Fused Deposition Modeling (FDM), the preferred additive manufacturing technology used for creating plastic-based parts, due to its affordability and simplicity of use. A well-known material used in FDM is Polylactic acid (PLA); however, its mechanical properties limit its use as a functional part for many applications. This research investigates the effect Copper-reinforcement has on PLA material's ultimate tensile strength (UTS) during production with a FDM process. The max tensile strength measured during this study was produced using a combination of low layer thickness (0.14 mm), high infill (90%), and high printing speed (100 mm/s). The wall thickness had a point of maximum tensile strength which was found at approximately 1.2 mm before strength began to decrease. Surface finish results were achieved with a combination of 0.14 mm layer thickness, 95 mm/s printing speed, 85% infill, and 1.0 mm wall thickness, and deviations from these settings led to increased roughness due to thermal and structural factors. Multi-objective evolutionary algorithm-based optimization MOEA/D method produced a combined "knee" point for tensile strength that was greater than all the highest experimental tensile strength measures while maintaining surface roughness identical to established through experimental means, demonstrating the effectiveness of multi-objective FDM parameter optimization utilizing it.

Predicting drilling parameters accurately to guarantee the creation of high-quality holes is a critical task in the field of precision machining. The optimisation of drilling parameters for excellent hole quality is the main subject of this scientific study paper, which explores the field of predictive modelling. In order to predict thrust force, delamination, and surface roughness, the research presents a unique method that utilizes the Gated Recurrent Unit (GRU) and recurrent neural network (RNN) architecture. The main goal is to use an Adaptive Fire Hawk Optimiser (AFHO) to guide the GRU model towards ideal hyperparameters. Through systematic experimentation and meticulous analysis, the study showcases the model's capability to forecast drilling outcomes with unprecedented precision, thereby streamlining the machining process and contributing to the advancement of quality hole production.

Aluminum 1100 is a commercially pure aluminum alloy with properties suitable for applications requiring ductility and workability. It is soft, weldable, and corrosion-resistant. This study attempts to determine the influence of machining on high-speed turning operations. The experiment is designed using the Design of Experiments of Response Surface Methodology, using input parameters such as cutting speed, feed rate, and cutting depth, to estimate surface roughness, temperature, and machining time of aluminum1100 as the workpiece material, with a carbide tool used for operation. The Analysis of Variance technique has been used to test the material's performance. In contrast, the Design Expert software has been used to study the impact of cutting parameters on the workpiece. A Backpropagation ANN model is developed in MATLAB to optimize cutting parameters and reduce Ra, T, and Tm values. The ANN indicates that the lowest expected value is in this case. The Multi-Objective Genetic Algorithms are employed to forecast turning parameters, and it is observed that, for an input parameter grouping of 16 Pareto-optimal solution sets, the ideal Ra ranges from 1.37 to 1.62 µm, and the temperature ranges from 34.10 to 34.08 °C. The machining time ranges from 1.27 to 1.34 min. Among all, cutting speed has the greatest influence on the parameter. The confirmatory analysis shows that the experimental and predicted values differ by less than ±2% and agree admirably with the experimental values.

The significance of producing Carbon nanomaterials (CNMs) reinforced polymer composites are increasing in manufacturing trades due to their exceptional performances. CNM modified composites are primarily employed in structural component needs due to expanded physicomechanical properties. This paper highlights a coherent approach of the VIšekriterijumsko KOmpromisno Rangiranje(VIKOR) and Teaching learning-based optimization algorithm (TLBO) to evaluatethe Milling efficiency. The machining was performed for the Milling process of0-D carbon nano onion (CNO) reinforced polymer (Epoxy) composite at four different levels of Box Behnken Design (BBD). The Milling performances such as Material Removal Rate (MRR) and Surface roughness (SR) were optimized to enhance product quality and productivity. The control of varying process constraints, viz. Weight % of CNO filler content(A), cutting speed (B), feed rate (C) and depth of cut (D), was used to optimize the machining response. The conflicting response is aggregated through the VIKOR method to develop the fitness function for an algorithm. The process constraints play a significant role in influencing the cost and productivity ofthe machined components. The objective function derived from VIKOR was supplied as input into the TLBO algorithm. The results demonstrated that the spindlespeed, feed rate, and weight % of CNO filler are the most contributing factors for machining indices. Also, the hybrid VIKOR-TLBO module shows a lower error percentage than the conventional VIKOR method. The microstructural investigation of the machined surface reveals the feasibility of the proposed hybrid module in a production environment.

In this study, several series of experiments on turning process of AISI 12L14 free cutting steel characterized by its self-lubrication and the high percentage of lead in its composition were performed to rate the influence of cutting conditions (Vc, f and ap) on the machining performance such as surface roughness, cutting force, cutting power and material removal rate. A computer generated optimal design of experiment based on the I-optimality criteria along with analysis of variance was created to study the characterizations in turning of this steel, and desirability function was utilized for the optimization. The global optimization, combined high surface quality and productivity with low cutting power consumption, gave 12 optimal setting points provided high desirability values. The obtained correlation for surface roughness, cutting force, material removal rate and cutting power were 99.4%, 95.5%, 99.7% and 94.3%, respectively.

The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on flank wear, surface roughness, and lifespan in finish turning of AISI 304 stainless steel using multilayer Ti(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA). The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established through desirability function approach (DFA) optimization. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed. The optimum level of input parameters for composite desirability was found Vc1-f1-t1 for VB, Ra and Vc1-f1 for T, with a maximum percentage of error 6.38%.

The present work deals with some machinability studies on tool wear and surface roughness, in finish hard turning of AISI D2 steel using PCBN, Mixed ceramic and coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters viz., cutting speed, cutting time and tool hardness on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA).The relationship(s) between input variables and the response parameters are determined using a quadratic regression model. The results show that the tool wear was influenced principally by the cutting time and in the second level by the cutting tool hardness. On the other hand, cutting time was the dominant factor affecting workpiece surface roughness followed by cutting speed. Finally, the multiple response optimizations of tool wear and surface roughness were carried out using the desirability function approach (DFA).

This article investigates the effect of cutting parameters on the surface roughness and flank wear during machining of titanium alloy Ti-6Al-4V ELI( Extra Low Interstitial) in minimum quantity lubrication environment by using PVD TiAlN insert. Full factorial design of experiment was used for the machining 2 factors 3 levels and 2 factors 2 levels. Turning parameters studied were cutting speed (50, 65, 80 m/min), feed (0.08, 0.15, 0.2 mm/rev) and depth of cut 0.5 mm constant. The results show that 44.61 % contribution of feed and 43.57 % contribution of cutting speed on surface roughness also 53.16 % contribution of cutting tool and 26.47 % contribution of cutting speed on tool flank wear. Grey relational analysis and TOPSIS method suggest the optimum combinations of machining parameters as cutting speed: 50 m/min, feed: 0.8 mm/rev., cutting tool: PVD TiAlN, cutting fluid: Palm oil.

Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.

This paper suggests an advanced hybrid multi output optimization technique by applying weighted principal component analysis (WPCA) incorporated with response surface methodology (RSM). This investigation has been carried out through a case study in CNC turning of Aluminum alloy 63400 for surface roughness (Ra) and tool vibration (db) optimization. Primarily, input parameters such as spindle speed (N), feed rate (S) and depth of cut (t) are designed for experiment by using RSM Box-Behnken methodology. The aluminum alloy workpieces are machined by using coated carbide tool (inserts) in dry environment. Secondly, the empirical model for the responses as the functions of cutting parameters are obtained through RSM technique and the adequacy of the models have been checked using analysis of variance (ANOVA). Finally, the process parameters are optimized using WPCA technique. The confirmatory experiment has been performed using optimized result and it reveals that multiple response performance index (MPI) value was increased by 0.2908 from initial setting. The increases in MPI value indicates that the aforesaid optimization methodology is suitably acceptable for multi response optimization for turning process.