Additive manufacturing (AM), the process in which a three-dimensional (3D) object is built by adding subsequent layers of materials, enables novel material compositions and shapes, often without the need for specialized tooling. On March 11-13, 2024, the Board on Mathematical Sciences and Analytics of the National Academies held a workshop on Statistical and Data-Driven Methods for Additive Manufacturing. The workshop brought together researchers from different AM communities, statisticians, data scientists, and AI/machine learning (ML) experts to examine approaches that enhance dimensional accuracy and dimensional stability; recent advances and future directions in statistics, data analytics, AI, and ML; and the issues associated with a rapid advance of AM material qualification and part certification.
Table of Contents
Front Matter 1 Introduction 2 Data, Statistics, and Analytics for Additive Manufacturing in the National Laboratories 3 Enhancing Dimensional Accuracy and Stability with Digital Integration 4 Dimensional Accuracy, Part Quality, and Process Stability in Additive Manufacturing 5 Dimensional Accuracy, Part Quality, and Process Stability in Post-Additive Processes 6 Recap of Day 1 7 A Primer on Statistics, Data Analytics, and Artificial Intelligence 8 Statistics, Data Analytics, and Artificial Intelligence for Automated Machine Calibration and Toolpath Correction 9 Overview of Measurement and Metrology 10 Measurements and Calibration for Statistics, Data Analytics, and Artificial Intelligence 11 Recap of Day 2 12 Barriers to the Rapid Advance of Additive Manufacturing Material Qualification and Part Certification 13 Key Themes from the Workshop Appendix A: Public Meeting Agenda Appendix B: Biographical Information for Workshop Planning Committee Members and Speakers
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