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This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

In this research, the free vibration sensitivity analysis of cracked fiber metal laminated (FML) beams is investigated numerically and experimentally. The effects of single and double cracks on the frequency of the cantilever beams are simulated using the finite element method (FEM) and compared to the experimental results.

In FEM analysis, the crack defect is simulated by the contour integral technique without considering the crack growth. The specimens are fabricated with an aluminum sheet, woven carbon fiber and epoxy resin. The FML specimens are constructed by bonding five layers as [carbon fiber-epoxy/Al/carbon fiber-epoxy/Al/carbon fiber-epoxy]. First, the location and length of cracks are considered input factors for the frequency sensitivity analysis. Then, the design of the experiment is produced in the cases of single and double cracks to compute the frequency of the beams in the first and second modes using the FEM. The mechanical shaker is used to determine the natural frequency of the specimens. In addition, the predicted response values of the frequency for the beam are used to compare with the experimental results.

Consequently, the results of the sensitivity analysis demonstrate that the location and length of the crack have significant effects on the modes.

Effective interaction diagrams are introduced to investigate crack detection for input factors, including the location and length of cracks in the cases of single and double cracks.

Die edge quality with its corresponding die strength are two important factors for excellent dicing quality especially for low-k wafers due to their weak mechanical properties and fragile structures. It is shown in past literatures that laser dicing or grooving does yield good dicing quality with the elimination of die mechanical properties. This is due to the excess heat energy that the die absorbs throughout the procedure. Within the internal structure, the mechanical properties of low-k wafers can be further enhanced by modification of the material. The purpose of this paper is to strengthen the mechanical properties of wafers through the heat-treatment process.

The methodology of this approach is by heat treating several low-k wafers that are scribed with different laser energy densities with different laser micromachining parameters, i.e. laser power, frequency, feed speed, defocus reading and single/multibeam setup. An Nd:YAG ultraviolet laser diode that is operating at 355 nm wavelength was used in this study. The die responses from each wafer are thoroughly visually inspected to identify any topside chipping and peeling. The laser grooving profile shape and deepest depth are analysed using a laser profiler, while the sidewalls are characterized by scanning electron microscopy (SEM) to detect cracks and voids. The mechanical strength of each wafer types then undergoes three-point bending test, and the performance data is analyzed using Weibull plot.

The result from the experiment shows that the standard wafers are most susceptible to physical defects as compared to the heat-treated wafers. There is improvement for heat-treated wafers in terms of die structural integrity and die strength performance, which revealed a 6% increase in single beam data group for wafers that is processed using high energy density laser output but remains the same for other laser grooving settings. Whereas for multibeam data group, all heat-treated wafer with different laser settings receives a slight increase at 4% in die strength.

Heat-treatment process can yield improved mechanical properties for laser grooved low-k wafers and thus provide better product reliability.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

Epoxy resins are widely used in a variety of engineering applications, including composite wind turbine blades used in the renewable energy industry, highly complex structural components for aircraft, paints, coatings, industrial tooling, biomedical systems, adhesives, electronics and automotive. Epoxies' low fracture toughness is one of the key obstacles preventing its adoption in a wider range of applications. To address epoxy's low fracture toughness, this paper aims to examine the roles of intra-ply hybridization and nano reinforcing.

This paper investigates the role of intra-ply hybridization of glass-carbon woven fibers and adding 0.8 wt.% of multiwall carbon nanotube (MWCNT) nano reinforcement to overcome the low fracture toughness of epoxy. A bending test is used to calculate the composites elastic parameters, and a notched sample three-point bending test is used to show crack behavior in addition to using materials characterization methods to reveal the effect of the MWCNT on structure, bonding, glass transition temperature (Tg) and dispersion of MWCNT in the matrix. Furthermore, this paper suggests using the finite element method to overcome the difficulty in calculating the crack extension.

Intra-ply hybridization and MWCNT reinforcement decrease the crack extension of epoxy with time. The inclusion of high-strength carbon fiber increased the fracture toughness of glass composite. Furthermore, the existence of MWCNT in the surrounding area of the notch in epoxy composites hinders crack propagation and provides stiffness at the interface by bridging the crack and eventually enhancing its fracture toughness.

Studying the role of intra-ply hybridization of glass-carbon woven fibers and adding 0.8 wt.% of MWCNT nano reinforcement to overcome the low fracture toughness of epoxy. Additionally, this research recommends using the finite element method to overcome the challenge of computing the crack extension.

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

This study aims to evaluate the failure behavior of glass fiber-reinforced epoxy (GFRE) laminate subjected to cyclic loading conditions. It involves experimental investigation and statistical analysis using Weibull distribution to characterize the failure behavior of the GFRE composite laminate.

Fatigue tests were conducted using a tension–tension loading scheme at a frequency of 2 Hz and a loading ratio (R) of 0.1. The tests were performed at five different stress levels, corresponding to 50%–90% of the ultimate tensile strength (UTS). Failure behavior was assessed through cyclic stress-strain hysteresis plots, dynamic modulus behavior and scanning electron microscopy (SEM) analysis of fracture surfaces.

The study identified common modes of failure, including fiber pullouts, fiber breakage and matrix cracking. At low stress levels, fiber breakage, matrix cracking and fiber pullouts occurred due to high shear stresses at the fiber–matrix interface. Conversely, at high stress levels, fiber breakage and matrix cracking predominated. Higher stress levels led to larger stress-strain hysteresis loops, indicating increased energy dissipation during cyclic loading. High stress levels were associated with a more significant decrease in stiffness over time, implying a shorter fatigue life, while lower stress levels resulted in a gradual decline in stiffness, leading to extended fatigue life.

This study makes a valuable contribution to understanding fatigue behavior under tension–tension loading conditions, coupled with an in-depth analysis of the failure mechanism in GFRE composite laminate at different stress levels. The fatigue behavior is scrutinized through stress-strain hysteresis plots and dynamic modulus versus normalized cycles plots. Furthermore, the characterization of the failure mechanism is enhanced by using SEM imaging of fractured specimens. The Weibull distribution approach is used to obtain a reliable estimate of fatigue life.

Additive manufacturing of polymer composites is a transformative technology that leverages the benefits of both composite material and 3D printing to produce highly customizable, lightweight and efficient composites for a wide range of applications.

In this research work, glass fiber-reinforced polylactic acid (PLA) filament is used to print the specimen via fusion deposition modeling process. The process parameters such as infill densities (40%, 50% and 60%) and raster angle/orientations (0°, 45° and 90°) are varied, and the specimens for tensile, flexural, impact, hardness and wear testing are prepared as per their respective ASTM standards.

The results revealed that with an increase in infill density, the mechanical properties of glass fiber-PLA specimens increase progressively. Optimal tensile properties and flexural properties are obtained at 0° and 90° raster angle orientations and 60% infill density. Minimum wear rate is achieved at 0° raster angle orientation and it increases at 45° and 90° raster angle orientations.

Using SEM, the microscopic analysis of the fractured specimen was analyzed to study the interface between the fibers and matrix and it indicates the presence of good adhesion between the layers at 60% infill density and 0° print orientation.