Combination Of Process Parameters Research Articles

Green synthesis of Carbonized Chitosan-Fe3O4-SiO2 nano-composite for adsorption of heavy metals from aqueous solutions

Water pollution with heavy metals owing to industrial and agricultural activities have become a critical dilemma to humans, plants as well as the marine environment. Therefore, it is of great importance that the carcinogenic heavy metals present in wastewater to be eliminated through designing treatment technologies that can remove multiple pollutants. A novel green magnetic nano-composite called (Carbonized Chitosan-Fe3O4-SiO2) was synthesized using Co-precipitation method to adsorb a mixture of heavy metal ions included; cobalt (Co2+), nickel (Ni2+) and copper (Cu2+) ions from aqueous solutions. The novelty of this study was the synthesis of a new nano-composite which was green with magnetic properties to be more sustainable and environmentally friendly. Its magnetic properties made it separated easily from solutions after accomplishment of the adsorption process using a magnet. Extended Freundlich isotherm was the best fitted model with maximum adsorption capacity of the metal ions mixture 2908.92 mg/g. Different experimental parameters have been studied included the initial concentration for a mixture of nickel, cobalt and copper metal ions (0.05–0.1 molar), dosage of adsorbent (0.5–3.5 g/L) and contact time (6–90 min) to investigate their changing effect on the removal percents of the heavy metal ions mixture from aqueous solutions. The experimental adsorption percent of cobalt ion ranged from 1.58 to 64.28%, nickel ion adsorption percent ranged from 10.68 to 94.12% and copper ion adsorption percent ranged from 4.41 to 76.23% at pH = 9 were based on the combination of the adsorption process parameters.

Effect of oxygen generating nanozymes on indocyanine green and IR 820 mediated phototherapy against oral cancer

The hypoxic environment within a solid tumor is a limitation to the effectiveness of photodynamic therapy. Here, we demonstrate the use of oxygen generating nanozymes (CeO2, Fe3O4, and MnO2) to improve the photodynamic effect. The optimized combination of process parameters for irradiation was obtained using the Box Behnken experimental design. Indocyanine green, IR 820, and their different combinations with oxygen generators were studied for their effect on oral carcinoma. Dynamic light scattering technique showed the average particle size of CeO2, MnO2, and Fe3O4 to be 211 ± 16, and 157 ± 28, 143 ± 19 nm with PDI of 0.23, 0.28 and 0.20 and a zeta potential of −2.6 ± 0.45, −2.4 ± 0.60 and −6.1 ± 0.23 mV, respectively. The formation of metal oxides was confirmed using UV–visible, FTIR, and X-ray photon spectroscopies. The amount of dissolved oxygen produced by CeO2, MnO2, and Fe3O4 in the presence of H2O2 within 2 min was 1.7 ± 0.15, 1.7 ± 0.16, and 1.4 ± 0.12 mg/l, respectively. Growth inhibition studies in the FaDu oral carcinoma spheroid model showed a significant (P < 0.05) increase in growth reduction from 81 ± 2.9 and 88 ± 2.1% to 97 ± 1.2 and 99 ± 1.0% for ICG and IR 820, respectively, after irradiation (808 nm laser, 1 W/cm2, 5 min) in the presence of CeO2 (25 μg/ml). In conclusion, oxygen-generating nanozymes can improve the photodynamic effect of ICG and IR 820.

An Experimental Study in Laser-Assisted Machining of AerMet100 Steel.

To solve the problems of poor surface quality and low tool life in conventional machining (CM) of AerMet100 steel, an experimental study was conducted in laser-assisted machining (LAM) of AerMet100 steel. The effects of laser power, cutting speed, feed rate, and depth of cut on the surface roughness of AerMet100 steel were studied based on a single-factor experiment. The degree of influence of each factor on the surface roughness was evaluated by analyses of variance and range in the orthogonal experiment, and the combination of process parameters for the optimal surface roughness was obtained. The order of influence was as follows: laser power > cutting speed > depth of cut > feed rate; the optimal combination of process parameters was laser power 200 W, cutting speed 56.5 m/min, feed rate 0.018 mm/rev, and depth of cut 0.3 mm. Compared to CM, the surface morphology of the workpiece under the optimization of LAM was relatively smooth and flat, the surface roughness Ra was 0.402 μm, which was reduced by 62.11%, the flank wear was reduced from 208.69 μm to 52.17 μm, there were no tipping or notches, and the tool life was significantly improved. The study shows that the LAM of AerMet100 steel has obvious advantages in improving surface quality and reducing tool wear.

OPTIMIZATION OF MACHINING AA4015/B4C METAL MATRIX COMPOSITES BY TAGUCHI METHOD

This research investigates the impact of dry machining AA4015/B4C MMCs (metal matrix composites) utilizing an uncoated B4C insert on Flank wear (VBc) and surface roughness (Ra). This work attempts to close the knowledge gap on the effects of cutting parameters (feed, depth of cut, and speed) on tool wear and surface quality in machining technology. The primary rationale lies in optimizing machining processes for MMCs, known for their challenging machinability due to their tough metallic matrix and hard ceramic reinforcement. The study’s significance is underscored by the exploration of optimal processing parameters to minimize Flank wear and improve surface roughness, crucial factors influencing component quality and lifespan. Specifically, the research identifies v1-f1-d3 (VBc) and v3-f1-d3 (Ra) as the best process parameter combinations, significantly reducing both VBc and Ra. The obtained mathematical models for VBc and Ra provide statistically significant insights into the relationships between cutting variables and performance characteristics. The employment of Taguchi’s L9 orthogonal array proves invaluable in achieving these optimized process parameters efficiently. The Taguchi method’s advantage lies in its ability to systematically explore numerous variables and their interactions with minimal experiments. By reducing the number of trials required, this methodology streamlines the optimization process, saving time, resources, and costs while delivering enhanced machining performance for MMCs. This research, through its systematic approach and emphasis on optimized parameters, contributes to the advancement of machining techniques for MMCs, holding implications for various industrial applications demanding high-performance materials.

Effect of Sampling Rate, Filtering, and Torque Onset Detection on Quadriceps Rate of Torque Development and Torque Steadiness.

Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a significant gap in standardized methodology for measuring these metrics, which limits their utility in comparing outcomes across different studies and populations. To address these gaps, we evaluated the influence of sampling rates, signal filtering, and torque onset detection on RTD and torque steadiness. Twenty-seven participants with a history of a primary anterior cruciate ligament reconstruction (N = 27 (11 male/16 female), age = 23 ± 8 years, body mass index = 26 ± 4 kg/m2) and thirty-two control participants (N = 32 (13 male/19 female), age = 23 ± 7 years, body mass index = 23 ± 3 kg/m2) underwent isometric quadriceps strength testing, with data collected at 2222 Hz on an isokinetic dynamometer. The torque-time signal was downsampled to approximately 100 and 1000 Hz and processed using a low-pass, zero-lag Butterworth filter with a range of cutoff frequencies spanning 10-200 Hz. The thresholds used to detect torque onset were defined as 0.1 Nm, 1 Nm, and 5 Nm. RTD between 0 and 100 ms, 0 and 200 ms, and 40-160 ms was computed, as well as absolute and relative torque steadiness. Relative differences were computed by comparing all outcomes to the "gold standard" values computed, with a sampling rate of 2222 Hz, a cutoff frequency in the low-pass filter of 150 Hz, and torque onset of 1 Nm, and compared utilizing linear mixed models. While all combinations of signal collection and processing parameters reached statistical significance (p < 0.05), these differences were consistent between injured and control limbs. Additionally, clinically relevant differences (+/-10%) were primarily observed through torque onset detection methods and primarily affected RTD between 0 and 100 ms. Although measurements of RTD and torque steadiness were generally robust against diverse signal collection and processing parameters, the selection of torque onset should be carefully considered, especially in early RTD assessments that have shorter time epochs.

PULSE DEPOSITION METHOD FOR WIRE AND ARC ADDITIVE MANUFACTURING

Purpose. To reduce the waviness of the side surfaces of wire and arc additive manufactured parts. Research methods. Two groups of deposited specimens were used. The waviness of the side surfaces was measured from digital images of the cross sections of the specimens. The images were obtained by optical digital scanning. To establish a functional relationship between the geometric parameters of the beads and the main deposition parameters, regression and analysis of variance were performed on the measured data. Results. It was found that different combinations of the main process parameters resulted in a surface waviness of 1.21 ± 0.23 mm. Based on the results obtained, a pulse deposition method was developed. The implementation of regular pauses reduced the heat input and the time spent by the material in the molten state, which limited its distribution. The proposed method resulted in a significantly lower waviness of 0.47±0.08 mm and a significant improvement in the stability of the resulting surface irregularity. Scientific novelty. It has been shown for the first time that the waviness of the side surfaces in wire and arc additive manufacturing does not depend on the main deposition parameters, but is related to the nature of the arc-based deposition process. The developed method of pulsed deposition limits the time the metal remains in the molten state, which reduces the waviness of the surfaces by up to 60% and improves the stability of the geometry by three times, reducing the standard deviation to 0.08 mm. Practical value. Pulsed deposition improves the predictability of the printed geometry by improving the accuracy and quality of the side surfaces. This reduces the required machining allowance, speeds up production, and reduces material waste. In some cases, the predictability of the geometry makes it possible to eliminate post-processing.

Continuous flow production of bioactive ceria quantum dots: New paradigms to the effect of process parameters on surface oxygen vacancy tuning

Precisely monitored nucleation-growth kinetics for governing the size, surface chemistry, and sought-after attributes of quantum dots (QDs) for large-scale manufacturing remains a formidable challenge. This study evinces the importance of ultrafast mixing and high heating rates for rapid nucleation, particularly in synthesizing monodispersed QDs with enriched surface defects. Recently, cerium oxide (CeO2) nanostructures have gained prominence in antioxidant therapy owing to the co-existence of Ce3+ and Ce4+. However, current batch processes lack scalability, reproducibility, and control over the reaction kinetics, key for fine-tuning the surface defect-driven properties of CeO2 nanostructures. Addressing this knowledge gap, we demonstrate a unique, sustainable, continuous flow platform that allows simultaneous engineering of surface oxygen vacancies (VO●) and regulates the size of L arginine functionalized CeO2 QDs (VO●-rich l-arg-CeO2 QDs). Introduction of the helical coil reactor regulated by dean vortices yields monodispersed QDs with enhanced Ce3+/Ce4+ratio and VO● at the surface. Through various experimental methodologies, we showed how adjusting temperature, flowrate, and pH enables achieving the desirable size (3 nm), thereby bestowing an optimal surface Ce3+ and VO● fractions, pivotal for size-dictated photo-response, physiochemical properties, and biofunctionality of the QDs. The abundant surface VO● (72 %) entails narrowing of the band gap (∼ 2.5 eV), resulting in unprecedented photothermal response (ΔT = 19.7 ± 0.6 °C) and photoluminescence, features not typically found in defect-free conventional CeO2 nanostructures. With a strategic combination of process parameters, the defect-rich material system displayed excellent biocompatibility (95.6 %), and antioxidant efficacy on human keratinocyte (HaCaT) cells, and long-term stability (ζ = -29±1.5 mV) in suspensions, even after 120 days. This economical, high throughput continuous flow platform for fabricating biofunctionalized VO●-rich l-arg-CeO2 QDs outperforms conventional batch processes, opening numerous possibilities for lab-to-clinic translation in the fight against oxidative stress-related disorders.

Parametric investigation of wire-EDM parameters for micro channel as a foundation of high-quality micro needle array fabrication

Micro channels are widely used in microfluidics, and medical science for the extraction of DNA, electrophoresis, and blood protein analysis. In the current work, the wire electrical discharge machining (WEDM) process was used to fabricate micro channels on the stainless steel 316 (SS316) substrate. To check the feasibility of fabricating high-quality micro channels and thereafter micro-scale structures with the WEDM process the effect of different process parameters such as pulse-off time, pulse-on time, servo gap reference voltage and wire feed rate were investigated. The quality attributes taken were namely surface roughness of the channel base, overcut, micro channel depth, and material removal rate (MRR). The pulse-on time significantly affects surface roughness and MRR. The servo gap reference voltage is the key process parameter affecting the overcut and micro channel depth. It was observed from the parametric study that 15 µs pulse-on time, 35 µs pulse-off time, 17 v servo gap reference voltage, and 8 m/min wire feed rate are the optimum levels for getting high MRR, low surface roughness, less overcut and high micro channel depth. Additionally, the topographical, morphological, surface, and sub-surface characterization were also carried out to assess the surface integrity and extent of thermal damage caused by the WEDM process. With the optimum combination of process parameters a 6 × 6 micro needle array was successfully fabricated on the same substrate using micro channels as a basis of the fabrication process for the first time using the WEDM process.

Combined influence of traverse and rotation speeds in friction stir processing OF AlSi10Mg alloy produced by laser-powder bed fusion

In this study, friction stir processing (FSP) was used to enhance the corrosion properties through local modification of the microstructure of AlSi10Mg alloy fabricated by laser-powder bed fusion (L-PBF) technology. The impact of traverse and rotational speeds on the mechanical and electrochemical properties of L-PBF AlSi10Mg was analyzed with different combinations of processing parameters, with rotational speeds ranging from 800 to 1600 rpm and traverse speeds from 100 to 300 mm/min. The experimental results showed that the Si content within the stir zone is dependent on the combined influence of the rotational and traverse speeds, with a lower traverse speed promoting an enhanced grain refinement and a more homogenous Si distribution, indicated by a low LAGBs with a high HAGBs, resulting in the decrease of the hardness value and improvement of the corrosion performance of the processed material. The lowest hardness was found on the samples processed with a traverse speed of 100 mm/min and the best corrosion performance was observed from the samples processed with a rotational speed of 1600 rpm and a traverse speed of 100 mm/min. The results also indicated that the rotational speed showed a major role on the process, with the influence of the traverse speed diminishing when the material is processed at higher rotational speeds.

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.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

Precision control and parameter optimization in screw extrusion 3D printing of polypropylene materials

Fused Deposition Modeling (FDM), a widely-utilized additive manufacturing (AM) technology, has found significant favor among automotive manufacturers. Polypropylene (PP) compound is extensively employed in the production of automotive parts due to its superior mechanical properties and formability. However, aiming at the problem of poor dimensional accuracy of pure PP parts, the quality of products can be enhanced by optimizing the combination of processing parameters. In this paper, the dimensional accuracy of 3D-printed components made from pure PP material is investigated. Key influencing factors such as infill percentage, infill pattern, layer thickness, and extrusion temperature are considered. To gain a deeper understanding, fluid simulation is conducted, and mathematical models are established to correlate processing parameters with dimensional accuracy. Furthermore, the Taguchi's experiments are designed and the experimental data are subjected to rigorous Signal-to-Noise ratio and ANOVA analyses. Within the experimental range, the lower extrusion temperature, infill percentage and layer thickness yield the best dimensional accuracy. Considering the influence factors of X, Y and Z directions, the optimal processing parameters for PP prints using screw extrusion 3D printers are determined as follows: an extrusion temperature of 210 °C, an infill percentage of 40 %, a layer thickness of 0.3 mm, and a concentric circle infill pattern. This study provides reference value for the subsequent improvement of the dimensional accuracy of the printed parts.

Orthogonal experimental study of ultrasound-assisted fused deposition molding

To improve the quality of fused deposition molding (FDM), we introduced ultrasonic vibration into the molding process to enhance the strength of the tensile samples. We used the response surface method to optimize the FDM molding process. We carried out orthogonal experiments to investigate the effects of ultrasonic power and process parameters (extrusion temperature, hotbed temperature, and delamination thickness) on the mechanical properties of fused deposition acrylonitrile butadiene styrene products. We obtained the quadratic regression equations and response surfaces of the data through analysis of variance and significance analysis by software. Last, we received the optimum combination of process parameters for tensile strength as extrusion temperature 233.85 °C, hotbed temperature 93.38 °C, delamination thickness 0.1 mm, and ultrasonic power 43.09 W.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Research on cutting force characteristics of laser ultrasonically assisted turning of cemented carbide

Cemented carbide possesses excellent characteristics such as high strength and hardness, but its poor machinability has restricted its widespread application. Laser ultrasonically assisted turning (LUAT) can enhance the machinability of workpieces and is widely used in precision machining of difficult-to-machine materials. In this paper, LUAT is applied to the cutting of cemented carbide, and the characteristics of cutting force in the machining process are studied. A theoretical model of cutting force is established by analyzing the interaction between the cutting tool, chip and workpiece during the turning process. The maximum error was found to be 18.6% by comparing with the experimental data, which better predicted the changing trend of cutting force and verified the accuracy of the model. Furthermore, experiments on LUAT of cemented carbide are carried out using the Taguchi method. The influence of cutting tools and processing parameters on cutting force is analyzed, revealing that the amplitude has the most significant impact on cutting force. The optimal combination of processing parameters is also obtained. Finally, a comparison is made between different turning methods. Compared to conventional turning, laser assisted turning, and ultrasonically assisted turning, the total cutting force in LUAT is reduced by 53.44%, 45.62%, and 21.64%, respectively, resulting in improved surface quality of the workpiece.

Optimization of porosity behavior of hybrid reinforced titanium metal matrix composite through RSM, ANN, and GA for multi-objective parameters

Titanium matrix composites (TMCs) have high specific strength and stiffness, and high-temperature TMCs can reduce weight by up to 50% when compared with monolithic super alloys while preserving equal stiffness and strength in jet engine systems for propulsion. The purpose of this work examines the use of mathematical models and learning approaches to optimize response such as porosity and control variables in synthesized hybrid titanium metal matrix composites (HTMMCs) reinforced by B4C-SiC-MoS2-ZrO2. To further understand the impacts of process factors on porosity reduction, the study employs methodologies such as the response surface methodology (RSM), integrated artificial neural networks (ANN), and genetic algorithm (GA). The findings indicate that these strategies have the potential to contribute to the industry. The optimal combination of 7.5wt.% SiC, 7.5wt.% B4C, 7.5wt.% ZrO2, 4wt.% MoS2, and 73.5wt.% Ti compositions was determined utilizing process factors such as milling period (6h), compaction pressure (50MPa), compact duration (50min), sintering temperature (1200°C), and sintering time (2h) as compared to pure Ti grade 5. The mechanical properties of the optimum combination of reinforcement weight percentage and process parameters resulted in a minimum porosity of 0.118%, density of 4.36gcm3, and micro-hardness of 63.4HRC boosted by 1.76%, and compressive strength of 2500MPa increased by 2.6%. In addition, these HTMMCs had a minimal wear rate of 0.176mm3/Nm and a corrosion resistance rate of 2.15×10-4mmpy. The investigation result analysis discovered that the RSM and combined ANN-GA models considerably enhanced the forecasting of multidimensional interaction difficulties in composite material production that were highly statistically connected, with R2 values of 0.9552 and 0.97984. The ANN-GA model provided a 95% confidence range for porosity predictions, which increased the production use of titanium-based particle composites. Furthermore, HMMCs can be utilized in the automotive and aviation industries with enhanced corrosion and wear resistance.

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles.

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations.

OPTIMASI MULTI PARAMETER MESIN 3D PRINTER FDM UNTUK MATERIAL THERMOPLASTIC POLYURETHANE GUNA MENGHASILKAN ERROR DIMENSI TERKECIL MENGGUNAKAN METODE 2K FACTORIAL DESIGN

Fused Deposition Modeling or FDM is a 3D printing technology that is commonly used in manufacturing processes based on Additive Manufacturing Technology. One of the materials that can be used on FDM machines is thermoplastic polyurethane (TPU). One measure of the quality of printed results is the machine's accuracy in producing workpieces that have the smallest possible dimensional error. So this research aims to obtain the printing parameters that have the most influence on print results and find out the optimal combination of printing process parameters on the machine to produce the smallest dimensional error for TPU material. The design of experiment method used is 2k factorial design using 4 process parameters, 2 levels and 3 responses. The parameters used are nozzle temperature, print speed, fill density, and enclosure box. The optimization results obtained an optimal combination of printing process parameters to produce the smallest dimensional error, namely nozzle temperature of 210°C, print speed of 30 mm/s, fill density of 100%, and not using an enclosure box.

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

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.