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