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Journal of Computing and Information Science in Engineering

December 2022 Spotlight: Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling

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On December 16, 2022, Dr. Christopher McComb (Carnegie Mellon University) presented the JCISE December 2022 spotlight talk based on his paper: Glen Williams, Nicholas A. Meisel, Timothy W. Simpson, Christopher McComb (2022), Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling. ASME J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060903. https://doi.org/10.1115/1.4055854. View the recording on ASME JCISE’s YouTube channel through the link below.

Abstract

The intersection between engineering design, manufacturing, and artificial intelligence offers countless opportunities for breakthrough improvements in how we develop new technology. However, achieving this synergy between the physical and the computational worlds involves overcoming a core challenge: few specialists educated today are trained in both engineering design and artificial intelligence. This fact, combined with the recency of both fields’ adoption and the antiquated state of many institutional data management systems, results in an industrial landscape that is relatively devoid of high-quality data and individuals who can rapidly use that data for machine learning and artificial intelligence development. In order to advance the fields of engineering design and manufacturing to the next level of preparedness for the development of effective artificially intelligent, data-driven analytical and generative tools, a new design for X principle must be established: design for artificial intelligence (DfAI). In this paper, a conceptual framework for DfAI is presented and discussed in the context of the contemporary field and the personas which drive it.

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Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.

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