Smart 3D Printing Quality Control Service Portal: PrintFixer 1.0

We at the University of Southern California  have been developing a smart 3D printing service portal to control dimensional quality and shape distortion of 3D printed products. The service portal is at PrinFixer 1.0

Selected users can be provided free dimensional quality control service. Join us to build a quality control service platform for 3D printing user community!

Contact: Prof. Q. Huang (qiang.huang@usc.edu)

Special Issue on Machine Learning in Dimensional Metrology: Call for Papers

https://www.journals.elsevier.com/precision-engineering/call-for-papers/special-issue-on-machine-learning-in-dimensional-metrology

Precision engineering has been, and continues to be, one of the disciplines needed to enable future technological progress, especially in the area of manufacturing. Demands for increasing precision have also evolved in almost all other industrial sectors; ranging from planes, trains and automobiles, to printers (printing electronics and optical surfaces), scientific and analytical instruments (microscopes and telescopes to particle accelerators), medical and surgical tools, and traditional and renewable power generation. At the root of all of these technologies are increasingly advanced machines and controls, and in manufacturing, advanced products. Metrology plays a key role in precision engineering, allowing the degree of precision and accuracy to be quantified and used in later processes. Modern digital manufacturing (a.k.a. Industry 4.0) requires the creation, manipulation and sharing of large amounts of data. But, the adoption of digital technology in manufacturing processes is currently hindered by lack of efficiency and confidence in data that is captured within those processes. Confidence in data is the key enabler for adoption of the Industry 4.0 methodologies. Through traceability, metrology is one of the pillars for demonstrating confidence in data, without which, industry suffers from unnecessary waste, inefficient processes and increased costs for quality. A significant issue with the adoption of digital manufacturing is the vast amount of data that can be produced with new measurement technologies. But, this data-rich issue can be an opportunity if advanced data handling, analysis and learning methods can be developed and employed. These issues are ideal for machine learning (ML), which is only now being utilised for measurement applications, to enhance the capability and performance of instruments, e.g. understand surface orientations, automatically segment 3D point clouds, infer surface information from missing data using a priori information and automatically segment objects, especially for machine vision applications.

Many of the speed and data challenges that plague metrology for digital manufacturing could be overcome by combining ML approaches with precision engineering/metrology. The special issue will investigate where machine learning can be applied in the following areas:

• Coordinate metrology
• ML in optical metrology
• ML for quality control and in-process metrology
• ML for surface metrology
• ML for X-ray computed tomography
• ML for critical dimension and overlay
• Sampling and interpolation in surface measurement
• Ultra-fast surface defect and feature recognition
• Uncertainty methods with ML measurement models
• Monte Carlo simulation using ML methods

Guest Editors:
Prof. Richard Leach
University of Nottingham
richard.leach@nottingham.ac.uk

Dr. Samanta Piano
University of Nottingham
Samanta.piano@nottingham.ac.uk

Dr. Benjamin Haefner
Karlsruhe Institute of Technology
benjamin.haefner@kit.edu

Prof. Robert Schmitt
RWTH Aachen and Fraunhofer IPT
r.schmitt@wzl.rwth-aachen.de

Prof . Bianca Colosimo
Polytechnico Milano
biancamaria.colosimo@polimi.it

Submission Deadline: February 2021
Acceptance Deadline: April 2021

Please submit your paper here - https://www.evise.com/profile/#/PRE/login

USC Post-doc Position on Data Science for Engineering Applications (Position filled)

Posting Date: June 5, 2020

Application Deadline: Open until filled

Prof. Huang's Lab, in the Epstein Department of Industrial and Engineering at the University of Southern California (HuangLab.usc.edu), seeks applicants for a postdoctoral scholar working in Data Science for Engineering Applications. Project goals focus on, but not limited to, developing AI and Machine Learning algorithms for non-intrusive inspection and maintenance systems with imaging data.  The duration of the initial appointment will be for one year. The position is can be renewable based on research productivity, performance and funding. Performance review is conducted every six months.

USC provides outstanding benefits for post-doc scholars. Policy regarding minimum salary and benefit can be found at https://policy.usc.edu/postdoctoral-scholars/.

Duties & Responsibilities:
The postdoctoral scholar will be expected to take a leading role in developing machine learning algorithms and defect detection methods for non-intrusive inspection and maintenance systems. Other than top-notch publications, the postdoctoral scholar is expected to prepare project reports, supervise and collaborate with junior PhD students, and attend conferences to present his/her research work. 

Stating date: September 16, 2020

Contact: Prof. Huang (Email: qiang.huang@usc.edu)

Note: This announcement might be updated with more details.