Grant: CCI Applied Research and Development Grants
Red River College Polytechnic
$39,996.00
Data-driven Toolpaths for Microstructure Control in Laser Powder Bed Fusion Additive Manufacturing - OptiFab Technologies Inc.
FY 2024-25
Summary
Additive Manufacturing (AM) has revolutionized the manufacturing industry in several directions. Laser powder bed fusion (LPBF) is one of the powder bed-based AM technologies that offers tremendous potential for fabricating complex metal parts with high precision and efficiency. However, a key obstacle hindering the widespread adoption of LPBF in industry is the difficulty in precisely controlling microstructure formation during the printing process. This challenge can result in inconsistencies in material properties and performance, thereby impacting the quality and reliability of printed components. To address this challenge, innovative solutions are needed to understand and predict the complex interactions between process parameters, microstructure evolution, and material properties in LPBF. The rise of digital technologies such as artificial intelligence (AI) and machine learning (ML) offers opportunities to tackle these challenges. By harnessing advanced computational modeling, data analytics, and materials science techniques, researchers and industry experts can devise strategies to forecast and enhance LPBF processes, thereby improving the quality and properties of AM parts.
The Technology Access Centre for Aerospace and Manufacturing (TACAM) at Red River College Polytechnic (RRC Polytech) plans to collaborate with the OptiFab Technologies Inc., which specializes in providing cutting-edge solutions in metal AM, particularly in the field of LPBF. The goal is to create and verify computational algorithms that predict process variables, such as toolpath strategies, ensuring parts meet desired properties and geometrical accuracy. The project aims to develop data-driven toolpaths for microstructure control in LPBF-built materials. By leveraging advanced data analytics techniques, the goal is to optimize the printing process parameters and scan patterns to achieve desired microstructures and material properties.
This project is expected to yield both economic and industrial benefits through innovative research. The TACAM team will leverage its existing capabilities and expertise in design, additive manufacturing, advanced metrology, and inspection techniques to deliver the project. OptiFab is a progressive company in Canada with a high-priority commitment to research and development activities. The company has the capacity to implement and further advance the results of this project through the commercialization of the digital solution developed.
Program
CCI Applied Research and Development Grants
Recipient
Location
Manitoba, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
CCARD-2024-00175
Name nom
Galaugher, Jolen
Department département
Research Partnerships and Innovation - Red River College Polytechnic
Organization id
102486
Institution établissement
Red River College Polytechnic
Province en
Manitoba
Province fr
Manitoba
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2024
Award amount
39996
Program id
CCARD
Program name en
CCI Applied Research and Development Grants
Program name fr
ICC Subventions de recherche et développement appliquée
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
nan
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Field of research list codes
RDF2040306
Field of research list names en
Manufacturing processes and technologies (except textiles)
Field of research list names fr
Procédés et technologies de fabrication (sauf pour les textiles)
Socioeconomic objective list codes
RDS10699
Socioeconomic objective list names en
Industrial production and technology, n.e.c.
Socioeconomic objective list names fr
Production et technologie industrielles, n.c.a.
Application title
Data-driven Toolpaths for Microstructure Control in Laser Powder Bed Fusion Additive Manufacturing - OptiFab Technologies Inc.
Keywords
Additive Manufacturing; Laser Powder Bed Fusion ; Artificial Intelligence; Machine Learning; Non-destructive X-ray CT inspection
Application summary
Additive Manufacturing (AM) has revolutionized the manufacturing industry in several directions. Laser powder bed fusion (LPBF) is one of the powder bed-based AM technologies that offers tremendous potential for fabricating complex metal parts with high precision and efficiency. However, a key obstacle hindering the widespread adoption of LPBF in industry is the difficulty in precisely controlling microstructure formation during the printing process. This challenge can result in inconsistencies in material properties and performance, thereby impacting the quality and reliability of printed components. To address this challenge, innovative solutions are needed to understand and predict the complex interactions between process parameters, microstructure evolution, and material properties in LPBF. The rise of digital technologies such as artificial intelligence (AI) and machine learning (ML) offers opportunities to tackle these challenges. By harnessing advanced computational modeling, data analytics, and materials science techniques, researchers and industry experts can devise strategies to forecast and enhance LPBF processes, thereby improving the quality and properties of AM parts.
The Technology Access Centre for Aerospace and Manufacturing (TACAM) at Red River College Polytechnic (RRC Polytech) plans to collaborate with the OptiFab Technologies Inc., which specializes in providing cutting-edge solutions in metal AM, particularly in the field of LPBF. The goal is to create and verify computational algorithms that predict process variables, such as toolpath strategies, ensuring parts meet desired properties and geometrical accuracy. The project aims to develop data-driven toolpaths for microstructure control in LPBF-built materials. By leveraging advanced data analytics techniques, the goal is to optimize the printing process parameters and scan patterns to achieve desired microstructures and material properties.
This project is expected to yield both economic and industrial benefits through innovative research. The TACAM team will leverage its existing capabilities and expertise in design, additive manufacturing, advanced metrology, and inspection techniques to deliver the project. OptiFab is a progressive company in Canada with a high-priority commitment to research and development activities. The company has the capacity to implement and further advance the results of this project through the commercialization of the digital solution developed.