CSIRO 
Victoria, Australia Descrizione del progetto
Funding and duration
four year scholarship package totalling approximately $48,000 per annum tax exempt (2026 rate); four-year scholarship and research training program.
Project overview
Project title
Quality enhancements in manufacturing processes to predict and avoid wastage in various products.
Project description
This Project will develop an AI-powered predictive quality management system to address inefficiencies and waste in pipe manufacturing. The expected outcome is an automated, data-driven framework that improves product quality and reduces material wastage. This solution will potentially lead to cost savings, improved sustainability, and enhanced competitiveness in Australian manufacturing.
Supervisory team
University
Name of university supervisor | Ambarish Kulkarni
Name of university | Swinburne University of Technology
Email address | ambarishkulkarni@swin.edu.au
Faculty | Department of Mechanical and Product Design Engineering
CSIRO
Name of CSIRO supervisor | Xun Li
Email address | xun.li@csiro.au
CSIRO Research Unit | Data61
Industry
Name of industry supervisor | Ben Twigg
Name of business/organisation | Steel Mains Pty Ltd
Email address | Ben.Twigg@steelmains.com
Further details
Primary location of student | Swinburne University of Technology, John Street, Hawthorn VIC 3122, Australia
Industry engagement component location | Steel Mains Proprietary Pty Ltd, 125-175 Patullos Lane Somerton VIC 3062, Australia
Other locations | CSIRO Marsfield, 26 Pembroke Road, Marsfield NSW 2122, Australia
Ideal student skillset | Essential skills:Background in engineering, computer science, or data analytics.Knowledge of manufacturing processes and quality control principles.Experience with machine learning and basic AI concepts. Strong problem-solving and critical thinking abilities. Desirable skills:Familiarity with IoT systems, sensors, or vision systems. Experience with real-time data acquisition and analysis. Understanding of statistical process control or predictive maintenance.Good communication skills and ability to work collaboratively in interdisciplinary teams.
Application close date | Open until position filled
Apply | Contact Ambarish Kulkarni