Search Results (17)

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L₂₇ orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R²) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R² and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article

Additive manufacturing (AM) methods are increasingly being adopted as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of the layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and especially to impact strength. Most products used in the industry have complex geometries and thin walls. Therefore, solid infill is often required in production, and this production must take place in the ZX orientation. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in terms of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientations. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, the printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed for experimental design and then each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, and factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m², followed by PA6 GF30 at 8.1 kJ/m², and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m². Compared to the literature and manufacturer datasheets, it was concluded that the impact strength values had significantly increased and the chosen experimental factors and their levels, particularly nozzle temperature, were effective. Full article

This paper focuses on the multi-objective optimization of friction stir welding process parameters. Three input variables, including the axial load (AL), tool rotation speed (RS), and tool tilt angle (TA), were selected to optimize the mechanical characteristics of Al-2024 friction stir-welded (FSW) joints. The ultimate tensile strength (UTS) and impact energy (IE) were selected as output responses to measure the mechanical characteristics of Al-2024 FSW joints. A total of nine experiments, using the L9 orthogonal array as part of the Taguchi method, were performed to determine the significance of the process parameters. Gray relational analysis (GRA) was employed to conduct the multi-objective optimization of these combinations of process parameters. The results of the analysis of variance (ANOVA) showed that the AL has the most significant effect on the UTS and IE of Al-2024 FSW joints, followed by the TA and RS. The Taguchi-based GRA analysis revealed that an AL of 10 KN, a TA of 2 degrees, and an RS of 1500 rpm resulted in an optimal UTS of 333.06 MPa and an IE of 40.62 Joules. In these optimal experimental settings, optical microscopy analysis revealed the presence of a recrystallized fine-grain structure in the heat-affected zone of the welded region. Full article

ZE41 is a magnesium alloy used in heat exchangers, condensers, reactors, and pressure vessels where good surface qualities are required. This current research focuses on the investigation of the striation angle (SA), surface roughness (SR), and striation zone (SZ) in ZE41, using abrasive waterjet cutting. Significant variables in the investigation were jet pressure, traverse speed, mass flow rate, and stand-off distance. In accordance with Taguchi’s L18 orthogonal array, the responses for each cut test were studied. In addition, the principal component-based grey incidence (PGI) technique successfully combined the strengths of the optimization tool to identify the ideal parameter condition. The confirmation results revealed that the PGI technique improved SR by 4.02%, SZ by 6.67%, and 1.48% in the SA. Full article

Three-dimensional printing (3DP), known as additive layer manufacturing (ALM), is a manufacturing process in which a three-dimensional structure is constructed by successive addition of deposited layers. Fused Deposition Modeling (FDM) has evolved as the most frequently utilized ALM process because of its cost-effectiveness and ease of operation. Nevertheless, layer adhesion, delamination, and quality of the finished product remain issues associated with the FDM process parameters. These issues need to be addressed in order to satisfy the requirements commonly imposed by the conventional manufacturing industry. This work is focused on the optimization of the FDM process and post-process parameters for Polylactic acid (PLA) samples in an effort to maximize their tensile strength. Infill density and pattern type, layer height, and print temperature are the process parameters, while annealing temperature is the post-process parameter considered for the investigation. Analysis based on the Taguchi L18 orthogonal array shows that the gyroid infill pattern and annealing cycle at 90 °C results in a maximum ultimate tensile strength (UTM) of 37.15 MPa. Furthermore, the regression model developed for the five variables under study was able to predict the UTS with an accuracy of more than 96%. Full article

A high strength-to-weight ratio, stiffness, fatigue resistance, a low coefficient of thermal expansion, and tailorable properties make glass-fibre-reinforced plastic (GFRP) a popular choice for a wide range of applications, including aircraft structures, automobile chassis, and shipbuilding. However, milling GFRP composites is challenging because of their heterogeneous nature and two-phase structure, which lead to high cutting forces and delamination. A statistical experiment was carried out using the Taguchi design of experiments to investigate the effect of machining settings on GFRP composite performance metrics such as surface delamination, surface roughness, and material removal rate. The L27 orthogonal array was used for the experiment, and it served as the foundation for the choice of material, input variables, levels, and output response variables. The experiment’s outcomes were analysed using MINITAB software® 18 Version and the Analysis of Variance (ANOVA) method. Based on the signal-to-noise (S/N) ratio, the ideal conditions were selected, and confirmation studies were carried out to ensure their applicability. In order to identify the ideal circumstances for the manufacturing and machining parameters, the data were normalised to a range from zero to one. To overcome the difficulties involved in milling GFRP composites, a thorough investigation and optimisation of the manufacturing process factors and machining settings is essential. Full article

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed samples. Nozzle Temperature, Layer Thickness, Orientation Angle, Raster Deposition Angle, Printing Speed, Bed Temperature, and Infill Density were studied. An L27 orthogonal array was compiled with five replicas. A total of 135 trials were conducted, following the ASTM D695-02a specifications. The stopwatch method was used to assess the construction time and energy usage. The compressive strength, toughness, and elasticity modulus were experimentally determined. The Taguchi technique ranks each control parameter’s impact on each response measure. The control parameter that had the greatest impact on both energy use and printing time was layer thickness. Additionally, the infill density had the greatest influence on the compressive strength. Quadratic regression model equations were formed for each of the response measures. The ideal compromise between mechanical strength and energy efficiency is now reported, with merit related to technological and economic benefits. Full article

Thermal spray is an effective process for the fabrication of a metal matrix composite (MMC), where a zirconium diboride reinforcement is embedded in a molybdenum matrix to enable the combining of favorable properties in a new composite. The combination of two leading materials in the category of ultra-high-temperature ceramics (UHTCs) is due to a very high melting point (Mo: 2623 °C and ZrB₂: 3245 °C), high thermal conductivity (Mo: 139 W/m°C and ZrB2: 24 W/m°C), good thermal shock resistance, low coefficient of thermal expansion (Mo: 5.35 µm/m°C and ZrB₂: 5.9 × 10⁻⁶ K⁻¹), retention of strength at elevated temperatures and stability in extreme environments. Thermal spraying of the Mo/ZrB2 composite possesses a non-linear behavior that is influenced by many coating variables. This characteristic makes finding the optimal factor combination difficult. Therefore, an effective and strategic statistical approach incorporating systematic experimental data is needed to optimize the process. In this study, the L9 orthogonal array in the Taguchi approach was utilized to optimize the spraying distance (SD), number of passes (NP), pressure (P) and coat-face temperature (T_CF) using a dummy fiberglass substrate. The performance was evaluated based on the coating density (C_d) of the surfaces. Based on confirmation tests, our Taguchi analysis determined the ideal process parameters, which considerably enhanced the coating process. From the output response of the ANOVA, the most influential parameters for achieving a high coating density (C_d) were determined to be SD = 20 cm, NP = 24, P = 4 bar and T_CF = 330 °C ((SD.)1-(NP.)3-P2-(S.T.)3). These observations show that the coating density (C_d) was significantly influenced by the coat-face temperature, followed by the number of passes, spraying distance and pressure with the following contributions 6.29, 17.89, 17.42 and 3.35%, respectively. Full article

With the growth of the manufacturing industry, the demand for alloy materials with high hardness, toughness, and impact strength has increased. Since products from such alloy materials are extremely difficult to manufacture with high accuracy and reduced surface roughness using traditional machining techniques, wire electric discharge machining can be used to machine such complex parts with more precision. In this case-study-based research, machining factors such as current, pulse-on time, and voltage are studied to determine their effects on the material removal rate for AISI 1045 medium carbon steel. The Taguchi L₉ orthogonal array is used in the design of experiments for optimization. Statistical techniques such as analysis of variance and signal-to-noise ratio are used to identify the control parameters that matter most in bringing about optimal results. Based on the results, the current is the most crucial control variable in this investigation. Moreover, the maximum material removal rate obtained was 0.7112 mm³/min with the obtained optimized values of current (I) = 16 A, voltage (V) = 50 V, and pulse-on time (Ton) = 100 µs. Full article

In the present study, a comparison of two widely used optimization approaches for fused deposition modeling (FDM), that is, Taguchi method in contrast with response surface method (RSM), was investigated. Four operating parameters, namely extrusion temperature, layer thickness, raster width, print speed, and their interaction terms, were identified as control variables with three levels, while tensile strength and compressive strength were selected responses. L27 orthogonal array and face-centered central composite design (FCCCD) were used for the experimental approach for Taguchi and RSM, respectively. The signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were employed to find the optimal FDM parameter combination as well as the main factor that affect the performance of the PLA samples. Based on experimental results, it was observed that conclusions about significant ranking of parameters on FDM process from these two methods were different. However, both the Taguchi method and RSM succeed in predicting better results compared with the original groups. In addition, the optimum combinations for tensile strength and compressive strength obtained from the RSM were 2.11% and 8.15% higher than Taguchi method, respectively. Full article

Our objective was to determine and optimize the significant parameters affecting mechanical properties and mean fiber diameter (MFD) of a novel GTR/GBR membrane composed of polycaprolactone (PCL) and chicken eggshell membrane (ESM). For this, we prepared electrospun membrane specimens (n = 16) with varying concentrations of PCL, ESM, nano-hydroxyapatite (HAp), and altered electrospinning parameters as generated by DOE++ software. After the determination of MFD and mechanical properties for all specimens, Taguchi orthogonal array L8 design was used to screen significant factors affecting the MFD and mechanical properties. PCL wt%, ESM wt%, HAp wt%, applied voltage (AV), flow rate (FR), and spinneret-collector distance (SCD) were the independent variables investigated. The response variables analyzed were MFD, tensile strength (TS), and elastic modulus. ANOVA outlined ESM wt%, HAp wt%, AV, FR, SCD, and an interactive effect between PCL wt% and AV to be the significant factors affecting modulus values of an electrospun PCL/ESM membrane (p < 0.05). Furthermore, concentrations of PCL and ESM were the significant factors affecting MFD (p < 0.05) and there were no significant factors affecting the TS values. Optimization using DOE++ software predicted that the maximal TS of 3.125 MPa, modulus of 278.168 MPa, and MFD of 882.75 nm could be achieved. Full article

A new non-dispersive, anti-washout grout consisting of ordinary Portland cement, slag, superplasticizer, and methylbenzyl cellulose is proposed herein for the treatment of open karst, jointed and fractured rock, open-work gravel, and permeable sediments. A series of laboratory experiments were performed to design an anti-wash out grout suitable for grout injection of coarse aggregates depicting partially and open-jointed saturated rock mass and grouting concrete aggregates for underwater construction. The Taguchi orthogonal array was used to obtain nine different grout mix ratios. A total of four variables were considered, each with three different levels of the water–cement ratio, slag, and dosage of additives such as the superplasticizer and methyl benzyl cellulose. The laboratory determination of grout characteristics recording of mini slump, temperature, pH, visual assessment of grout dispersion, bleeding, and initial setting time and as well as uniaxial compressive strengths and permeabilities of the hardened grout samples were tested. To evaluate the suitability of the grout mixes, an analysis of variance was used for factor analysis and Grey relational analysis (GRA) was used to determine the optimal grout mix design. Based on the GRA, the following levels of the factors afforded the best results: water level 1 (0.3%), SP level 3 (0.01%), methylbenzyl cellulose level 2 (0.002%), and slag level 3 (0.1%). This paper describes the research methodology, detailed research observations, and analyses involved in designing the appropriate concrete mix. Based on the conclusions, relevant commendations regarding the suitability of grout testing equipment and grout mix designs are presented. Full article

In this paper, we study the entropy functions on extreme rays of the polymatroidal region which contain a matroid, i.e., matroidal entropy functions. We introduce variable strength orthogonal arrays indexed by a connected matroid M and positive integer v which can be regarded as expanding the classic combinatorial structure orthogonal arrays. It is interesting that they are equivalent to the partition-representations of the matroid M with degree v and the $(M,v)$ almost affine codes. Thus, a synergy among four fields, i.e., information theory, matroid theory, combinatorial design, and coding theory is developed, which may lead to potential applications in information problems such as network coding and secret-sharing. Leveraging the construction of variable strength orthogonal arrays, we characterize all matroidal entropy functions of order $n\le 5$ with the exception of $log10·U_{2,5}$ and $logv·U_{3,5}$ for some v. Full article

Three-dimensional (3D) printing is a manufacturing technology which creates three-dimensional objects layer-by-layer or drop-by-drop with minimal material waste. Despite the fact that 3D printing is a versatile and adaptable process and has advantages in establishing complex and net-shaped structures over conventional manufacturing methods, the challenge remains in identifying the optimal parameters for the 3D printing process. This study investigated the influence of processing parameters on the mechanical properties of Fused Deposition Modelling (FDM)-printed carbon fiber-filled polylactide (CFR-PLA) composites by employing an orthogonal array model. After printing, the tensile and impact strengths of the printed composites were measured, and the effects of different parameters on these strengths were examined. The experimental results indicate that 3D-printed CFR-PLA showed a rougher surface morphology than virgin PLA. For the variables selected in this analysis, bed temperature was identified as the most influential parameter on the tensile strength of CFR-PLA-printed parts, while bed temperature and print orientation were the key parameters affecting the impact strengths of printed composites. The 45° orientation printed parts also showed superior mechanical strengths than the 90° printed parts. Full article

A robust measurement system in the Taguchi Method as a testing method should be explained from the beginning of an experimental design until the application of the optimum condition. Measurement has always been described either by discussing the measurement concepts theoretically or demonstrating a case study on how the data measurement is being done practically. The distance between theory and practical case study that connects the test method used for measurement is always missing. In this paper, a case of T-peel test on strength measurement is used to reflect the robust measurement system, which includes the theory of experimental design together with methods to achieve the optimum condition. Seven control factors, two noise factors with one signal factor are used with orthogonal array L18. Not only the experiment results, but methodology on choosing the control, noise, and signal factors are described intensively. Therefore, the aims are to provide the procedure on evaluating optimum conditions, to analyze variability and optimization of T-peel test when measuring the strength, and to establish a mainstream flow to achieve high-quality experimental design for a robust measurement system. As a result, a robust measurement system that includes variation elimination is developed, which consists of four elements—F (function), N (noise), C (Control), and O (Optimization). The elements of the F-N-C-O system are connected to one another by the Plan-Do-Study-Act; P-D-S-A cycle. The results affect the existing measurement system by enlightening the black box of parameter design behind optimization results in Taguchi Method. Thus, the measurement is more convergent and obtain higher degree of confidence in parameter design. Full article