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Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufac

Lead Research Organisation: Edge Hill University
Department Name: Computer Science

Abstract

The proposed project aims to collaboratively integrate modern data system, experiments, imaging, machine learning and predictive engineering-physical modelling for additive manufacturing (AM) and materials developments. Through focused knowledge transfer, close interdisciplinary teamwork and fusion of the academic-industrial research/resource, the team will jointly establish a systematic data system of the structure, properties, defects and distortions in AM of a range of materials at different scales and use the data for materials development and AM process optimisation. The effect of AM processing and surface treatments on the surface integrity and functional properties (e.g. corrosion resistance) of AM materials is to be systematically established. The project will develop practical imaging and processing algorithms for the analysis, design, and joint quality control for the input materials in AM, including powder production. Engineering and key physical modelling is to be integrated with machine learning for predictive composition and structure design for optimum synergy between printability, properties and performances. Materials development balancing printability and structure properties will be focused on advanced materials requiring critical phase control in AM, including duplex
stainless steels, amorphous glass metals and Mg. The advanced data and materials will serve as a pivoting platform for future research and innovation in AM, speeding up material development within the full product development life cycle. Through focused intersectoral and international knowledge exchange and joint R&I within a multidisciplinary team, the project will contribute to the continuous practical applications of Industry 4.0 technologies and development for industry5.0 in AM, further enhancing the design freedom in composition and structure for application-specific products, and accelerating the researcher development with lasting impact in the EU and beyond.

Publications

10 25 50
 
Title metallic materials defects images 
Description The dataset includes 742 images about the defects of three metallic materials: AlSi10Mg, super duplex stainless steel (SDSS) 2507 and ZK60Mg, 
Type Of Material Database/Collection of data 
Year Produced 2025 
Provided To Others? Yes  
Impact It would facilitate the research and development of novel models and methods for the detection and classification of defects of materials in their design and manufacturing process. 
URL https://github.com/EHU-IVCRC22/metallic-materials-defects-images
 
Description Departmental seminar 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact Introduce the topics about materials defects, detection, feature extraction, data capture, activities taken and experience.
various questions were asked and discussed, new research directions were identified,
Year(s) Of Engagement Activity 2025
 
Description Invited Speaker 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact To give a talk in the 7th International Conference on Video, Signal and Image Processing (VSIP 2025)
Year(s) Of Engagement Activity 2025
URL https://www.vsip.net/
 
Description PhD recruitment 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Postgraduate students
Results and Impact PhD study application
Year(s) Of Engagement Activity 2024,2025