Project Description

This convergent project will build upon emerging research opportunities at the intersection of manufacturing and computing to pioneer and validate a novel platform for data-driven, agile cyber-manufacturing of designer nanomaterials integrated into wearable devices. The proposed efforts will leverage the team’s multidisciplinary expertise in material design, processing, device integration, cyber-manufacturing, machine learning, and statistics. The research goals will be accomplished through performing a hypothesis-driven, hierarchical, and synergistic procedure that weaves design and manufacturing of materials and devices, and the development of a data infrastructure that enables efficient and reliable learning and control of process parameters. The proposed cyber-manufacturing platform and service, which do not currently exist, will enable future ubiquitous, high throughput, data-driven production of nanomaterials integrated into personalized devices for societally pervasive applications (e.g., health monitoring and treatment). During the course of the project, the co-advisors will work closely with the postdoctoral researcher to establish and implement an individual development plan. The PIs will provide the postdoc with opportunities to network with leading researchers in the advanced manufacturing and machine learning communities, and supervise graduate/undergraduate researchers. The mentoring in this project is expected to prepare the postdoc for a stellar career in engineering academia through interdisciplinary research, training, and professional development.

Start Date

07/01/2021

Postdoc Qualifications

The candidates should have prior research experience with applying machine learning algorithms and data-driven approaches to industrial processes (e.g., additive manufacturing systems, nanomanufacturing systems, etc.). We look for highly motivated individuals with a solid background in related areas such as manufacturing, electrical engineering, mechanical engineering, industrial engineering, etc.  

Co-Advisors

Wenzhuo Wu
wenzhuowu@purdue.edu
Ravi and Eleanor Talwar Rising Star Assistant Professor
School of Industrial Engineering, Purdue University
https://engineering.purdue.edu/wugroup

Arman Sabbaghi
sabbaghi@purdue.edu
Associate Professor in the Area of Applied Statistics
Associate Director, Statistical Consulting Service
Department of Statistics, Purdue University
www.stat.purdue.edu/~sabbaghi

References

Xu, S. and Wu, W. (2020) Ink‐based additive nanomanufacturing of functional materials for human‐integrated smart wearables. Advanced Intelligent Systems. doi:10.1002/aisy.202000117.

Francis J., Sabbaghi A., Shankar R., Ghasri-Khouzani M., Bian L. (2020). Efficient distortion prediction of additively manufactured parts using Bayesian model transfer between material systems. ASME Journal of Manufacturing Science and Engineering. 142(5): 051001 (16 pages).

Wang, Y. X., Raquel de Souza Borges Ferreira, Wang, R. X., Qiu, G., Li, G. D., Qin, Y., Ye, P. D., Sabbaghi, A., Wu, W. Z. (2019) Data-driven and probabilistic learning of the process-structure-property relationship in solution-grown tellurene for optimized nanomanufacturing of high-performance nanoelectronics. Nano Energy. 57, 480-491.

Ferreira R., Sabbaghi A., Huang Q. (2020). Automated geometric shape deviation modeling for additive manufacturing systems via Bayesian neural networks. IEEE Transactions on Automation Science and Engineering. 17(2): 584-598.

Sabbaghi A., Huang Q. (2018). Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables. Annals of Applied Statistics. 12(4): 2409-2429.