Grant: Alliance Grants
York University
$75,647.00
Machine learning aided accelerated design and characterization of automotive composites
FY 2023-24
Summary
Recent environmental issues related to global climate change and greenhouse gas emissions have prompted automotive manufacturers to focus on the development of lightweight and fuel-efficient vehicles. Typical steels and Aluminum (Al) alloys are increasingly being replaced by automotive composites driven by such light-weighting trends. The mechanical properties (strength, creep, fatigue) of legacy materials such as steel and Al alloys are well-understood and predictable within the industry. However, the use of composite materials within the automotive design process introduces two significant challenges. The first is that composites, because of their heterogenous nature, introduce a broad expanse of compositional design space. The second is that polymers, because of their viscous nature, are highly sensitive to processing, environmental, and testing conditions. These two challenges combined brings about endless possibilities of structure-property-process relationships that could require an exhaustive testing program and hence prolonged design cycles. As part of a broader effort to shorten the design cycle, this project will seek to characterize the static strength and creep properties of certain automotive composites used within a major automotive parts manufacturer and analyze these within the broader context of supplier provided and other available composite materials data. The combined data from various sources, will be used to train an optimal machine learning/artificial-intelligence algorithm capable of predicting the mechanical behavior for a broad range of compositions, processing, environmental and testing conditions. The outcome of this work will benefit automotive manufacturers in Canada with improving the quality and reliability of next generation automotive parts, thereby increasing business value and retaining and creating more jobs. Furthermore, the use and further proliferation of such lightweight composite automotive parts will provide broader societal benefits to Canadians and the environment through reduced greenhouse gas emissions from the transportation industry.
Program
Alliance Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
585971-2023
Name nom
Rizvi, Reza R
Department département
Mechanical Engineering
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
75647
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1701
Research subject group en
Design and manufacturing
Research subject group fr
Engineering design
Research subject en
Conception et fabrication
Research subject fr
Conception technique
Application title
Machine learning aided accelerated design and characterization of automotive composites
Keyword
Creep; Machine Learning; Open Data; Polymer Composites; Stress-Strain
Application summary
Recent environmental issues related to global climate change and greenhouse gas emissions have prompted automotive manufacturers to focus on the development of lightweight and fuel-efficient vehicles. Typical steels and Aluminum (Al) alloys are increasingly being replaced by automotive composites driven by such light-weighting trends. The mechanical properties (strength, creep, fatigue) of legacy materials such as steel and Al alloys are well-understood and predictable within the industry. However, the use of composite materials within the automotive design process introduces two significant challenges. The first is that composites, because of their heterogenous nature, introduce a broad expanse of compositional design space. The second is that polymers, because of their viscous nature, are highly sensitive to processing, environmental, and testing conditions. These two challenges combined brings about endless possibilities of structure-property-process relationships that could require an exhaustive testing program and hence prolonged design cycles. As part of a broader effort to shorten the design cycle, this project will seek to characterize the static strength and creep properties of certain automotive composites used within a major automotive parts manufacturer and analyze these within the broader context of supplier provided and other available composite materials data. The combined data from various sources, will be used to train an optimal machine learning/artificial-intelligence algorithm capable of predicting the mechanical behavior for a broad range of compositions, processing, environmental and testing conditions. The outcome of this work will benefit automotive manufacturers in Canada with improving the quality and reliability of next generation automotive parts, thereby increasing business value and retaining and creating more jobs. Furthermore, the use and further proliferation of such lightweight composite automotive parts will provide broader societal benefits to Canadians and the environment through reduced greenhouse gas emissions from the transportation industry.
