Advancing Intelligent Manufacturing: Medical Innovation, 3D Printing, and Custom Automation at CIM
The Centre for Intelligent Manufacturing (CIM), which works with industry partners to overcome manufacturing difficulties, is led by Dr. Carolyn Moorlag. In order to improve product design and production, CIM focuses on advanced analytics, robots, artificial intelligence (AI), and 3D printing. Small and medium-sized businesses (SMEs) in Halton, Peel Region, and the northwest Greater Toronto Area (GTA) are the center’s main clientele. By assisting them in resolving design issues and de-risking prototypes, the center helps them save a lot of money and time.
Customized 3D printing and automation
Custom automation, which includes 3D printing customized parts like grippers and guides for robotic fixtures in automated manufacturing, is a major research focus at CIM. To provide automation solutions that improve industrial manufacturing, CIM collaborates with businesses like Rex Power Magnetics in Toronto and A. Berger Precision in Brampton. These automated procedures are increasingly using AI, which increases their precision and flexibility. Additionally, CIM uses its proficiency in 3D printing and prototyping to show that custom industrial parts are feasible. Businesses can cut waste, avoid costly manufacturing setups, and lessen their need on imported parts by creating 3D-printed models for direct field usage. For instance, CIM is investigating 3D-printed lighting fixtures, including creative robotic-integrated designs, in partnership with Mississauga-based HCI Lighting.
Devices in Medicine and Wearable Technology
The medical industry can benefit from CIM’s expertise, not only in 3D printing and custom prototyping, but also making use of robotics capabilities. The ability to print high strength medical implant components, such “bone” core and the softer surrounding “tissue” enables the ability to conduct projects in the area of medical implants and testing, and particularly to realistically test medical products such as implants. Leveraging an in-lab industrial robot, CIM is working with medical devices company ProsFit to test a 3D printed lower leg implant via robotic gait simulation in order to create a digital twin for custom fitting and improved implant acceptance. CIM is also engaged in the development of medical wearables, an area of research which can benefit the lives of many underserved groups in our communities. Wearable devices for medical treatment and testing must be flexible, comfortable, biocompatible, and durable, as well as meeting the design requirements for new applications. In collaboration with Burlington-based Level Motion, CIM is working to design and print components for wearable sensing and tracking during physical therapy. The system would allow the medical practitioner to help the patient reach better outcomes remotely and outside of scheduled rehabilitation treatments. Multiple medical devices projects take advantage of the customize printing available in the CIM lab for customized components combined with electronics, sensor and software integration, and the recent inclusion of AI.
AI-Powered Visual Inspection System
One of CIM’s notable initiatives is the creation of a visual inspection system powered by AI as in below figure, intended to transform quality control processes within manufacturing. The objective of this project is to develop an AI system capable of conducting automated visual assessments of machined components with remarkable precision. Specifically, the system is designed to identify defects as small as 50 microns with a 95% accuracy rate. It will differentiate between acceptable and defective parts, categorize the defects, and function autonomously, enhancing the overall quality and accuracy of the final output. The project is structured around four key objectives are to :
- Design an image collection system including initial designs of various components, mechanical design, electrical design, and software design.
- Preprocess and analyse the image data sets and create datasets to train/test the AI.
- Apply an algorithm to identify and categorize defects with a high level of accuracy.
- Test and evaluate the proposed system by Test a new part “reality check”
