Advanced composites manufacturing

High value composites manufacturing is a new capability brought to the centre by Prof Peter Schubel. The research activities focus on automated manufacturing, process development, advanced infusion processing and process modelling for the aerospace, defence and civil engineering sectors.

Core Projects

  1. DST-Next Generation Out-Of-Autoclave Composites Repair 2018-2022
  2. CRC-P – Advanced Composite Pultrusion Manufacturing for Next Generation Civil Structures 2019-2022
  3. AOARD – Self-learning Process Modelling in Composites Manufacturing 2020-2023
  4. Boeing Aerostructures Australia – Faster cycle time in resin infusion process 2020-2023
  5. DST – Tailored Composite Scarf Repair with Additive Manufacturing 2019-2020 
  6. Advanced Queensland – Manufacturing graphene nanofiber sensor integrated in composite-pultruded for infrastructure inspection 2020-2023

Core Research Areas


This program is focused on holistic engineering approach to optimise the existing composite pultrusion process for increased productivity and development of novel value-added products such as, fire retardant civil structures to be used in rail and pedestrian bridges and most notably for Australian Inland Rail project. These high strength advance structures will capture new high value markets such as oil & gas and transport industry in national and international markets such as the US and Europe.

Filament winding

We develop composite fuel tanks for low cost transport. The project will demonstrate the critical technologies in manufacturing linerless, filament wound composite tanks. The project will manufacture linerless composite fuel tanks up to two meters in diameter.

Smart composites

 This program develops new multifunctional pultruded composite structures using novel graphene nanofibrous sensor for smart health monitoring of infrastructures. Through incorporation of piezo-resistive graphene nanofibers and integration of experimental, modelling and process optimisation tools, the pultrusion process will help to increase productivity and create multifunctional innovative structures with the ability to carry electrical current to monitor durability and impact loading in infrastructures. 

Composites Repair

The objectives of the research project are to:
• Developing a novel experimental and numerical approach to characterise the out-of-autoclave repair process
• Understanding the link between the process and the repair defects
• Integrating this understanding into a practical repair process control solution

AM Metal/Composite scarf bonding

 This proposal aims to further adhesively bonded composite repairs with additive manufactured (AM) metal, building on novel repair technologies emerged from Stage-1 UK-Australia collaboration. AM cellular lattice structures offer tailorable mechanical and thermal properties. Stage-1 demonstrated the peeling shear stress concentration can be completely eliminated at the bond line of composite-AM metal, against 140-160% stress concentrations in composite-composite bonding. Composite repair with AM metal provides the enhanced performance, light-weighting and affordable cost of sustainment for military platforms. The consortium has the capabilities to mature the patent filed composite-AM metal repair technology, with access to fast exploitation routes.

Applied artificial intelligence

 Aerospace composite manufacturing faces challenges in high production cost and skilled labour shortage. Machine learning technology plays a critical part in next-generation automation for composite manufacturing. This project aims to establish a framework of machine learning applications in both autoclave and out-of-autoclave manufacturing processes for aerospace composites. The study focuses on machine learning for manufacturing induced defect control, through the big data generated from physical processes and virtual simulations. Coupled with the numerical process modelling, the data of pressure mapping, cure profile and laminate layup qualities will be acquired by using high temperature pressure matt, dielectric analyser and machine vision. By harnessing the big data, the machine learning algorithms will proactively adapt appropriate process parameters to minimise the predicted defect formations. The proposed study will advance the application of artificial intelligence in aerospace composite manufacturing.

Resin infusion

The Project will address the safety and technical risks applicable to infusion of hot exothermic resins, and prevent thermal runaways that leads to charred resin,  rejected parts, release of toxic fumes and risk of fire. Novel solutions will be substantiated by simulations and experimental trials to enable a reduction in the cycle of resin infusion based on controlling and accelerating the transition between process steps.
The research questions to be addressed and systematic research to be conducted will focus on how, through use of simulation, sensor networks and empirical models.