Despite the vast applications of fibre-reinforced polymer composites in multiple domains, conventional fabrication is laden with many difficulties, thus bringing focus on Additive manufacturing technologies. The aim of this study is to evaluate the effect of infill percentage, layer thickness and carbon fibre layer position on the mechanical tensile performance of Fused Deposition Modelling (FDM) printed short Carbon Fibre-reinforced Polylactic Acid composites. Experimentation is based on full factorial design, with elastic modulus and maximum tensile stress as the response variables. Two distinct models for output are developed using an multivariate regression analysis and Generalised Regression Neural Network and are further validated through three set of randomised experiments. Genetic Algorithm is used to optimise the outputs, the results of which agree well with experiments. Changes in the carbon fibre layer position in the fabricated composites are found to have a visible and significant effect on the mechanical performance of the composites.

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Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) were developed and compared to assess their performance with limited training data. Various smoothing factors and kernels were tested for GRNN and GPR, respectively. The prediction of models was compared in terms of the mean absolute percentage error, root mean square error, and coefficient of determination. The results showed that GPR outperforms GRNN and accurately predicts the μ-EDM process responses. The GRNN’s performance was better for less stochastic output with a discernible pattern than other outputs. The Automatic Relevance Determination (ARD) squared exponential kernel was found to be the best performing kernel among those chosen. GPR models can be used with reasonable accuracy to predetermine critical process outputs as they have R2 values above 0.90 for both training and validation data for all outputs. This work paves the way for future industrial implementation of GPR to model and predict the outputs of complex hybrid machining processes.

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