Grant: Alliance Grants
University of Ontario Institute of Technology
$29,900.00
AI-based early failure detection in 3D printing for better print quality, less material waste, and shorter trial and error process
FY 2020-21
Summary
3D printing technologies are rapidly evolving. Fused Deposition Modeling (FDM) is one of the most popular types of 3D printers. However, there are still many challenges related to product quality, robustness, and reliability, which hinders the business expansion of 3D printing in the manufacturing industry. Research on the failure detection for the typical FDM machines and the corresponding automatic monitoring methods are required to address these challenges. This research program proposes an AI-based early failure detection system for 3D printing. This research program aims to achieve the following 4 objectives: (1) investigate the failure mechanisms in the 3D printing process to reproduce failures for data collection. (2) design experiment, collect the failure data, carry out correlation analysis, and extract the best features for failure detection. (3) based on the selected features, design the AI-based algorithms to detect failures. (4) implement the early failure detection system in the cloud-based 3D printing management system at Mech Solutions. This proposed project will combine the AI-based early fault detection system developed in Dr. Lin's lab and the commercialization capability of the industrial partner.
The NSERC Alliance Grant program provides an opportunity for us to develop the advanced failure detection methods in the university lab. The industrial partner, Mech Solutions, can benefit from the developed algorithms from Dr. Lin's lab to improve the 3D printing service. The results will significantly benefit the relevant products and services in a wide range of important applications in Canada through direct transfer of knowledge and technology to industry.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
557164-2020
Name nom
Lin, Xianke
Department département
Automotive, Mechanical and Manufacturing Engineering
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
29900
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
2100
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical engineering
Research subject fr
Génie mécanique
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
AI-based early failure detection in 3D printing for better print quality, less material waste, and shorter trial and error process
Keyword
NSERC
Application summary
3D printing technologies are rapidly evolving. Fused Deposition Modeling (FDM) is one of the most popular types of 3D printers. However, there are still many challenges related to product quality, robustness, and reliability, which hinders the business expansion of 3D printing in the manufacturing industry. Research on the failure detection for the typical FDM machines and the corresponding automatic monitoring methods are required to address these challenges. This research program proposes an AI-based early failure detection system for 3D printing. This research program aims to achieve the following 4 objectives: (1) investigate the failure mechanisms in the 3D printing process to reproduce failures for data collection. (2) design experiment, collect the failure data, carry out correlation analysis, and extract the best features for failure detection. (3) based on the selected features, design the AI-based algorithms to detect failures. (4) implement the early failure detection system in the cloud-based 3D printing management system at Mech Solutions. This proposed project will combine the AI-based early fault detection system developed in Dr. Lin's lab and the commercialization capability of the industrial partner.
The NSERC Alliance Grant program provides an opportunity for us to develop the advanced failure detection methods in the university lab. The industrial partner, Mech Solutions, can benefit from the developed algorithms from Dr. Lin's lab to improve the 3D printing service. The results will significantly benefit the relevant products and services in a wide range of important applications in Canada through direct transfer of knowledge and technology to industry.
