Balasundaram Muthu Chozha Rajan

This experimental study focused on optimizing the machinability properties of Kevlar fiber–reinforced aluminum laminates (KRALL). For this, the gray relational approach and Taguchi approach were used to optimize the abrasive water jet machining (AWJM) factors with multi-response characteristics. Kevlar fiber and aluminum-based fiber metal laminates are known as KRALL. AWJM was used to machine the KRALL. The Taguchi L9 orthogonal array was used to establish the levels and factors. Water jet pressure, stand-off distance (SOD), and nozzle feed rate were considered as process factors in the Taguchi technique. The experimental results were analyzed by using the Taguchi method and gray relational approach. The gray relational approach and analysis of variance were used to analyze the most influential factors, such as roughness and kerf angle. The performance factors of roughness and kerf angle were found to be significantly influenced by SOD. According to the detailed experimental findings, the Taguchi approach and gray relational approach successfully optimized the machining factors of roughness and minimum kerf angle in the AWJM of KRALL.

This study presents a novel hybrid polymer composite reinforced with carbon, basalt, and KMnO ₄ -treated hemp fibers, further enhanced with dual organic fillers—calcium carbonate (CaCO ₃ ) and coconut shell powder (CSF). Unlike previous studies, this work uniquely combines chemically treated natural fibers with synthetic reinforcements and dual fillers, optimized using Taguchi’s L9 orthogonal array and regression modeling. The optimized configuration of 15 wt.% treated hemp fiber, 74 wt.% epoxy concentration, and carbon–basalt hybrid reinforcement exhibited superior mechanical properties, including a tensile strength of 132.56 MPa, flexural strength of 353.77 MPa, hardness of 78 Shore D, and impact energy of 7.5 kJ/m ² . Thermal stability was significantly improved, with major degradation temperatures exceeding 383°C, as confirmed by TGA/DTG analysis. Regression models showed strong predictive accuracy, validated through residual analysis. The synergistic effect of hybrid reinforcement and optimized filler loading led to a dense, thermally stable matrix with enhanced mechanical integrity, making these composites highly suitable for applications in automotive, aerospace, and structural engineering sectors.

The use of thermoplastics has increased in recent years in a variety of fields. Some machining work is necessary to maximize the benefits of the polymer. The thermoplastic material used in this analysis was Derlin (32 mm in diameter and 15 mm in thickness), which was drilled to investigate the drilling characteristics using a Computer Numerical Control vertical milling centre. The experiments were carried out using an L9 orthogonal array with speed, feed, and pecking as input parameters to study its machining characteristics. Hard carbide tools and HSS drill bits were used in the work. Surface roughness and pecking were the outputs investigated. Analysis of variance (ANOVA) and the decision tree algorithm were used to examine the output parameters. According to the Taguchi and Decision Tree algorithm results, the significant factors for surface roughness in hard carbide tools were speed and feed. For HSS drill bits, the main dominating factor for surface roughness was speed. For roundness errors, pecking was the dominant factor. The decision tree was developed from the output, and the accuracy is high.

An investigation was done on composites containing AA6061-T6 as matrix and 15wt.% of SiC as reinforcement, where an AA6061-T6/15% SiC alloy was fabricated by stir casting methodology to achieve optimal conditions for machinability analysis utilizing electrical discharge machining (EDM). Taguchi-based L27 orthogonal array-based design of experiments was employed for experimentation to investigate the effect of process parameters like peak current (IP), pulse on time (T_on) and gap voltage (V) on material removal rate (MRR), tool wear rate (TWR), circularity (CIR), cylindricity (CYL) and perpendicularity (PP). An entropy method weights incorporated with the additive ratio assessment (ARAS) approach was used to estimate the best optimum level of input parameters. The weights were 0.087, 0.264, 0.244, 0.147 and 0.259 for MRR, TWR, CIR, CYL and PP. From the ARAS method of utility degree, the optimal conditions are obtained in the fourth experiment having the 12 A, 50 μs and 40 V with the utility degree of 0.8883. By using an scanning electron microscope, the machined surface is analysed for the microstructural examination, at higher peak current (IP) micro-voids and craters with a poor surface finish were achieved.

The process of turning hardened material with a Rockwell hardness of 45–70 on the C scale is known as hard turning. The current work involved dry turning of heat-treated oil-hardened non-shrinking (OHNS) die steel. The experiment was designed using an L9 orthogonal array. The experiment’s input parameters include speed, feed and depth of cut (DoC), while its outputs include material removal rate (MRR), surface roughness and machining time. This work uses the C 4.5-decision tree algorithm for the L9 orthogonal array, transforming low-level data into high-level knowledge through if-then-else rules. For each performance attribute, rules are framed based on certain conditions. The experimental results match the attribute-based rules.