Machine learning model for predicting the hardness of additively manufactured acrylonitrile butadiene styrene

Abstract

Additive manufacturing is an incipient technology with great potential to achieve excellent mechanical properties with minimal material wastage. Additive manufacturing is well known for its quick fabrication of end-use components. The process parameter described during slicing process will influence the mechanical properties of the additively fabricated components. In this present study, machine learning models such as linear regression, decision tree, random forest, adaboost were used to optimize the process parameters and to predict hardness value for other process parameters. Machine learning models such as linear regression, decision tree, random forest, and adaboost were used to predict the output response. These models were selected to illustrate the variance in performance of various kinds of machine learning model on real-time experimental data. The test specimens were fabricated through fused filament fabrication (FFF). Acrylonitrile Butadiene Styrene (ABS) is an engineering thermoplastic used as a feedstock material. The hardness values were determined using a shore D durometer, which is specifically meant to measure the hardness of plastics and rubbers. The design of experiments was evaluated by considering four parameters at three different levels. Infill density, layer thickness, print orientation, and raster orientation are the four parameters, which was taken into account. The robustness and acceptability of four different machine learning models were evaluated at various error metrics. From the results, the adequacy of random forest model is highlighted over other machine learning models. From observation, the value of the co-efficient of determination(R2) for various models ranged from 0.8437 to 0.9136, with the random forest model ranking the highest (0.9136). This research will be helpful in predicting the level of input parameters needed to obtain the maximum hardness of ABS material.