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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.

Surface roughness and delamination during the milling of carbon fiber reinforced polymer (CFRP) composite parts in aviation can lead to component rejection. This article aims to optimize cutting conditions to reduce these failures while ensuring compliance with aviation standards. By improving machinability, the goal is to minimize part rejection rates and scrap, optimizing costs and increasing safety.

Full factorial experimental design and response surface methodology (RSM) were used to establish relationships between the cutting parameters and the cutting force, delamination and surface roughness. To validate the model and identify significant parameters, analysis of variance (ANOVA) was performed. The cutting parameters were optimized to reduce cutting force and improve surface quality using ANOVA and RSM.

The lowest response values can be achieved with a cutting speed of 285.35 m/min and a feed of 358.57 mm/min using the Aluminum Chromium Nitride (AlCrN)-coated tool. Accordingly, the optimum cutting force was obtained as 190.97 N, delamination depth as 1.562 mm and surface roughness as 1.431 µm. It has been seen that the obtained surface roughness and delamination values are consistent with aviation literature studies, sectoral data and standards.

This study uniquely examines cutting force, surface roughness and delamination using Titanium Aluminum Nitride (TiAlN)- and AlCrN-coated tools instead of traditional Poly Cyristaline Diamond (PCD) tools. It employs a two-stage experimental framework, starting with a full factorial design followed by RSM. The initial data have been used as inputs for optimization in the second stage to achieve more accurate results.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).