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Progress in Additive Manufacturing

10.04.2024 | Full Research Article

Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches

verfasst von: Saty Dev, Rajeev Srivastava

Erschienen in: Progress in Additive Manufacturing

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Abstract

This study investigates and optimizes the tensile, flexural and compressive strengths of polymer parts for local and small industrial components. Extrusion-based additive manufacturing, i.e., fused deposition modeling (FDM) technology is used to manufacture the parts from acrylic butadiene styrene (ABS) material. The samples are produced according to selected process parameters such as road angle, filling percentage, layer size, printing temperature and printing speed, which are varied at three levels. The experiments are designed using a central composite design based on response surface methodology (RSM) in MINITAB software. Mechanical testing is performed using universal testing machines and data are collected for statistical analysis. In addition, hybrid approaches based on artificial intelligence are used for parameter optimization to achieve maximum tensile, bending and compressive strengths. Tensile and bending samples are also subjected to fracture mechanism investigation using scanning electron microscopy (SEM). The results show that the first technique, i.e., response surface methodology and genetic algorithm, resulted in an improvement in tensile, flexural and compressive strength by 2.5%, 7.58% and 8.86%, respectively, compared to the highest values of all experiments. The second approach, i.e., the genetic algorithm for artificial neural networks, provides the tensile, bending and compressive strengths with an improvement of 3.74%, 2.04% and 5.49%, respectively. Similarly, the third technique, adaptive neuro-fuzzy inference system genetic algorithm, yields 4.97%, 6.16%, and 2.62% improvement in tensile, flexural, and compressive strength, respectively. Overall, the results show that the hybrid optimization techniques can provide the desired mechanical strengths of FDM parts for selected applications. The optimized results are confirmed by experiments and scientific methods.

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Titel: Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches
verfasst von: Saty Dev
Rajeev Srivastava
Publikationsdatum: 10.04.2024
Verlag: Springer International Publishing
Erschienen in: Progress in Additive Manufacturing
Print ISSN: 2363-9512
Elektronische ISSN: 2363-9520
DOI: https://doi.org/10.1007/s40964-024-00606-z

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