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This research paper explores the impact of various input parameters,including Infill percentage, Layer Height, Extrusion Temperature, and PrintSpeed, on the resulting Tensile Strength in objects produced through additivemanufacturing. The main objective of this study is to enhance our understandingof the correlation between the input parameters and Tensile Strength, as wellas to identify the key factors influencing the performance of the additivemanufacturing process. To achieve this objective, we introduced the utilizationof Explainable Artificial Intelligence (XAI) techniques for the first time,which allowed us to analyze the data and gain valuable insights into thesystem's behavior. Specifically, we employed SHAP (SHapley AdditiveexPlanations), a widely adopted framework for interpreting machine learningmodel predictions, to provide explanations for the behavior of a machinelearning model trained on the data. Our findings reveal that the Infillpercentage and Extrusion Temperature have the most significant influence onTensile Strength, while the impact of Layer Height and Print Speed isrelatively minor. Furthermore, we discovered that the relationship between theinput parameters and Tensile Strength is highly intricate and nonlinear, makingit difficult to accurately describe using simple linear models.

Employing Explainable Artificial Intelligence (XAI) Methodologies to  Analyze the Correlation between Input Variables and Tensile Strength in  Additively Manufactured Samples