Selection Of Process Parameters Research Articles

Integration of improved meta-heuristic and machine learning for optimizing energy efficiency in additive manufacturing process

Additive manufacturing (AM) has been a vital element of smart manufacturing. The high energy intensity or environmental sustainability issue of AM, however, has posed a great challenge to the future massive application, particularly laser-based direct energy deposition (L-DED). This study aims to determine the optimal processing parameters for energy-saving without compromising the geometrical appearance. A quantification model for energy efficiency at the process level was established, with two energy efficiency indicators of AM process. Then, a meta-heuristic Mayfly algorithm, augmented with Bayesian technique and mutation strategies, was proposed to improve hyperparameters in a typical machine learning model (XGBoost). Based on the full factorial L-DED experiments, this study compared the improved XGBoost with four types of XGBoost derivatives via four algorithm evaluation metrics. Non-dominated sorting genetic algorithm II was adopted to optimize the processing parameters subject to the constraints of geometrical appearance. Results indicated that the proposed algorithm outperformed other XGBoost derivatives in terms of prediction accuracy and convergence rate. The energy efficiency could be improved by 76.35 J/g or 6.78 % on average while ensuring the geometry of the deposited layers. This study could help enhance energy-efficient additive manufacturing via proper processing parameters selection and facilitate the sustainability in AM domain.

Prediction of Geometric Characteristics of Laser Cladding Layer Based on Least Squares Support Vector Regression and Crested Porcupine Optimization.

The morphology size of laser cladding is a crucial parameter that significantly impacts the quality and performance of the cladding layer. This study proposes a predictive model for the cladding morphology size based on the Least Squares Support Vector Regression (LSSVR) and the Crowned Porcupine Optimization (CPO) algorithm. Specifically, the proposed model takes three key parameters as inputs: laser power, scanning speed, and powder feeding rate, with the width and height of the cladding layer as outputs. To further enhance the predictive accuracy of the LSSVR model, a CPO-based optimization strategy is applied to adjust the penalty factor and kernel parameters. Consequently, the CPO-LSSVR model is established and evaluated against the LSSVR model and the Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP) model in terms of relative error metrics. The experimental results demonstrate that the CPO-LSSVR model can achieve a significantly improved relative error of no more than 2.5%, indicating a substantial enhancement in predictive accuracy compared to other methods and showcasing its superior predictive performance. The high accuracy of the CPO-LSSVR model can effectively guide the selection of laser cladding process parameters and thereby enhance the quality and efficiency of the cladding process.

A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

Aluminum–air batteries are characterized as “green energy for the 21st century” due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural networks. The quick and exact selection of process parameters meets the goals of density, self-corrosion current, and anode usage, hence improving the forming quality and processing efficiency of SLM aluminum anodes. The experimental results show that the process parameter backpropagation model’s parameter configurations match to the real densities and self-corrosion currents, which are somewhat higher than the specified target values. The maximum error rate for the aluminum anode forming quality prediction model is 8.23%. Furthermore, the actual anode utilization rate is somewhat lower than the projected target value, indicating that the backpropagation model can satisfy actual production needs.

Valorization of Hibiscus Flower (Hibiscus sabdariffa L.) Anthocyanins to Produce Sustainable Spray-Dried Ingredients

The recent increase in sustainability awareness has triggered the industry to establish novel, eco-friendly sources of plant-based ingredients. In the present study, hibiscus flowers (Hibiscus sabdariffa L.) were investigated as a sustainable source of anthocyanins for use in spray-dried ingredients with antioxidant capacity. To this end, the extraction and spray-drying microencapsulation of hibiscus flower anthocyanins were optimized and the final products were evaluated for their oxidative stability index. Initially, preliminary experiments were carried out to evaluate the effects of selected processing parameters on anthocyanin extraction. Next, the extraction was optimized through a 22 central composite design, considering ethanol concentration (44–56% v/v) and extraction time (1.6–6.4 h) as independent factors. The optimum extraction conditions (8 h, 41.6% v/v ethanol concentration) were used to produce anthocyanin-rich extracts, which were microencapsulated by spray drying using a 22 central composite design with the carrier addition rate (1–3% w/v) and inlet temperature (160–192 °C) as factors. Maximum values of solids recovery (60.8%) and anthocyanin retention (96.0%) were reached when 3.2% w/v of starch–alginate carrier blend and a 170.7 °C inlet temperature were used. Finally, when hibiscus microcapsules were added to soybean oil, higher oxidative stability was achieved compared to the control. Overall, we demonstrate an industrially friendly and scalable approach that takes advantage of abundant hibiscus flowers as a viable source of anthocyanins for multiple applications.

Computational analysis of the simultaneous application of ultrasound and electric fields in a lipid bilayer

The combined application of electric fields and ultrasonic waves has shown promise in controlling cell membrane permeability, potentially resulting in synergistic effects that can be explored in the biotechnology industry. However, further clarification on how these processes interact is still needed. The objective of the present study was to investigate the atomic-scale effects of these processes on a DPPC lipid bilayer using molecular dynamics simulations. For higher electric fields, capable of independently forming pores, the application of an ultrasonic wave in the absence of cavitation yielded no additional effects on pore formation. However, for lower electric fields, the reduction in bilayer thickness induced by the shock wave catalyzed the electroporation process, effectively shortening the mean path that water molecules must traverse to form pores. When cavitation was considered, synergistic effects were evident only if the wave alone was able to generate pores through the formation of a water nanojet. In these cases, sonoporation acted as a mean to focus the electroporation effects on the initial pore formed by the nanojet. This study contributes to a better understanding of the synergy between electric fields and ultrasonic waves and to an optimal selection of processing parameters in practical applications of these processes.

A novel alloy design approach in developing CoNi-based high entropy superalloy using high entropy alloys thermodynamic and spark plasma sintering

This study introduces an innovative alloy design strategy by integrating HEAs principles to develop CoNi-based high entropy superalloys (HESAs). The powder of designed HESA produced using gas atomization and consolidated using spark plasma sintering (SPS). The alloy design strategy validated by CALPHAD to mitigate detrimental phase precipitation and segregation during powder production, material consolidation, and post-processing heat treatments. CoNi-HESA powder with a calculated mixing entropy of 1.568R was successfully developed and consolidated using SPS. Optimization of SPS parameters at 1175 °C, 10 min, and 100 °C/min achieved a relative density of 99.9 %. Subsequent heat treatments further improved the material's characteristics. Solutionizing at 1190 ± 10 °C for 2 h with water quenching resulted in a fine grain structure of ∼7 μm grain size. Aging at 900 °C for 24 h, followed by water quenching, yielded uniformly distributed fine γ′ precipitates comprising approximately 65 % of the γ matrix, without unwanted secondary precipitates or TCP phases. This study demonstrates the effectiveness of the novel approach in designing CoNi-based HESAs and optimizing their properties through careful process parameter selection and post-heat treatment cycles. The combination of thermodynamic calculations, HEA principles, and powder metallurgy techniques offers a promising approach for developing advanced high entropy superalloys with tailored microstructures.

Viscosity Calculation for Al‐Si‐Mg‐Fe System through CALPHAD Method

Viscosity is a crucial parameter affecting the fluidity of metal melts, which directly influences founding properties of Al alloys. However, obtaining viscosity measurement data is difficult for metal melts, and the viscosity prediction for ternary and multi‐component alloys would provide the significant data for selection of process parameters. This study compares the applicability of the Hirai model, SDS model, and R‐K function for viscosity calculations in the sub‐binary systems of Al‐Si‐Mg‐Fe alloys. R‐K function shows the best agreement with experimental data. However, the SDS model shows lower relative error than Hirai model which would play an important role in those system lacking experimental data to predict the viscosities. Ultimately, a database capable of predicting viscosity values for the entire composition and temperature range of the Al‐Si‐Mg‐Fe system was established using the CALPHAD method. Viscosity parameters for Al‐Si, Al‐Mg, Al‐Fe, Mg‐Si, and Mg‐Fe were evaluated and optimized through the R‐K derivation, corroborated with existing experimental data. Using the R‐K function, successful extrapolation and prediction of viscosity for the Al‐Si‐Mg‐Fe ternary and quaternary systems were achieved, with a mean square error between predicted and experimental values of only 0.7%, demonstrating the successful application of the Al‐Si‐Mg‐Fe database for viscosity prediction.This article is protected by copyright. All rights reserved.

Multi-objective optimization of high-power fiber laser cutting process using data augmentation-based ANN-Adam model

During fiber laser cutting of sheet metal, excessive heat-affected zone (HAZ) and recast layer thicknesses (RLT) can adversely affect subsequent precision machining and welding processes on the parts. As a result, it is necessary to optimize the process parameters to control the HAZ and RLT. In this work, laser power, gas pressure, focal plane position (FPP), and cutting speed are selected as input parameters. The cutting experiments are conducted using response surface methodology, and the factors that significantly affect the cutting quality are identified by analysis of variance (ANOVA). To improve the generalization ability and robustness of the model, a data augmentation-based artificial neural network (ANN) model is developed to achieve accurate prediction of cut quality characteristics. Further, this study performs a multi-objective optimization of the cutting process parameters using the Adam optimizer. The research results show that the mean relative errors of the ANN model in predicting the HAZ and RLT are 2.44 % and 1.55 %, respectively. The determination coefficients R2 are 0.9542 and 0.9779, respectively. The optimal combinations of process parameters obtained through optimization are laser power 8.9 kW, gas pressure 0.8 bar, FPP 11.8 mm, and cutting speed 1.3 m/min. Experimental validation shows that the relative errors between the predicted and the experiment values of the cut quality characteristics are kept within 5 % under the optimal process parameters. This fully verifies the effectiveness of the optimization model in guiding the selection of laser-cutting process parameters and improving cutting quality.

Artificial Intelligence Modeling of the Heterogeneous Gas Quenching Process for Steel Batches Based on Numerical Simulations and Experiments

High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production.

The Influence of Selected Process Parameters on the Efficiency of the Process of Gas Nitriding of AISI 1085 Steel.

The main aim of the manuscript was to investigate the impact of modifying the parameters of the gas nitriding process of samples made from AISI 1085 steel on the course and results of the process carried out in a chemical reactor allowing for thermogravimetric measurements. The tested steel was subjected in a chemical reactor to the process of gas nitriding in the temperature range of 490-580 °C, using different sample heating rates (in the range of 1-25 °C/min) and various mixtures of nitriding gases (pure NH3, or NH3 with the addition of H2 or N2). To assess the impact of the tested process parameters on its efficiency, the thickness of the nitrided layers produced, the change in sample mass, the structure of the phases produced, the phase composition and the microhardness were examined. For the research methodology used, it was found that reducing the amount of NH3 and/or using H2 or N2 admixtures adversely affects the thickness of the nitride layers produced. At the same time, the use of a lower maximum process temperature with the same gas mixture resulted in a significant difference in the thickness of the layers. It was also found that the use of pure NH3 or a gas mixture (NH3 + H2) with higher NH3 contents resulted in higher surface microhardnesses of the samples and that for these samples, the hardness increased to a greater depth.

The effect of fine particle peening on aviation gear performance, part II: Rolling contact fatigue

AbstractFine particle peening (FPP) technology offers an effective method to achieve outstanding surface integrity and fatigue performance in aviation gears. However, the lack of sufficient research on the correlation between process parameters, surface integrity parameters, and fatigue life undermines the full potential of FPP in strengthening aviation gears. Therefore, we have developed an elastic–plastic contact fatigue life model to predict the fatigue life of AISI 9310 gear steel after different strengthening processes. This model can consider surface integrity parameters such as surface micro‐topography, residual stress gradient, and hardness gradient. To validate the model, we conducted rolling contact fatigue tests and compared the results with the predictions from our model. The influence of process parameters on the rolling contact fatigue of AISI 9310 gear steel was further investigated. A process window for FPP was established, and an optimization method was proposed in order to provide guidance for selecting process parameters for FPP on high‐performance aviation gears.

Determination of selective laser melting process parameters of tungsten carbide powders coated with nickel via electroless plating for improved interface properties

Tungsten carbide powders are commonly used at various industrial applications due to their high hardness. However, these powders may exhibit low binding and processing challenges without a binder. Therefore, various binding methods such as electroless Ni coating are used to hold WC powders together, increase their processability and improve the mechanical properties of the final product. Electroless Ni plating plays an important role in the use of WC powders, especially in processes such as selective laser melting. The structural, tribological, and mechanical properties of parts manufactured by SLM method are significantly influenced by the parameters employed in the process. Hence, optimizing process variables is essential to unlock the full potential of this advanced manufacturing technique. The main objective of this study was to identify the optimum process parameters for the SLM method of WC-Ni composite powders, which were prepared by coating WC powder materials with electroless Ni. Experimental investigations focused on analyzing the laser power, scanning speed, and hatch spacing parameters. The effects of these parameters on the mechanical and tribological properties of WC-Ni composite material were investigated using microhardness tester, tribometer device, optical microscope, 3D optical profilometer and scanning electron microscope. Eventually, it was determined that the manufacturing parameters employed in the SLM method had a significant impact on relative density, microstructure, surface roughness, hardness, and tribological properties. Samples exhibited a relative density of nearly 99%, and the highest observed hardness value was 1962 HV. This article supports a specialized SLM manufacturing process developed specifically to enhance durability and performance across a broad spectrum of industrial applications through interface improvement and the optimization of manufacturing parameters.

Selection and Optimization of Carbon-Reinforced Polyether Ether Ketone Process Parameters in 3D Printing-A Rotating Component Application.

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Effect of in-situ activated core-shell particles on fatigue behavior of carbon fiber reinforced thermoplastic composites

In this unique study, the effect of adding core-shell particles (CSPs) on fatigue performance of carbon-fiber reinforced PA6 (CF-PA6) laminates is investigated. The thermoplastic laminates were prepared using compression molding and were reinforced at ply interfaces with 2 wt% and 4 wt% CSPs of the polymer mass. A manual method was used to disperse CSPs using a sieve and carefully selected process parameters. The cyclic tests were conducted and assessed, considering S–N curve, stiffness degradation, and energy dissipation. Consequently, the fatigue life of modified composites improved respectively by eight and four times when 2 wt% and 4 wt% CSPs were used. The results showed that an optimal improvement was achieved with a 2 wt% CSPs. The fatigue strength coefficient and fatigue strength exponent of CF-PA6 composites improved by 22.13 % and 9.85 %, respectively. The findings have the potential to establish a new frontier in thermoplastic research and would help designers to enhance the fatigue properties of thermoplastic laminates in specific elastic tailoring structures.

Casting of nickel powder based MMC using microwave energy

In this paper, an experimental setup is developed to cast a metal matrix composite (MMC) specimen, by using microwave energy as a heat source. A MMC (Nickel and 15 % of SiC by weight percentage) specimen of size 140 × 30 × 2 mm3 is in-situ cast by using a domestic microwave applicator at 2.45 GHz frequency, and 900 W, power output. As per selected process parameters, the exposure time to cast MMC specimen, inside an electromagnetic environment is optimized as 33 min. A COMSOL Multiphysics software tool is also used to model the in-situ microwave-assisted casting setup. Numerical simulations are performed to visualize the microwave heating effects within the applicator cavity. Microwave heating effects are analyzed in terms of electric field distribution, resistive heating and temperature profile. As per the simulation study results, maximum electric field intensity (4.46 × 104 (V/m)) is observed around the mold. Resistive heating losses are maximum (4 .9× 109 W/m3) in the susceptor zone. It is observed from the simulation study, that uniform heating of the specimen is observed throughout the cross-section and the melting temperature of the specimen is achieved in 38 min. A good correlation is observed between experimental and simulation study results in terms of exposure time of cast specimen and microwave heating effects within the applicator cavity

Mapping the roles of scan strategy and build orientation in predicting the crystallographic texture and yield strength of 316L stainless steel produced by laser powder bed fusion

Laser Powder Bed Fusion (L-PBF) has emerged as a leading additive manufacturing (AM) technique for manufacturing complex metal parts, with 316L stainless steel being favored for its exceptional corrosion resistance and mechanical properties. While previous studies have explored the effects of different scan strategies and build orientations on the microstructure and room temperature tensile properties of L-PBF printed 316L stainless steel, there is still a lack of a comprehensive predictive model that accurately captures the combined effects of these process parameters on the final crystallographic texture and tensile properties. This study presents a novel approach that establishes a direct correlation between process parameters, microstructure, and mechanical behavior by modeling the influence of scan strategy rotation angles and build orientations on the crystallographic texture of L-PBF-manufactured 316L stainless steel. To validate the model, a series of samples were fabricated using various scan strategy rotation angles and build orientations, enabling detailed microstructural analysis and mechanical testing. The findings demonstrate a direct relationship between the selected process parameters and the resulting mechanical properties, underscoring the importance of scan strategy and build orientation selection on mechanical response. Furthermore, our calibrated model exhibits high predictive accuracy as validated through a comparison with experimental data.

Comparing surface characteristics of Cu-Al-Fe alloys using thermal-based machining processes

The exceptional shape memory properties and load-handling capabilities of shape memory alloys makes them perfect for a spectrum of applications, such as actuators in robotics, control of aircraft wings and biomedical applications. Other than nickel-titanium shape memory alloy there are also various combinations of elements that show the same memory effect, namely Cu-Al-Fe, Cu-Al-Zn, and Cu-Al-Fe-Mn. However, a major problem often associated with shape memory alloys is the technical difficulty associated with machining them. Consequently, they do not tend to last longer when put to use. An optimal selection of input process parameters has a direct influence on the physical properties of these compounds. The traditional machining process, such as drilling, milling and grinding, pose a great threat to their properties. Hence, the research community has focused their study towards unconventional processes, such as (i) Wire Electric Discharge Machining, and (ii) Laser Beam Machining. In this research study, machining of the Cu-Al-Fe shape memory alloy was performed using three unconventional machining processes, namely (i) electric discharge machining (EDM), (ii) laser beam machining (LBM), (iii) powder-mixed electric discharge machining (PMEDM) and the effect of input process parameters were analysed. Various characterization tests provide useful information specific to changes in the microstructure and thermal behaviour of the alloy. The results show that the samples machined with PMEDM, EDM and LBM have surface roughness values of 2.377, 3.120 and 3.432 µm, respectively, which proves that PMEDM is best suited for machining the CuAlFe smart alloy.

Research on the calculation method of grinding temperature field of spiral bevel gear based on generative machining

At present, the process parameter setting of spiral bevel gear grinding is mainly based on an empirical method, and the tooth surface temperature is difficult to estimate effectively during grinding, which may lead to excessive tooth surface temperature and surface burn caused by improper selection of process parameters. Aiming at the above problems, this paper presents a method for calculating the temperature field of spiral bevel gear forming based on a combination of analytical and numerical methods. The motion equation of spiral bevel gear grinding is derived based on the method of tooth surface development. The time-varying heat flow calculation method in tooth surface grinding was developed through the discrete and two-dimensional contact zone. The time-varying temperature field analysis method of spiral bevel gear forming is developed using the time-varying heat flow and finite element calculation method. The related research can realize the temperature prediction of grinding tooth surfaces and provide the basis for improving grinding technology.

Atomistic analysis on implantation effects of hydrogen ions and copper ions into 4H-SiC

The microstructure evolution of 4H-SiC under the implantation of hydron ions and copper ions was investigated using molecular dynamics (MD) simulations and experimental analysis. MD models were developed to simulate the implantation of single Cu2+ and H+ ions into SiC, analyzing lattice damage, amorphous defects, and temperature distribution. H+ ion implantation resulted in a higher number of amorphous atoms compared to Cu2+ ions. The maximum atomic temperature during Cu2+ ion implantation was significantly higher (1635 K) than during H+ ion implantation (950 K), although the volume of the temperature rise region was larger in the H+ model. Cu2+ ions induced more severe lattice vibrations than H+ ions. Atom movement along regions of weak lattice binding energy was observed. The study also examined overlapping multiple ion implantations, considering ion type, initial energy, and incident angle. Cu2+ ions exhibited smaller sputtering yield, projection range, number of amorphous atoms, and energy increment compared to H+ ions. A 0° incident angle resulted in higher sputtering yield and increased amorphous structure formation due to the channeling effect, which was more pronounced in the H+ model. The experimental results demonstrate that compared to Cu2+ ions, H+ ions have a greater impact on the surface peak roughness, sub-surface damage depth, and surface damage of SiC samples. It can provide a reliable guidance for selecting process parameters and the types of ions to implant.