This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

• Keywords
• Multi-response S/N ratio, Optimized heterogeneous ensemble, Prediction performance, Response, Wire electrical discharge machining,
• Publication type
• Journal Article MeSH

Plant-derived fibres, called lignocellulosic fibres, are a natural alternative to synthetic fibres in polymer composite reinforcement. Utilizing renewable resources, such as fibre-reinforced polymeric composites made from plant and animal sources, has become a crucial design requirement for developing and producing parts for all industrial goods. Natural-fibre-based composites are used for door panels, trays, glove boxes, etc. This study involves developing and thermal analysing a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre by way of a vacuum infusion process. As per ASTM Standard, eight different sequences were fabricated and thermally characterized. In the present study, three stages of weight loss (%) are shown by the thermogravimetric analysis (TGA). The sample loses less weight during the first stage, more during the second, and more during the third. The sample's overall maximum temperature was recorded at 630 °C. It was discovered that sample D (80.1 °C) had the highest heat deflection temperature, and sample B had the lowest (86.0 °C). Sample C had a low thermal expansion coefficient, while sample G had a high thermal expansion coefficient. Sample E had the highest thermal conductivity, measured at 0.213 W/mK, whereas sample A had the lowest conductivity, at 0.182 W/mK. From the present study, it was found that sample H had better thermal characteristics. The result of the present investigation would generate thermal data regarding hybrid ramie and flax composites, which would be helpful for researchers and practitioners involved in the field of biocomposites.

• Keywords
• flax, heat deflection temperature, hybrid green composites, ramie, thermal expansion, thermal properties, thermogravimetric analysis,
• Publication type
• Journal Article MeSH