Spark plasma sintering of ZnO varistors: Parameter optimization via Taguchi method and correlation with microstructure and electrical characteristics

Optimizing pulsed field ablation for cardiac arrhythmias integrating Taguchi method, machine learning and genetic algorithms

Cryogenic treatment (CT) has emerged as a pivotal supplementary process to conventional heat treatment, promising significant enhancements in the wear resistance and longevity of engineering components. This paper presents a systematic and critical review of the extant literature on the application of deep cryogenic treatment (DCT) to alloy steels, with a focus on prevalent grades such as AISI D2, M2, 52100, and EN series steels. The review consolidates the established metallurgical mechanisms—primarily the transformation of retained austenite and the precipitation of nano-scale eta-carbides—that underpin property improvements. A thematic analysis of reported data on hardness, wear resistance, toughness, and fatigue strength is conducted, revealing a strong material-dependent response where high-carbon, high-alloy steels derive maximum benefit. Crucially, this review identifies a significant methodological gap in the prevailing research paradigm: a near-universal reliance on one-variable-at-a-time (OVAT) experimental approaches and a lack of standardized tribological testing protocols. This limits the ability to optimize treatment parameters and generalize findings. We argue for the integration of robust statistical frameworks, specifically the Taguchi method and Analysis of Variance (ANOVA), with standardized wear tests (e.g., ASTM G99) to transform CT from an empirical art into a optimized science. The paper concludes by delineating clear research gaps, notably the lack of systematic studies on common Indian standard (EN) steels, and proposes a structured future research direction that bridges materials science with quality engineering principles for predictable and application-specific component enhancement.

The machining process in the manufacturing world greatly influences its quality and productivity. One of the tools in the machining process is a CNC lathe that has parameters for its use process. The process parameters in the CNC machining process are spindle speed, feed rate, and cutting depth. Some aspects that are greatly influenced by process parameters are surface roughness and MRR (Material Removal Rate), which function as efficiency in improving product quality. This study aims to analyse the effect of machining process parameters on surface roughness and MRR on test specimens made of ST 37 material. The method in this study uses the Taguchi method with the aim of increasing productivity and product and process quality. The results of this study show surface roughness based on the use of two types of tools on ST 37 material, with the DNMG 105608-MA tool producing a roughness value of 1.128 µm and the CNMG tool producing a value of 0.865 µm. Meanwhile, the MRR value produced by the DNMG 105608-MA tool is 1,072.8 mm³/s, which is slightly superior to that of the CNMG 120408-MK tool, with an MRR value of 1,032.5 mm³/s.

This study analyzes the effect of optimizing CAD/CAM-based CNC milling parameters to reduce the machining time of a brake pad mold holder. The main issue addressed is the excessive machining duration caused by non-optimal cutting parameters, namely spindle speed, feed rate, and depth of cut. A quantitative experimental approach was employed using the Taguchi method and ANOVA, integrated with CAD/CAM simulation in Autodesk Fusion 360. A 3D model of the brake pad mold holder was developed and simulated to obtain machining time data from nine parameter combinations based on an L9 orthogonal array. The results show that depth of cut has the most significant influence on machining time (87.48%), followed by feed rate (12.49%) and spindle speed (0.03%). The optimal parameter combination was 3500 rpm spindle speed, 175 mm/min feed rate, and 0.6 mm depth of cut, achieving the shortest machining time of 16.633 seconds. This optimization improved machining efficiency by 78.12% compared to the initial condition. These findings confirm that integrating statistical optimization with CAD/CAM simulation can effectively accelerate high-precision manufacturing processes for automotive components.

This study aims to analyze the effect of variations in the number and diameter of dimple holes on the stiffness of mild steel plates using the Dimple Dies Triangle Profile forming method. Plate stiffness is important for reducing vibrations, especially in vehicle components. The material used was a 0.6 mm thick galvanneal plate, with dimple diameters of 25 mm and 30 mm, and hole quantities of 16 and 20. Testing was conducted using a Vibroport 80 device to measure natural frequency as an indicator of stiffness. The experimental design employed the Taguchi method with an L4 orthogonal array to analyze the influence of each parameter. The results showed that the number of dimple holes significantly affected the plate’s natural frequency, while the dimple diameter had no significant effect. The optimal combination producing the highest stiffness was a 25 mm dimple diameter with 16 holes. ANOVA and S/N ratio analysis supported these findings, indicating that the number of holes contributed more than 90% to the variation. This research is expected to serve as a reference for developing stiffer and more vibration-resistant metal plate designs, particularly for automotive and other engineering structure applications.

The high cost and food competition associated with conventional biodiesel feedstocks limit its widespread adoption. This research investigates using palm fatty acid distillate (PFAD), a low-cost industrial by-product which is high in free fatty acids (FFAs), as a sustainable feedstock. A novel glycerol-free esterification route was developed employing ethyl acetate as acyl acceptor and sulfuric acid as catalyst, producing fatty acid ethyl esters (FAEEs) and valuable acetic acid as by-product. The characterization of PFAD confirmed its high FFA content of 91.78 ± 0.62%, then the Taguchi method was employed to systematically optimize key reaction parameters: reaction time (2-4h), temperature (55-75°C), catalyst amount (3-9 wt%), and ethyl acetate to PFAD molar ratio (5:1-15:1). Taguchi orthogonal design revealed that the ethyl acetate to PFAD molar ratio had the most significant impact on FFA conversion and time had the lowest impact. Optimal conditions were identified as 4h, 65°C, 6 wt%, and 15:1 molar ratio, yielding a predicted maximum FFA conversion of 87.74%. Experimental validation under these conditions achieved an average conversion of 86.28, confirming excellent agreement with the prediction. This research presents one of the first systematic Taguchi optimizations of PFAD esterification using ethyl acetate, offering a greener, glycerol-free pathway compared to conventional methanol-based processes. The proposed route contributes to sustainable biodiesel production from waste products, aligning with global renewable energy goals and economic viability.

The manufacturing sector is under intense pressure to improve product quality and productivity while reducing production costs due to the rising cost of complex products. The performance of the cutting tool was much improved after the carbide inserts were coated with CVD. As it is customary in the industry, ISO P-20 coated insert grades were utilized for the studies. Cryogenic treatment was applied to the carbide inserts by bringing the temperature down to -186°C. Frozen tungsten carbide inserts have a longer lifespan, according to the research. In order to determine the impact of cryogenic treatment on the toughness and tool life of coated and uncoated carbide inserts used to turn alloy steel, we utilised the Taguchi technique to consider both technical and economic aspects. The efficiency was tested using Taguchi orthogonal array L16, taking into account feed rate, cutting speed, insert coating, and tool type. While turning continuously, the effect of cutting speed was 18.01% and when facing intermittently, it was 9.45%. This was achieved using inserts coated with a sandwich layer of Al2O3 and TiC, with a thickness of 18.3 microns, on EN24 grade alloy steel with BHN values ranging from 234 to 236. While the feed rate contributed to 16.47% during intermittent facing operation, it was 21.66% in the continuous turning operation. When compared to untreated inserts, those that have been cryogenically treated had a 66.79% longer life during intermittent facing. At A2B2C1 parameters the inserts wear study shown that, the tool life of cryogenic treated coated carbide inserts found 42.81% higher than untreated coated carbide inserts for the same tool wear. Tempering of cryogenic-treated inserts at 200°C for 150min, resulted to 46.53% higher when compared to untreated coated carbide inserts in the continuous turning test and 71.44% higher in the intermittent facing trial compared to untreated coated carbide inserts.

Abstract This work investigates the applicability of gossypium seed biodiesel (B20) blended with tert‐butyl alcohol (TBA) for improving the performance and emission behavior of diesel engines operating under conditions representative of automotive traction applications. Experimental trials were conducted on a Common Rail Direct Injection (CRDI) engine, focusing on the optimization of injection timing (IT), injection pressure (IP), and TBA blending ratio. A combined optimization strategy employing the Taguchi method and Grey Relational Analysis (GRA) was used to identify the most favorable operating conditions, while an Artificial Neural Network (ANN) model was developed to predict engine responses. The optimization framework identified an injection timing of 27°CA bTDC, a 5% TBA blending ratio, and an injection pressure of 260 bar as the optimal parameter combination. Under these conditions, ANN predictions closely matched experimental results, with limited prediction errors of 6.9% for brake thermal efficiency (BTE), 3.7% for brake‐specific fuel consumption (BSFC), 1% for CO, 0.7% for NOx, and 11% for smoke emissions. In comparison with neat diesel operation, the optimized biodiesel TBA blend exhibited a marginal change in BTE (≈0.9%), while BSFC increased by about 8.7%, primarily attributed to the lower calorific value of the oxygenated fuel. Emission analysis revealed a significant reduction in NOx emissions of approximately 21.8%, along with a nearly 7.4% decrease in CO 2 emissions. Results show cottonseed biodiesel–TBA blends with optimized injection parameters can run effectively in CRDI engines, delivering cleaner emissions with acceptable performance trade‐offs, supporting their viability for sustainable automotive diesel applications.

Sustainable wastewater treatment by banana peel/layered double hydroxide composite under ideal conditions using the Taguchi method.

The optimization of braking working parameters was conducted by analyzing the influential factors: vehicle speed (80, 100, and 120 km/h), a quarter of the vehicle mass (200, 250, and 300 kg), and brake pressure (3, 4, and 5 MPa). The goal was to achieve the lowest temperature on the braking pad contact surface and reduce braking time. Experimental work was conducted on the test rig BRAKE DYNO 2020. By applying ANOVA analysis, it was found that vehicle speed accounts for 72% of the influence on brake pad temperature, followed by vehicle mass and braking pressure accounting for 20% and 8%, respectively. On the other hand, for the mean value of simulated vehicle braking time, the brake pressure had the biggest influence (66.78%), followed by vehicle speed (21.75%), and a quarter of the vehicle mass (10.90%). Both the Taguchi method and the regression model showed a good correlation with the experimental results.

Considered an important measurement parameter in the machining industry, surface roughness has a fundamental role in ensuring the quality of the final product. In turning operations, existing approaches to predicting surface quality rely heavily on factors related to tool-workpiece interaction, heat, and material. The objective of this research is to create a forecasting model that will help analyze the effect of the rake angle on surface quality when machining C35E steel. Taguchi's factorial design methodology is used in the experimental design. The parameters selected for this study are cutting speed, cutting depth, feed rate, and angle of attack. Using a conventional lathe and a P25 carbide tool, a set of 30 experimental data on C35E steel was used in this research. In order to evaluate surface roughness during the turning process and compare the experimental results with the predicted results, a linear regression model is used. To determine the accuracy of the predicted values, the coefficient of determination, the regression graph, and the mean square error were used. This research then presents a learning model that can predict surface roughness by mainly modifying the angle of attack when machining C35E steel. The ideal choice of this angle will improve the efficiency of turning C35E steel and increase the quality of the finished components. The application of the model thus developed demonstrates its reliability, as the discrepancy between the experimental and predicted results is negligible.

This study investigates seismic loads in single-story masonry buildings with walls of varying heights and thicknesses, and determines optimum wall dimensions for seismic resistance using the Taguchi method. For this purpose, 25 (5 × 5 = 25) different masonry building models were created with thicknesses of 16, 20, 24, 28, and 32 cm and heights of 260, 280, 300, 320, and 340 cm. The building models were analysed using a software package in accordance with the 2018 Turkish Building Earthquake Code (2018 TBEC). C-30 concrete and S-420 steel were used in the designed building models. A 12 cm thick reinforced concrete slab was placed on top of the masonry walls. A live load of 0.2 t/m2 was designed on the slab, and the mortar strength of the brick wall was taken as 30 MPa. When a building model with a height of 260 cm and a thickness of 16 cm was used as a reference, it was observed that the seismic resistance of other building models increased by approximately 72%, while shear forces increased by approximately 89% in the “x” direction and approximately 95% in the “y” direction. Furthermore, it was observed that as the ratio of wall height to wall thickness increased, the seismic resistance of the building models decreased. The seismic resistance of 25 different building models was analysed using the Taguchi method, depending on wall thickness and wall height. The analysis revealed that the building model with walls 24 cm thick and 340 cm high was the most resistant to shear forces, while the building model with walls 32 cm thick and 340 cm high provided the best resistance to seismic loads.

An indigenous breast phantom was customized to optimize the imaging quality of the CT scan according to Taguchi's methodology. A 3D printer made the base gauge of the breast phantom, and the polysmoothTM filament was sprayed. The gauge can be categorized into three major parts vertical and horizontal line pairs, two V-shape slices, and two arrays of nodules with various sizes, then a spherical shell mad by paraffin casting and the sunflower oil was infused as filling material. The Taguchi approach led to the development of a customized measuring device that enabled quantitative assessment aimed at improving the spatial resolution of CT image quality. Five essential factors for operating the CT chest scan (kVp, mAs, FOV, Pitch, and gantry rotation time) were organized according to Taguchi L18(21 × 34) suggestion. Three well-trained radiologists ranked the imaging quality in three discrete time periods according to the imaging quality's sharpness, contrast, and spatial resolution. The derived average, standard deviation, and signal-to-noise ratio of specific factors were reorganized and analyzed from the multiple measurements to propose the optimal CT chest scan protocol recommendation. Accordingly, the optimal suggestion was A1(120 kVp), B3(200 mAs), C2(FOV 350 mm2), D1(pitch 0.516) and E2(rotation time 0.75 s) to fulfill the ALARA principle. In this research, a numerical metric termed the minimum detectable difference (MDD) was introduced to evaluate imaging performance, and its calculated value demonstrated a resolution capability of approximately 1.57 mm.

The current study investigated the deposition behaviour of the tool particles made from powder metallurgical (PM) green compact tool of W-Cu for coatings on Inconel 800 superalloy substrate using the electric discharge alloying (EDA) process. The effect of process variables such as compact load (CL), voltage (V) and duty factor (DF) is systematically analysed over the output responses, i.e., material deposition rate (MDR), tool wear rate (TWR) and surface roughness (Ra). The results indicated a maximum TWR of 225.325 mg/min, a maximum MDR of 47.250 mg/min and a minimum Ra of 3.5 µm. The alloyed layer is further characterized by FESEM analysis. The weight and atomic percentage of the migrated particles are determined by EDS analysis. The elemental mapping further confirmed the enrichment of tool particles and other base material constituents within the alloyed surface. The XRD analysis further showed the existence of tool elements and the development of new compounds in the alloyed layer. The alloyed layer exhibited an average layer thickness of 11.354 µm.

Corrigendum: Design and optimization of a low voltage RF switch MEMS capacitance using genetic algorithm and Taguchi method

Abstract In this paper, the efficiency of the GTAW process is assessed by evaluating the dilution ratio of the welding process. To determine the degree of dilution and, consequently, the efficiency of the GTAW welding process, the geometry of the weld bead is studied, analyzing the following variables: root dimension, face, perimeter, area, and volume of the weld bead. This work employs an experimental design using the Taguchi methodology in an L4 orthogonal array, where two main factors are defined to weld duplex stainless steel S32001 in flat profiles, 3 mm thick, using the GTAW welding process. This work evaluates the influence of welding parameters on both the process efficiency and the weld bead geometry. Of the two welding parameters studied, amperage and welding speed, amperage has the greatest influence on both the efficiency of the welding process and the geometry of the weld bead, while welding speed has the least influence. The efficiency of the GTAW welding process, as defined by the European standard, is set at a value of 0.6. However, this study determines that for duplex welding, the efficiency of the GTAW process has reached a value of 0.87.

In deregulated electricity markets, Generation Companies (GENCOs) are exposed to substantial financial risk due to volatile and uncertain electricity prices. Traditional generation asset valuation approaches, which rely primarily on expected profit, fail to adequately capture downside risk under market uncertainty. This study proposes an integrated risk-aware framework for generation asset valuation by embedding Value-at-Risk (VaR) into a Price-Based Unit Commitment (PBUC) model. VaR is employed to quantify potential profit losses at different confidence levels, enabling GENCOs to explicitly assess downside exposure associated with electricity price fluctuations. Spot price uncertainty is modeled using the Delta-Normal approach based on historical PJM market data. The resulting nonlinear mixed-integer optimization problem is solved using an Improved Immune Algorithm (IIA) enhanced with the Taguchi Method to improve convergence stability and solution diversity. Case studies on the IEEE 15-unit system demonstrate that the proposed IIA consistently outperforms conventional evolutionary algorithms in terms of profitability, robustness, and convergence reliability. The VaR analysis further reveals pronounced left-tail risk in profit distributions, particularly during peak-load periods, highlighting the importance of risk-adjusted commitment strategies. The proposed framework provides a practical decision-support tool for GENCOs to balance profitability and downside risk in competitive electricity markets.

This paper presents an optimized approach for predicting peak temperatures during Friction Stir Welding (FSW) of Al 6061 T6 alloys. COMSOL Multiphysics is used to perform finite element analysis (FEA) to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. A novel methodology integrating the Taguchi method, Analysis of Variance (ANOVA), and machine learning was employed to optimize parameters and predict peak temperatures. The influence of these parameters was investigated through L32 Taguchi array and ANOVA, revealing axial force and tool rotational speed as the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material's melting point, indicating the need for improved thermal control. The experimental work was compared with FEA results that confirmed a strong agreement between the two. A feed-forward backpropagation neural network (BPNN) was implemented, achieving an R² of 0.9903 and a mean squared error (MSE) of 1.2746 × 10-7. BPNN predicted peak temperatures with an error of 1.01%, outperforming Taguchi (3.57%) and ANOVA (3.39%). These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality.

In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accuracy, often neglecting the optimization of viewability required for human operators to clearly perceive object depth and structure in virtual environments. To address this, this study proposes a methodology to optimize the viewability of RTAB-Map-based 3D maps using the Taguchi method, aiming to enhance VR teleoperation efficiency and reduce cognitive workload. We identified eight key parameters that critically affect visual quality and utilized an L18 orthogonal array to derive an optimal combination that controls point cloud density and noise levels. Experimental results from a target object picking task demonstrated that the optimized 3D map reduced task completion time by approximately 9 s compared to the RGB image condition, achieving efficiency levels approaching those of the physical-world baseline. Furthermore, evaluations using NASA-TLX confirmed that intuitive visual feedback minimized situational awareness errors and substantially alleviated cognitive workload. This study suggests a new direction for constructing high-efficiency teleoperation interfaces from a Human–Robot Interaction perspective by expanding SLAM optimization criteria from geometric precision to user-centric visual quality.