Real-time quality assurance and machine validation for ultra precision manufacturing
Project overview
Project title
Real-time quality assurance and machine validation for ultra precision manufacturing
Project description
This project is developing a real-time quality assurance system to detect precision assembly errors and validate CNC machine performance using quantitative data. The expected outcome is an intelligent, data-driven quality control method that improves the accuracy and reliability of ultra-precision manufacturing. This technology will potentially result in improved product traceability, reduced defects, and increased compliance with medical and military standard for the advanced manufacturing industry.
Supervisory team
University
| Name of university supervisor | Prof. Songlin Ding |
| Name of university | Royal Melbourne Institute of Technology |
| Email address | songlin.ding@rmit.edu.au |
| Faculty | Engineering |
CSIRO
| Name of CSIRO supervisor | Dr. Dayalan Gunasegaram |
| Email address | Dayalan.Gunasegaram@csiro.au |
| CSIRO Research Unit | Manufacturing |
Industry
| Name of industry supervisor | Vijay Vysakumar |
| Name of business/organisation | ANCA Pty Ltd. |
| Email address | vijay.vysakumar@anca.com |
Further details
| Primary location of student | Royal Melbourne Institute of Technology, 124 La Trobe Street, Melbourne VIC 3000, Australia |
| Industry engagement component location | ANCA Pty Ltd, 25 Gatwick Rd, Bayswater North, Victoria, 3153, Australia |
| Other locations | CSIRO Clayton, Research Way, Clayton VIC 3168, Australia |
| Ideal student skillset | Essential Skills: Degree in Mechanical, Mechatronics, Electrical, or Manufacturing Engineering (or related field) Strong understanding of sensors, control systems, or metrology Proficiency in data analysis and signal processing Experience with MATLAB, Python, or similar software tools. Good exposure to high precision scientific instruments such as SEM, spectrometry and experimental imaging. Strong problem-solving and critical thinking skills Desirable Skills: Knowledge of CNC machines or precision manufacturing processes Experience with robotic systems or automation Familiarity with sensor fusion or machine learning Good written and verbal communication skills Ability to work independently and collaboratively in industry-academic environments |
| Application close date | Open until position filled |
| Apply | Contact Prof. Songlin Ding |