirements of low roughness (Ra) and high material removal rate (MRR) are screened, and the optimal combination of process parameters is obtained as follows: A = 4.5 μm, n = 6493.3 r/min, ap = 28.4 μm, and vf = 21.1 mm/min.

A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites

This work investigates the efficacy of high-pressure torsion (HPT), as a severe plastic deformation mechanism for processing plain and silicon-carbide-reinforced AA6061, with the broader objective of using the technique for improving the properties of lightweight materials for a range of objectives. The interactions between input variables, such as the pressure and equivalent strain (εeq) applied during HPT processing, and the presence of SiCp and response variables, like the relative density, grain refinement, homogeneity of the structure, and the mechanical properties of the AA6061 aluminum matrix, were investigated. Hot compaction (HC) of the mixed powders followed by HPT were employed to produce AA6061 discs with and without 15% SiCp. The experimental findings were then analyzed statistically using the response surface methodology (RSM) and a machine learning (ML) approach to predict the output variables and to optimize the input parameters. The optimum combination of HPT process parameters was confirmed by the genetic algorithm (GA) and ML approaches. Furthermore, the constructed ML and RSM models were validated experimentally by HPT processing the same material under new conditions not fed into the models and comparing the experimental results to those predicted by the model. From the ML and RSM models, it was found that processing the AA6061/SiCp composite HPT via four revolutions at 3 GPa produced the highest mechanical properties coupled with significant grain refinement compared to the HC condition. ML analysis revealed that the equivalent strain induced by the number of revolutions was the most effective parameter for grain refinement, whereas the presence of SiCp played the highest role in improving both the hardness values and the compressive strength of the AA6061 matrices.

Optimization of Selective Laser Sintering Three-Dimensional Printing of Thermoplastic Polyurethane Elastomer: A Statistical Approach

This research addresses the challenge of determining the optimal parameters for the selective laser sintering (SLS) process using thermoplastic polyurethane elastomer (TPU) flexa black powder to achieve high-quality SLS parts. This study focuses on two key printing process parameters, namely layer thickness and the laser power ratio, and evaluates their impact on four output responses: density, hardness, modulus of elasticity, and time required to produce the parts. The primary impacts and correlations of the input factors on the output responses are evaluated using response surface methodology (RSM). A particular response optimizer is used to find the optimal settings of input variables. Additionally, the rationality of the model is verified through an analysis of variance (ANOVA). The research identifies the optimal combination of process parameters as follows: a 0.11 mm layer thickness and a 1.00 laser power ratio. The corresponding predicted values of the four responses are 152.63 min, 96.96 Shore-A, 2.09 MPa, and 1.12 g/cm3 for printing time, hardness, modulus of elasticity, and density, respectively. These responses demonstrate a compatibility of 66.70% with the objective function. An experimental validation of the predicted values was conducted and the actual values obtained for printing time, hardness, modulus of elasticity, and density at the predicted input process parameters are 159.837 min, 100 Shore-A, 2.17 MPa, and 1.153 g/cm3, respectively. The errors between the predicted and experimental values for each response (time, hardness, modulus of elasticity, and density) were found to be 4.51%, 3.04%, 3.69%, and 2.69%, respectively. These errors are all below 5%, indicating the adequacy of the model. This study also comprehensively describes the influence of process parameters on the responses, which can be helpful for researchers and industry practitioners in setting process parameters of similar SLS operations.

Optimization of CNC Milling Parameters Based on Tool Life and Process Carbon Emission Prediction Models

Aiming at the problems of short tool life and high carbon emission in the process of CNC milling, a multi-objective optimization method of process parameters based on the BP neural network and NSGA-II algorithm is proposed. Based on the data sets generated by CNC milling under different process parameters, with tool life and process carbon emissions as outputs and different process parameter combinations as inputs, a BP neural network-based tool life and process carbon emissions prediction model is constructed. Taking the maximum tool life and minimum process carbon emission as the optimization goals, the NSGA-II process parameter multi-objective optimization main model is constructed. The model is then used as the objective function of the main model and optimized to obtain the Pareto optimal solution set. The optimal solution decision is made on the Pareto optimal solution set using the Topsis method, and the optimal process parameter combination is obtained. The optimization results show that the method can accurately predict the tool life and process carbon emission of CNC milling. It can also effectively optimize the process parameters, which provides a new method for the low-carbon optimization of CNC milling parameters.