The focus of the A*STAR Aerospace Programme (AP) is to find technological solutions for its Members. The solutions enhance AP Members’ ability to develop better products and services.

Due to the diverse interests of the Members, the scope of the research undertaken by AP are multi-faceted and broad. AP leverages on the wide spectrum of capabilities in the A*STAR Science and Engineering Research Council (SERC) Research Institutes. Synergy is achieved through collaboration amongst Members, integrating Members’ needs and leveraging on the combined expertise and experiences of consortium Members and A*STAR. 

To date, some key research focus areas include:

DEVELOPMENT OF COMPOSITE MATERIALS AND NDI TECHNIQUES
DEVELOPMENT OF NEW MATERIALS AND COATINGS
DEVELOPMENT OF ELECTRONICS AND SENSORS
MEASUREMENT AND INSPECTION TECHNOLOGIES
MODELLING OF MATERIALS, SYSTEMS AND PROCESSES
DATA ANALYTICS
COMMUNICATIONS AND WIRELESS NETWORK DEVELOPMENT
ADDITIVE MANUFACTURING AND REPAIR
The number of projects initiated continue to grow over the years. Projects have consistently been completed successfully and on schedule. Examples of past research projects undertaken include:
USE OF LASER FOR COMPOSITE MATERIAL REMOVAL

High-power pulsed lasers have been demonstrated to successfully remove Carbon Fiber Reinforced Plastic (CFRP) composite materials in desired patterns. This technique can be automated to improve CFRP repair processes.

DIRECT-WRITE PIEZOELECTRIC SENSORS FOR STRUCTURAL HEALTH MONITORING

Piezoelectric acoustic sensors and transducers were developed for structural health monitoring in aeronautical applications. These sensors are direct-write, and may be integrated directly into aircraft structures.

LIFETIME PREDICTION FOR ELECTRONICS IN HIGH TEMPERATURE AEROSPACE APPLICATIONS

A methodology was developed to predict the reliability of electronics modules operating under high temperatures. This will help OEMs push the envelope to meet demands for aerospace electronics to operate at higher temperatures.

ADVANCED TECHNIQUES FOR HANDLING IMBALANCED AND UNLABELLED DATA FOR CLASSIFICATION

A novel machine learning algorithm was developed to handle imbalanced and unlabelled data for more accurate classification. The solution enabled development of enhanced prognostics to improve preventive maintenance.

CHARACTERIZATION AND OPTIMIZATION OF WIRELESS NETWORKS ONBOARD COMMERCIAL PASSENGER AIRCRAFT

A tool was developed for the characterization and optimization of wireless networks for integrated in-flight entertainment and communications. The tool will allow OEMs to enhance the performance of wireless networks onboard commercial passenger aircraft.