M icrostructure Feature R ecognition for M a te ria ls Using S u rfacelet-B ased M ethods for C om puter-A ided D e s ig n -M a te ria l In teg ratio nWith the material processing freedoms o f additive manufacturing (AM), the ability to characterize and control material microstructures is essential if part designers are to properly design parts. To integrate material information into Computer-aided design (CAD) systems, geometric features o f material microstructure must be recognized and represented, which is the focus o f this paper. Linear microstructure features, such as fibers or grain boundaries, can be found computationally from microstructure images using surfacelet based methods, which include the Radon or Radon-like transform fo l lowed by a wavelet transform. By finding peaks in the transform results, linear features can be recognized and characterized by length, orientation, and position. The challenge is that often a feature will be imprecisely represented in the transformed parameter space. In this paper, we demonstrate surfacelet-based methods to recognize microstruc ture features in parts fabricated by AM. We will provide an explicit computational method to recognize and to quantify linear geometric features from an image.