Faculty Directory

BACKGROUND

Dr. McGregor is interested in how software and data can be leveraged to improve manufacturing and part qualification. He investigates methods for automating metrology of diverse part geometries using computer vision and computational geometry, and is developing machine learning models for rapidly qualifying additively manufactured parts. He is especially interested in developing models for qualifying part designs that have never previously been made, enabling first-time-right manufacturing strategies for additive manufacturing.

EDUCATION

• PhD, Mechanical Engineering, University of Illinois Urbana-Champaign
• BS, Mechanical Engineering, University of Arizona

• Advanced Manufacturing / Smart Manufacturing
• Additive Manufacturing / 3D Printing
• Machine Learning
• Metrology / Optical Metrology
• Computer Vision / Machine Vision
• Part Qualification
• Biomedical Devices

ENME 440/743: Applied Machine Learning for Engineering Design

ENME 437/697: Data Science in Manufacturing Quality Control

ENME 202: Computing Fundamentals for Engineers

ENME 416 / ENME 744: Additive Manufacturing

ENME 472: Integrated Product and Process Development

• ‪Davis J. McGregor‬ - ‪Google Scholar‬

SELECTED PUBLICATIONS

1. B.N. Mengesha, V. Aute, D.J. McGregor, and S. Azarm. “Multi-objective optimization of process parameters for part quality with laser powder bed fusion: a heat exchanger application.” ASME J. Mech. Des., 2025.

2. A.R. Chaudhary, Z. Wen, and D.J. McGregor. “Automated metrology for additively manufactured parts using deep learning and computer vision.” Proc. SPIE 13572, Automated Visual Inspection and Machine Vision VI, 2025.

3. M.V. Bimrose, D.J. McGregor, C. Wood, S. Tawfick, and W.P. King. “Additive manufacturing source identification from photographs using deep learning.” npj Adv. Manuf., 2, 20, 2025.

4. M.V. Bimrose, T. Hu, D.J. McGregor, J. Wang, S. Tawfick, C. Shao, Z. Liu, and W.P. King. “Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography.” J. of Intelligent Manufacturing, 2024.

5. M. Mehta, M.V. Bimrose, D.J. McGregor, W.P. King, and C. Shao. “Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories.” J. of Manufacturing Systems, 74, 752-761, 2024.

6. C.H. Conway, D.J. McGregor, T. Antonsen, C. Wood, C. Shao, and W.P. King. “Geometry repeatability and prediction for personalized medical devices made using multi-jet fusion additive manufacturing.” Additive Manufacturing Letters, 9, 100200, 2024.

7. D.J. McGregor, M.V. Bimrose, C. Shao, S. Tawfick, and W.P. King. “Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials.” Additive Manufacturing, 55, 102848, 2022.

8. D.J. McGregor, M.V. Bimrose, S. Tawfick, and W.P. King. “Large batch metrology on internal features of additively manufactured parts using X-ray computed tomography.” J. of Materials Processing Technology, 306, 117605, 2022.

9. D.J. McGregor, S. Tawfick, and W.P. King. “Automated metrology and geometric analysis of additively manufactured lattice structures.” Additive Manufacturing, 28, 535-545, 2019.

PATENTS

1. T.C. Gossett, G.T. Pinto, C.D. Wood, D.J. McGregor, and W.P. King. “De-identified search of part designs.” U.S. Patent 12,462,262 B2, Nov. 04, 2025.

2. T.C. Gossett, G.T. Pinto, C.D. Wood, D.J. McGregor, and W.P. King. “De-identified search of part designs.” U.S. Patent 12,443,964 B2, Oct. 14, 2025.

3. W.P. King, S. Tawfick, M. Bimrose, C. Wood, and D.J. McGregor. “Conformance testing of manufactured parts via neural networks.” U.S. Patent 12,125,190 B1, Oct. 22, 2024.