Nathan won the RAMP 2019 Competition!

A great achievement to win the RAMP 2019 award (https://event.asme.org/MSEC/Program/RAMP-2019-Competition) as a first-year PhD student! Congratulations to Nathan!

Hooding Ceremony for Yuan on May 9, 2019

Yuan comes back to USC from Facebook for the hooding ceremony today. Nice to have her in the lab.

Nathan becomes one of the three finalists for the RAMP 2019 competition!

Being a first-year PhD student at USC, Nathan will present  his paper at ASME MSEC 2019 conference and compete for the RAMP 2019 award (https://event.asme.org/MSEC/Program/RAMP-2019-Competition)!

Happy New Year! And Have a Productive and Prosperous 2019

Journal Article: Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence of Lurking Variables

This paper, written by Dr. Arman Sabbaghi and Dr. Qiang Huang, has been recently published by the Annals of Applied Statistics. It presents a strategy based on the engineering effect equivalence principle to address the fundamental challenge in model transfer of handling lurking variables across different environments. The link for the published article follows below.

https://projecteuclid.org/euclid.aoas/1542078050

Comments and discussions are most welcome!

Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement -- Available online

http://manufacturingscience.asmedigitalcollection.asme.org/article.aspx?articleid=2707894

Co-authored by He Luan, Marco Grasso, Bianca M. Colosimo and Qiang Huang, this study develops a data-driven prescriptive modeling approach as a promising solution for geometric accuracy improvement in Laser powder bed fusion (LPBF)  processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

Our modeling work is applicable to relatively repeatable LPBF processes where there are no large build-to-build variations. Machine-to-machine variation is not considered in this study. Though the proposed data-analytical black-box modeling framework can be applicable to the production of other geometries, further experimentation and analysis is needed to investigate the LPBF process performance when building larger products with more complicated shapes

Yuan Jin passed her dissertation defense on "Statistical Modeling and Process Data Analytics for Smart Manufacturing" on October 2nd, 2018.

Congratulations! Dr. Yuan Jin will join Facebook as a Research Scientist in Machine Learning Track.

2019 FACAM Workshop will be held at ENS Paris-Saclay on June 17-18 2019.

Please contact Prof. Nabil Anwer and Prof. Q. Huang if you are interested in the workshop.

He Luan passed her dissertation defense on "Statistical Modeling and Machine Learning for Shape Accuracy Control in Additive Manufacturing" on April 27, 2018

Congratulation! She has started a career at HP Lab.

Marie Skłodowska Curie Action (MSCA) training program - Individual Fellowship call 2018 at Politecnico di Milano

The aim of the call is to attract and train young and talented researchers to successfully applying for MSCA European Fellowship with the Politecnico di Milano as host institution. Candidates will be pre-selected based on their expression of interest, CV and motivation. Promising candidates will be invited to the Politecnico di Milano for 3 days (11-13 June 2018) to meet with their supervisors, to visit laboratories and facilities, to attend an in depth training course on the proposal writing and to make use of full support in the application writing process by POLIMI advisors.

The call involves several topics.

One topic at the Department of Mechanical Engineering of Polimi: Metal Additive Manufacturing for Industry 4.0. The focus is on bridging data analytics and statistical methods to additive manufacturing for novel intelligent systems and zero-defect manufacturing.

Details can be found here:

https://www.polimi.it/it/docenti-e-staff/bandi-e-concorsi/bando-per-lmsca-ef-master-class-2018-polimi/

The due date for submission is April 30th, 2018.