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

• URL: http://arxiv.org/abs/2305.18426v1
• Date: Sun, 28 May 2023 21:44:25 GMT
• Title: Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples
• Authors: Akshansh Mishra, Vijaykumar S Jatti
• Abstract summary: 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. We introduce 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysis [0.9954382983583578]
  The present work provides an application of Global Sensitivity Analysis to supervised machine learning methods such as Random Forests. Global Sensitivity Analysis is primarily used in mathematical modelling to investigate the effect of the uncertainties of the input variables on the output. A simulation study shows that our proposal can be used to explore what advances can be achieved either in terms of efficiency, explanatory ability, or simply by way of confirming existing results.
  arXiv  Detail & Related papers  (2024-07-19T10:45:36Z)
• SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories [0.7673339435080445]
  Assessing different material properties to predict specific attributes is a fundamental requirement for materials science-based applications. Our study addresses these challenges by leveraging machine learning to analyze material properties with greater precision and efficiency. By automating the data extraction process and using the extracted information to train machine learning models, our developed model, SciQu, optimize material properties.
  arXiv  Detail & Related papers  (2024-07-11T08:12:46Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition [0.8796261172196743]
  We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition (EPD), commonly known as e-coating.
  arXiv  Detail & Related papers  (2024-01-16T14:58:21Z)
• On the Onset of Robust Overfitting in Adversarial Training [66.27055915739331]
  Adversarial Training (AT) is a widely-used algorithm for building robust neural networks. AT suffers from the issue of robust overfitting, the fundamental mechanism of which remains unclear.
  arXiv  Detail & Related papers  (2023-10-01T07:57:03Z)
• PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening [0.0]
  Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery. The development of a versatile model capable of accurately scoring binding affinity and conducting efficient virtual screening remains a challenge. Here, we propose a viable solution by introducing a novel data augmentation strategy combined with a physics-informed graph neural network.
  arXiv  Detail & Related papers  (2023-07-03T14:46:49Z)
• PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning [54.409634256153154]
  In Reinforcement Learning (RL), enhancing sample efficiency is crucial. In principle, off-policy RL algorithms can improve sample efficiency by allowing multiple updates per environment interaction. Our study investigates the underlying causes of this phenomenon by dividing plasticity into two aspects.
  arXiv  Detail & Related papers  (2023-06-19T06:14:51Z)
• Metric Tools for Sensitivity Analysis with Applications to Neural Networks [0.0]
  Explainable Artificial Intelligence (XAI) aims to provide interpretations for predictions made by Machine Learning models. In this paper, a theoretical framework is proposed to study sensitivities of ML models using metric techniques. A complete family of new quantitative metrics called $alpha$-curves is extracted.
  arXiv  Detail & Related papers  (2023-05-03T18:10:21Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.