Tuesday, November 17, 2020

Post-doc position at University of Alberta

 


 Postdoctoral Position In Topology Optimization And Finite Element Modeling 

The Position 

The Additive Design and Manufacturing Systems Laboratories has recently commissioned a laser powder bed fusion metal additive manufacturing system and a wire-arc additive manufacturing system. The laboratory has experienced significant growth and is looking to hire a post-doctoral researcher who will support the topology optimization, structure design and finite element modeling for metal additive manufacturing processes. 

This position will be open to candidates possesing an undergraduate degree in Mechanical Engineering, Civil Engineering, or a relevant discipline, and a PhD in topology optimization, design for additive manufacturing, or finite element modeling for structures under dynamic forces (PhD obtained after 01-December-2016). Applicants who have applied before to ADAMS lab may also apply provided their PhD completion date is after December 01, 2016. The initial term of the contract will be one year with a possibility of extension dependant on continuation of contract and availability of funding. 

Candidate profile and requirements 

The candidate must necessarily possess expertise and experience in the following research areas: 

1. Application of topology optimization for design and modeling of mechanical structures using techniques such as unit cell-based design, level sets, meta materials, and generative design for structural design of mechanical components under given mechanical and manufacturing design specification. 

2. Expertise in solid modeling, mesh generation, and linear and non-linear finite element analysis with applications to areas such as vibration and impact, contact, fatigue, and wear of mechanical systems and machines under varying test conditions. Experience and expertise in at least one of the main FEA software's such as ABQUS or ANSYS is required. 

3. Experience of material characterisation including mechanical testing such as tensile, flexural, and compression testing, and digital image correlation. 


In addition to above necessary requirements, candidates with experience in the following areas will be given priority: 

  1. a. Experience of working with metal additive manufacturing systems. 
  2. b. Experience of microstructure and material characterisation such as microscopy, XRD, EDX, and EBSD. 
  3. c. Experience of working with polymer additive manufacturing systems. 


The successful candidate will be required to work independently and must demonstrate excellent verbal and written English communication skills evident through high-quality journal publications in the research area. It is expected that the successful candidate will be able to start the position at the latest by January 02, 2021. The candidate should possess or be eligible to apply for a valid drivers license and will be expected to travel within the city and Alberta as per the project requirements (and following COVID-19 mandated restrictions). The candidate should also be willing and eligible to travel to other provinces, or internationally as per the project needs, but following COVID-19 mandated restrictions noted by the University of Alberta, and the Governments of Alberta and Canada. 

The Project 

The candidate's primary responsibility will be to analyze and redesign components of machinery equipment used in the energy, mining, space, and automotive industries using topology optimization techniques and metal additive manufacturing technology to improve functional performance, reliability, and quality of the systems. 

The scope of the project includes the following tasks for the design and manufacturing of components: 

  • • Applying topology optimization and assembly consolidation concepts to reimagine existing conventionally manufactured components and assemblies for novel designs suitable for AM processes. 
  • • Validation of the designs using an appropriate finite element analysis. 
  • • Experimental characterization and validation of materials and printed components with a focus on wear resistant materials through laser powder bed fusion, wire arc additive manufacturing, or plasma transfer arc additive manufacturing. 


Application Procedure 

The application may be submitted through an online submission process by accessing the link given below: 

https://forms.gle/X1X6rm2Ju7SEpadD7 

The candidates will be asked to fill an online form and upload a single PDF file which includes: 

  1. i- A cover letter that high lights your suitability for the position 
  2. ii- A detailed curriculum vitae highlighting areas of research, a list of publications, awards and honours, and a list of three professional references. 
  3. iii- A minimum of 2 samples of the candidate's most significant scholarly work pertaining to the research areas defined in the candidate profile. Additional documents may be requested upon submission of the aforementioned documents. 


Please note that only the complete applications, including the documents i-iii from the candidates graduating with Ph.D. after December 01, 2016, will be considered. The review of applications will begin immediately, and applications will be accepted until the position has been filled. 

Interested candidates should use ADAMS771611PDF as the job competition number in the online form. 

We thank all applicants for their interest; however, only those short listed for an interview will be contacted. 

The University of Alberta is committed to an equitable, diverse, and inclusive workforce. We welcome applications from all qualified persons. We encourage women; First Nations, Métis and Inuit; members of visible minority groups; persons with disabilities; persons of any sexual orientation or gender identity and expression; and all those who may contribute to the further diversification of ideas and the University to apply. 

Saturday, June 20, 2020

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)

Friday, June 12, 2020

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

Friday, June 5, 2020

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.

Friday, February 14, 2020

Photos from FACAM 2020 Workshop at USC

Here are some photos of the inspiring presentation of our experts.


 Welcome Banquet at Downtown LA


 Prof. Nabil Anwer gave a talk about "Geometrical Product Specifications Challenges 
and Opportunities in Additive Manufacturing"


 Prof. Xianfeng Gu gave a lecture about "Surface Registration 
and Comparison Based on Conformal Geometry"


 Prof. Linkan Bian presented the research about Predicting the Fatigue Performance 
of Additively Printed Metal Parts Using In-Situ Monitoring


 Prof. Ahmed QURESHI gave a talk about "Understanding Process Drivers 
for Variability and Tolerancing in Additive Manufacturing"



 Prof. Hui Wang presented his research about "Multi-printer Co-learning 
of Kinematics-induced Variations For Extrusion-based Additive Manufacturing" 

Nathan Decker presented the smart 3D printing accuracy control software
"PrintFixer" developed by USC Huang Lab


 Fabio Caltanissetta presented the research about 
"Opportunities and challenges of in-situ monitoring in AM: an update"

 Zhaohui Geng presented the research about "Freeform Tolerance Specification 
and Metrological Analysis of Reverse Engineered Models"


Yuanxiang Wang presented the research about "A Convolution Learning and Prediction of 3D Freeform Shape Accuracy for Additive Manufacturing"

Wednesday, February 12, 2020

FACAM 2020 Workshop Agenda


FACAM 2020 Agenda
10-Feb
Location: GER 206, USC


1:30 PM
Prof. Q. Huang (USC)
Welcome and Introduction
2:00 PM
Prof.Nabil Anwer (ENS Paris-Saclay) 
Geometrical Product Specifications Challenges and Opportunities in Additive Manufacturing
3:00 PM
Prof. Tirthankar Dasgupta (Rutgers U)
Sequential learning of deformation models in additive manufacturing using adaptive data augmentation strategies
4:00 PM
Prof. Xianfeng (David) Gu (Stony Brook)
Surface Registration and Comparison Based on Conformal Geometry
5:00
Break


5:30 PM
Welcome Banquet at Downtown LA


11-Feb
Location: RTH 306, USC

Chair: Prof. Anwer (ENS Paris-Saclay)
9:00 AM
Refreshment
9:00 AM
Prof. Linkan Bian (MSU)
Predicting the Fatigue Performance of Additively Printed Metal Parts Using In-Situ Monitoring
9:45 AM
Prof. Ahmed QURESHI (U of Alberta)
Understanding Process Drivers for Variability and Tolerancing in Additive Manufacturing
10:30 AM
Zhaohui Geng, PhD Student, with Prof. B. Bidanda (U of Pittsburgh)
Freeform Tolerance Specication and Metrological Analysis of Reverse Engineered Models
11:15 AM
Nathan Decker (lead), Mingdong Lyu, Yuanxiang Wang, PhD Students (USC)
PrintFixer: Smart 3D Printing Accuracy Control Service


12:00 PM
Box lunch at RTH 306



Chair: Prof. Dasgupta (Rutgers U)
1:30 PM
Prof. Hui Wang (FSU)
Multi-printer Co-learning of Kinematics-induced Variations For Extrusion-based Additive Manufacturing 
2:15 PM
Fabio Caltanissetta, PhD student, with Prof. Marco Grasso, Prof. Bianca Colosimo (Politecnico di Milano) 
Opportunities and challenges of in-situ monitoring in AM: an update
3:00 PM
Yuanxiang Wang, PhD Student (USC)
A Convolution Learning and Prediction of 3D Freeform Shape Accuracy for Additive Manufacturing
3:45 PM
Discussions
4:30 PM
adjourn

Tuesday, January 7, 2020

FACAM Workshop 2020 Information

This post will contain all of the logistical information for the 2020 workshop, hosted on USC's campus from February 10th to 11th.

It will be continually updated as plans are finalized.

Hotel Information

Participants are encouraged to stay at the USC Hotel.  In order to qualify for an internal USC discount, please provide your check in/check out dates to Prof. Huang (qiang.huang@usc.edu), or Nathan Decker (ndecker@usc.edu), who will make your reservation for you.

Talks

Once you've finalized your working title, please send it to Prof. Huang or Nathan Decker.

Activities

Optional activities are planned for after the conclusion of sessions on Monday and Tuesday.  Participants will be able to explore Los Angeles, and enjoy great food.

Transportation/Navigation Information

For those who wish to use Uber/Lift get to/from LAX, there is now a rideshare pickup/drop-off area that is used.  Info can be found here.

Public transit options between LAX and the USC are also available, but will require a bus and a train.