Combination Of Process Parameters Research Articles

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.

Modelling and numerical analysis for rotatory friction welding of U75V steel rails

This article presents a rotatory friction welding (RFW) method for U75V steel rail, aiming to mitigate challenges related to property discrepancies between the as-welded joints and the rail base metal (BM) and to narrow the heat-affected zone (HAZ) in conventional flash-butt welding (FBW) joints. A rotational intermediate plate is designed for rails with non-axisymmetric cross-sections, necessitating stationary during RFW. Advantages include achieving a relatively uniform welding heat input and maintaining the peak temperature of the contact interface near A1. To implement these concepts, a 2D finite element (FE) model for the RFW process of U75V rail steel rods was established and validated through experiments with identical process parameters. Microstructure predictions derived from continuous cooling transformation diagram confirm that ferrite microstructure is formed near A1 through rail steel RFW. Subsequently, a 3D FE model for intermediate plate RFW steel rails is developed to explore appropriate process parameter combinations. A suitable process parameters combination was identified, ensuring the peak temperature of the majority model contact interface does not exceed A1, resulting in a 76.7% reduction in HAZ (from ∼50 to 11.66 mm), and axial shortening of 8.10 mm, a significant decrease compared to the usual burn-off (30–40 mm) during FBW. These findings underscore the efficacy of this innovative welding solution and emphasize the significance of simulation technology in process optimization.

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

With the rise of global industrialization, the requirements for the operating speed and carrying capacity of high-speed trains are increasingly higher. Because the wear and tear of rails gradually increases during the running of high-speed trains, strengthening or repairing rail surfaces is of paramount significance. Laser-directed energy deposition (DED) exhibits significant advantages in improving surface hardness, corrosion resistance, and abrasion resistance. Because of the multiple interacting optimization objectives, the development of a multi-objective optimization method for process parameters is significant for improving DED deposition quality. Response surface design employs multivariate quadratic regression equations to fit the functional relationship between the factors and the responses, which can be employed to find the optimal process parameters and solve multivariate problems. This study develops a multi-objective optimization model with response surface design and 2D process mappings to visually analyze the effects of scanning speed, laser power, and powder feed rate on aspect ratio, dilution rate, and microhardness. The optimal combination of process parameters for Ni-based alloys on U71Mn rail is a laser power of 431 W, a scanning speed of 5.34 mm/s, and a powder feed rate of 1.03 r/min. In addition, a multi-physics field finite element model is developed to analyze the evolution mechanism of the microstructure from the bottom to the top of the single track. This study can provide theoretical and technical support for the surface strengthening or repair of U71Mn rail.

Friction Stirs Processing - State of The Art Process to Fabricate Metal Matrix Composites

Friction stir processing(FSP) is the solid state method to fabricate the surface metal matrix composites(MMC). This process has been derived from the concept of Friction stir welding. As this process is performed in solid state, many defects related to phase transformation can be avoided. As this process is done below the melting point temperature of metal or alloy, it is easier to control the process. Machine variables (tool rotational speed, tool traverse speed, tool tilt angle etc.), tool design variables (diameter, profile and height of probe and shoulder of the FSP tool), and material properties are the main process variables to perform FSP in order to fabricate Metal matrix composite. In addition to these variables number of passes, tool traverse patterns, reinforcement particles’ content and its’ types also can be considered as process variables. Tribological properties’ comparison can be done between alloy and MMC fabricated by FSP for different combinations of process parameters. Microstructural study also can be carried out in order to see the effects of above process parameters. In this paper the above aspects are summarized and performance assessment done by various researchers has been covered.

Optimal Constrained Groove Pressing Process Parameters Applying Modified Taguchi Technique and Multi-Objective Optimization

Most engineering problems are complicated, and developing mathematical models for such problems requires understanding the phenomena through experiments. It is well known that as processing parameters with assigned levels increase, so does the number of experiments. By minimizing the number of experiments, Taguchi’s method of experimental design will help to furnish the idea of full factorial experimental design. Taguchi’s method is more appropriate for single-objective optimization problems and needs modifications while dealing with multi-objective optimization problems. Aluminum alloys are in great demand in today’s automotive and aerospace sectors due to their low density, good corrosion resistance, and excellent machinability. The material is subjected to a constrained groove pressing (CGP) process to obtain microstructural grain refinement with enhanced mechanical behavior. This paper considers AA6061 material having major alloys such as silicon and magnesium. For this work, 3 CGP process parameters (viz., displacement rate, plate thickness and number of passes) are assigned 3 levels to each parameter, acquired the test data, viz., grain size (gs), micro hardness (hs), and tensile strength (ult) based on L9 orthogonal array of Taguchi. Using a modified version of Taguchi’s methodology, it is possible to estimate the range of grain size (gs), micro hardness (hs), and tensile strength (σult) for effective combinations of the CGP processing parameters and validate the results with existing test data. A more dependable and simpler multi-objective optimization procedure is used to choose the optimal CGP processing parameters.

Study on the Microstructure, Corrosion Resistance and Dielectric Properties of Atmospheric Plasma-Sprayed Y2O3 Ceramic Coatings

Atmospheric plasma spraying (APS) is one of the most efficient processes for the preparation of yttrium oxide (Y2O3) ceramic coatings. Changing the spraying process parameters can significantly improve the microstructure and enhance the coating properties. In this study, the combination of plasma-spraying process parameters (current, spraying distance, and argon (Ar) flow) was varied by Response Surface Methodology (RSM) with the help of Minitab 19 software. Applied to the design of experiments, improvement of errors, and prediction of microstructure property results, the optimization and validation of experimental parameters for attaining the desired microstructure of Y2O3 coatings, especially porosity, was achieved. Process parameters were optimized by RSM: current 613.64 A, Ar flow rate 46.92 L/min, spray distance 15.38 cm, and optimum porosity 1.8% after optimization. Electrochemical corrosion experiments and breakdown voltage experiments revealed that the corrosion resistance and dielectric properties increased significantly as the porosity of the coatings decreased. Therefore, by optimizing the plasma-spraying process parameters, the porosity of the coatings can be significantly reduced and the corrosion resistance and dielectric properties of Y2O3 coatings can be effectively improved.

Process parameter optimization for laser directed energy deposition (LDED) of Ti6Al4V using single-track experiments with small laser spot size

Single-track experiments are routinely used in the optimization of process parameters in additive manufacturing processes. Most of the process parameter optimization studies use a laser spot size of 1 mm or more. Since laser spot size affects the input energy density and in turn the efficiency of the deposition process, it is important to develop process maps every time a laser of different spot sizes is used. In this work, we determine the process maps for a laser of 0.6 mm spot size. By combining the process maps and the metallographic inspection, we estimate the optimum process parameters (laser power, scan speed, powder feed rate) for building Ti6Al4V components using powder-based laser-directed energy deposition(LDED). Single-tracks corresponding to 64 different parameter combinations are deposited. After eliminating the process parameter combinations resulting in defective tracks, the optimum process parameters of 300 W laser power and 720 mm min−1 scan speed is established by considering the relationship between the process parameters and the geometrical features of the deposit. The experimental results are then used to calibrate the modeling parameters of a three-dimensional finite element model for simulating the deposition process.

Finite Element Simulation and Microstructural Evolution Investigation in Hot Stamping Process of Ti6Al4V Alloy Sheets.

Titanium alloy hot stamping technology has a wide range of application prospects in the field of titanium alloy part processing due to its high production efficiency and low manufacturing cost. However, the challenges of forming titanium alloy parts with large depths and deformations have restricted its development. In this study, the hot stamping process of a Ti6Al4V alloy box-shaped part was investigated using ABAQUS 2020 software. The thermodynamic properties of a Ti6Al4V alloy sheet were explored at different temperatures (400 °C, 500 °C, 600 °C, 700 °C, 800 °C) and different strain rates (0.1 s-1, 0.05 s-1, 0.01 s-1). In addition, the influence law of hot stamping process parameters on the minimum thickness of the formed part was revealed through the analysis of response surface methodology (RSM), ultimately obtaining the optimal combination of process parameters for Ti6Al4V alloy hot stamping. The experimental results of the hot stamping process exhibited a favorable correlation with the simulated outcomes, confirming the accuracy of the numerical simulation. The study on the microstructure evolution of the formed parts showed that grain refinement strengthening occurred in the part with large deformation, and the formed box-shaped parts exhibited a uniform and fine microstructure overall, demonstrating high forming quality. The achievements of the work provide important guidance for the fabrication of titanium alloy parts with large depths and deformations used in heavy industrial production.

Intelligent optimization design of squeeze casting process parameters based on neural network and improved sparrow search algorithm

Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

Optimizing the Preparation Process of Refined Asphalt Based on the Response Surface and Preparation of Needle Coke.

Refined asphalt was prepared by solvent extraction sedimentation based on the response surface design, using washing oil and kerosene as solvents and the coal tar pitch as raw materials. The mathematical models of the refined asphalt yield, quinoline insoluble (QI) content, ash content, solvent-to-oil ratio, aromatic-to-aliphatic hydrocarbon ratio, extraction temperature, and sedimentation time were proposed, analyzing the influence of each factor and their interactions on the response values. Therefore, the optimal combination of preparation process parameters and better operation window was obtained by optimizing the experiment. Meanwhile, refined asphalt with high QI content and low QI content was selected as raw material, and the needle coke was prepared through the process of carbonization and calcination. The influence of QI content on the composition and the structure of green coke and needle coke was investigated by X-ray diffraction (XRD), Raman spectra, and polarizing microscopy characterizations. The results showed that the solvent-to-oil ratio is 1.2, aromatic-to-aliphatic hydrocarbon ratio is 1.1, sedimentation time is 2 h, and extraction temperature is 110 °C, resulting in the yield of refined asphalt being 76%, QI content being less than 0.1%, and ash content being less than 0.05%, which meets the requirement of the high-quality needle coke. Otherwise, refined asphalt with lower QI content easily generates a mesophase with more fibers and a large structure in the thermal conversion process, and the corresponding green coke and needle coke have a relatively regular carbon microcrystalline structure.

Investigation of micro-electro-discharge machining process parameters during machining of M2 hardened die-steel

Micro-electro-discharge machining (micro-EDM) is a well-known micromachining method to create microstructures on hard-to-cut materials. To further exploit the capability of micro-EDM, the present research work investigates the effect of various important micro-EDM process parameters on the material removal rate (MRR), tool wear rate (TWR), and overcut (OC) during micro-cavity machining of M2-hardened die-steel. A Taguchi experimental design was used to study the effects of three process parameters namely pulse-on-time (Ton), peak current (Ip), and gap voltage (Vg). The experimental results showed that the most influencing process parameter in the present experimental investigation is peak current, Ip followed by pulse-on-time, Ton and gap voltage, Vg. Furthermore, a recent and effective method i.e. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) has been successfully employed to determine optimal process parametric combination for micro-EDM process to achieve maximum MRR and minimum TWR and OC during machining of micro-cavity on M2-hardened die-steel. From the present research investigation, the optimal micro-EDM process parametric combinations obtained for higher MRR, lower TWR and OC are found as 1 A of peak current, 30 μs pulse-on time and gap voltage of 60 V. Furthermore, through various scanning electron microscope micrographs of micro-cavities and micro-machined surfaces, analysis has been done to find out the micro-cavity with best geometrical accuracy and surface feature to validate the best and most ideal combinations of micro-EDM input parameters for micromachining operations. The results of this study show that micro-EDM is a promising process for machining M2-hardened die-steel. The optimization of process parameters using the MARCOS methodology can further improve the machining efficiency and accuracy of micro-EDM.

Experimental optimization of machining GH4145 by atomizing discharge ablation milling

Atomizing discharge ablation milling (ADAM) technology is an efficient discharge machining technology derived from the traditional electrical discharge machining (EDM) method, which can be used to efficiently machine hard-to-machine materials such as nickel-based superalloy. In this present, the performance of machining nickel-based superalloy GH4145 by ADAM and Air near-dry EDM were compared, and the experimental results showed that the material removal rate (MRR) obtained by ADAM was nearly double that of the latter. A single-factor experiment were conducted to investigate the effect of electrode rotation speed on ADAM’s processing performance. Subsequently, an orthogonal experimental method was used to design the experiment. The signal-to-noise ratio analysis method was used to systematically study the performance characteristics of ADAM, including the influence of atomization amount, oxygen pressure, discharge current, duty ratio on MRR and tool electrode relative wear rate (TWR). The results showed that discharge current was the most influential processing parameter on MRR and TWR. Finally, the optimal combination of processing process parameters that met the requirements of various processing effect evaluation indicators were obtained and the correctness of the single objective optimization results was verified through experiments.

Optimizing mechanical properties of virgin and recycled PLA components using Anova and neural networks

The increasing demand for polymers in additive manufacturing (AM) has led to a significant increase in plastic waste, with over 300 million metric tons used in recent years. This research article explores the use of Poly Lactic Acid (PLA) as a biodegradable thermoplastic recycled material for 3D printed components, comparing its properties with virgin PLA and discussing solutions for variation and mechanical features improvement. Fused Deposition Modeling (FDM) is a widely used additive manufacturing process that allows for the creation of three-dimensional objects by depositing molten material layer by layer. This study investigates the impact of infill density, layer thickness, and raster angle for recycled 3D printing material, focusing on their dimensions and their influence on processing efficiency. This research paper aims to investigate the mechanical effects of recycled 3d printed components which are printed by using FDM with the combination of different process parameters compared with virgin PLA. From results optimal process parameters are found to enhancing quality and performance of recycled 3D printed components. Later results are compared by Analysis of Variance (ANOVA) as a statistical tool and also with ANN technique, which minimizes error deviation.

Study on the Forming Process and Properties of AlSi60 Alloy by Selective Laser Melting

Hypereutectic Al-Si alloys, which have a silicon content ranging from 12% to 70%, are a new generation of casing materials for chip packaging. They have broad applications in aerospace, weaponry, and civilian communications. Selective Laser Melting (SLM) offers significant advantages in achieving near-net shaping of complex casings. This paper presents a study on the formation defects, microstructure, and room temperature tensile properties of AlSi60 alloy prepared by SLM. The results indicate that the primary forming defects in the SLM AlSi60 alloy are balling, lack of fusion, and porosity. These defects are mainly influenced by the volumetric energy density. Samples of good quality can be produced within the range of 150 J/mm3 to 250 J/mm3. However, the same volumetric energy density can result in differences in sample quality due to various combinations of process parameters. Therefore, it has been determined that a well-formed AlSi60 alloy can be obtained within a laser power range of 300 W–350 W, scanning speed of 400 mm/s–800 mm/s, and hatch spacing of 0.09 mm–0.13 mm, with a density close to 98%. The microstructure of the SLM AlSi60 alloy consists of primary Si phases with irregular shapes and sharp edges measuring 5–10 μm, eutectic Si particles of 0.5 μm, and α-Al phases, with eutectic Si dispersed within the α-Al. The SLM AlSi60 alloy exhibits fine and evenly distributed primary Si phases with an average hardness of 203 HV. No significant anisotropy in hardness values was observed in the X and Y directions. The tensile strength of the alloy reached an average of 219 MPa, with an average elongation of 2.99%. During the tensile process, cracks initiated by the primary Si phases rapidly expanded, exhibiting minor ductile fracture characteristics in the Al phases. Due to the high volume fraction of Si phases, the tensile test was dominated by brittle fracture. The tensile curve only exhibited the elastic stage.

EXPERIMENTAL INVESTIGATIONS ON THE ELECTROCHEMICAL MACHINING OF D3 DIE STEEL MATERIAL AND MULTI-OBJECTIVE PARAMETER OPTIMIZATION

The removal of metal in the form of sludge can be termed an ECM process, having primary salient features consisting of the tool, workpiece, power supply and electrolyte solution. ECM is a potential nontraditional machining process that includes too many factors contributing to process performance. Therefore, obtaining optimal factor combinations for higher efficiency is complex. In this research work, machining parameters are voltage, powder concentration and electrolyte concentration, based on which material removal rate (MRR) and surface roughness (SR) are derived. Regression analysis, signal-to-noise ratios and analysis of variance (ANOVA) are performed to determine the optimal levels and to examine the effects of machining parameters on MRR and SR. The results derived from the experiments noticed that the MRR decreases when the voltage level is increased and surface roughness decreases for the applied voltage. In case of an increase in powder concentration, MRR decreases from range 4 to 6 and further reduces to 6–8. In case of SR, it additionally decreases on the maximum level of powder concentration. In electrolyte concentration, the MRR decreases on the increment of electrolyte and increases after a short period of time; simultaneously SR is increased from range value 10–20 and decreased with a maximum level 20–30 of NaCl solution. A comparison of MOORA-PCA and TOPSIS-PCA demonstrates the superiority of TOPSIS over MOORA technique. Statistical results (95% confidence level) indicate that the voltage, powder concentration, and electrolyte concentration affect the surface roughness by 69.50%, 8.67% and 14.59% in the electrochemical machining of d3 die steel. Therefore, the optimum combination of process parameters corresponding to voltage: 45[Formula: see text]V, powder concentration: 4[Formula: see text]g/L, and electrolyte concentration: 10 NaCl/L, respectively, were found to yield the desired result. The prediction accuracy of the TOPSIS-PCA hybrid approach model is found better than MOORA-PCA technique.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Optimization of process parameters for laser cladding Stellite6 cobalt-based alloy

As a new remanufacturing technology, laser cladding can repair damaged parts in large valves in nuclear power plants. The objective is to enhance the surface morphology quality of parts post-remodeling, the paper optimizes the key process parameters in the laser cladding process. Numerical simulation was used to analyze the influence of each process parameter ( laser power, scanning speed, powder feeding speed) on the morphology of the cladding layer. The laser power and powder feeding rate were positively correlated with the width of the cladding layer, and the scanning speed was negatively correlated with the width of the cladding layer. The orthogonal experiments are designed based on the Taguchi method, and the Stellite6 single cobalt-based alloy cladding layer was prepared on the WCB low-carbon steel substrate. Analysis of variance and RSM was used to analyze the experimental results. The influence order of process parameters on the morphology of the cladding layer was determined. The regression prediction model was established to analyze the relationship between the process parameters and the morphology quality (width and height) of the cladding layer. The errors of the model are 5% and 3.5%, respectively. The optimal combination of process parameters is determined as follows: P = 1700 W, Vs = 8 mm/s, Vf = 26 g/min. The cladding layer prepared by the optimal process parameters has fine structure and good bonding. The accuracy of the predictions was validated using this model, which offered valuable theoretical guidance for predicting and controlling the geometric characteristics of the laser cladding of Stellite6 alloy.

Advancing scaffold porosity through a machine learning framework in extrusion based 3D bioprinting

Three Dimensional (3D) bioprinting holds great promise for tissue and organ regeneration due to its inherent capability to deposit biocompatible materials containing live cells in precise locations. Extrusion-based 3D bioprinting (EBP) method stands out for its ability to achieve a higher cell release rate, ensuring both external and internal scaffold structures. The systematic adjustment of key process parameters of EBP, including nozzle diameter, printing speed, print distance, extrusion pressure, material fraction, and viscosity allows for precise control over filament dimensions, ultimately shaping the desired scaffold porosity as per user specifications. However, managing these factors with all possible interactions simultaneously to achieve the desired filament width can be intricate and resource intensive. This study presents a novel framework designed to construct a predictive model for the filament width of 3D bioprinted scaffolds for various process parameters. A total of 157 experiments have been conducted under various combinations of process parameters and biomaterial’s weight fraction for this study purpose. A regression-based machine learning approach is employed to develop the predictive model utilizing Adj. R2, Mallow’s Cp, and Bayesian Information Criterion (BIC). Following model development, rigorous experimental validations are conducted to assess the accuracy and reliability of the model. Based on the cross-validation of randomly split test data, Adj. R2 model emerges as the highest performing machine learning model (Mean Squared Error, MSE = 0.0816) compared to Mallow’s Cp and BIC (MSE = 0.0841 and 0.0877, respectively) models. The comparative analysis results between the experimental and model’s data demonstrate that our predictive model achieves an accuracy of approximately 85% in filament width prediction. This framework presents a significant advancement in the precise control and optimization of 3D bioprinted scaffold fabrication, offering valuable insights for the advancement of tissue engineering and regenerative medicine applications.

Microstructure and Mechanical Properties of Cermet Composite Coating on TC4 Surface

In order to improve the surface quality and performance of TC4, the TC4-HfC-TaC metal-ceramic composite cladding coating was prepared on the surface of TC4 by using different process parameters by laser cladding technology. Through digital testing equipment, the macro morphology, molten pool size, dilution rate, Rockwell hardness (HRC), geometry, microstructure and other comprehensive properties of the coating are studied and analyzed. The results show that the performance characterization of TC4-HfC-TaC cladding layer is closely related to the melting state of refractory carbide ceramic phase HfC and TaC powder, when the laser beam energy density is not enough to completely melt the ceramic phase powder, with the increase of energy density, the coating surface tends to be flat, the molten pool size and dilution rate increase, the hardness gradually increases, the metallographic structure is refined uniformly, and the surface quality is improved. Excessive energy density of the laser beam can easily lead to excessive dilution rate of the cladding layer, reduced coating hardness, coarse metallographic structure, and reduced surface quality. Finally, the best combination of process parameters was screened out: laser power 3000 W, scanning speed 300 mm/min, spot size 2.5 × 11.5 mm, rectangular spot lap rate 40%, preset powder thickness 1.5 mm, at which time the coating hardness increased to 63.22 HRC. It is about 2.1 times the hardness of TC4 substrate, with the best performance, and the metallographic structure is densely arranged fine cell-like crystals, with outstanding microscopic characteristics, which realizes the purpose of preparing high-quality TC4-HfC-TaC cermet composite cladding coating on the surface of TC4 titanium alloy.