Dear colleagues,
We are seeing candidates (postdoctoral fellow and masters of applied
science) for R&D on the development of software solutions to optimize objects and
toolpaths for computer aided design (CAD). This project, conducted at École de technologie
supérieure, Montreal, Canada, aims to develop optimization tools in C++ using the Open
Cascade library. The goal is to reduce errors between a computer-aided design (CAD)
modeled object and a manufactured object. The entire project will employ three strategies
to minimize errors: (1) optimizing object surfaces, (2) optimizing cuts, and (3)
optimizing filling. The first strategy (surface
optimization) will focus on the 3D errors measured between a CAD object and the same
object built by additive manufacturing. To compensate for errors, we will create a second
(morphed) version that, once built, will result in an object closer to the desired
geometry. To achieve this, we will deform the surfaces of the new object to compensate for
the errors.
This deformation will be obtained through a minimization approach (such as gradient
descent or linear least squares) and will use measured deviations as input. The challenge
will be to maintain a rich and continuous representation of the surfaces while proposing
an efficient algorithm that minimizes computation costs. The second strategy involves
developing a system to effectively visualize surfaces, cuts, optimized objects, etc. The
project will identify and implement the most relevant cutting methods related to additive
manufacturing processes. Then we will optimize the positioning of cuts considering the
characteristics of additives manufacturing processes, such as incremental forming. Taking
into account the manufacturing process characteristics and the targeted number of cuts,
this approach will optimize the parameters of the cutting method to mitigate precision
problems. The optimization will be coupled with process simulations by the NRC for
predicting geometric errors. The third strategy focuses on optimizing filling for
deposition-based manufacturing processes. The project will consider cold spray deposition
processes of metallic powders, which can be modeled by scanning a Gaussian distribution.
Variability in Gaussian size due to feed-rate variations from the controller will be
factored to address the resulting errors through optimization loops. Multiple deposition
passes with tool orientation variations will be considered to compensate for material
shortages in the Gaussian's periphery. The recruited individuals will focus on one or
more of these three strategies based on their profile and expertise.
Expected candidate: The ideal candidate has some knowledge of computer graphics and is a
capable C++ programmer. Knowledge related to CAD modeling, Open Cascade, OpenGL or
Direct3D, spline and NURBS surfaces, numerical optimization or Python are also interesting
assets.
Funding: A scholarship is available and will be adjusted to the candidate’s profile.
Start date: Summer / fall 2024, winter 2025
Additional information: While ÉTS is a French speaking engineering school, all of the
courses and the thesis can be done in English.
Montréal is quite bilingual and someone who knows English and very basic French can do all
of their day-to-day activities without any problem.
Contact person:
Eric Paquette
eric.paquette(a)etsmtl.ca
https://profs.etsmtl.ca/epaquette/
--
Eric Paquette, Ph.D., ing. (he/him), Professeur Régulier
http://profs.etsmtl.ca/epaquette/
eric.paquette(a)etsmtl.ca Directeur du programme de maîtrise en TI Département de génie
logiciel et des TI École de technologie supérieure 1100, rue Notre-Dame OuestMontréal,
Québec, Canada, H3C 1K3 Tel. : +1 (514) 396-8587