Jingchao Jiang

Lecturer

University of Exeter

United Kingdom

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Dr. Jingchao Jiang is a Lecturer at the University of Exeter in the UK. His research primarily focuses on additive manufacturing, smart materials, and machine learning applications in manufacturing processes. Recent publications include contributions to the development of recycled materials, energy absorption structures, and advanced design optimization techniques for 3D printing technologies.

Jingchao Jiang

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About

Articles (19)

Review on Recycled Materials for Additive Manufacturing

Oxygen Vacancy‐Driven Lattice Modulation in Zn2P2O7: A Novel Anode Enabling Accelerated Kinetics and Long Cycling Stability for Sodium‐Ion Batteries

Stress-guided lightweight design and optimisation for 3D printing sacrificial moulds

INPR-Connector: Interlocking negative Poisson’s ratio connectors design for deployable energy absorption structures

Towards label-free defect detection in additive manufacturing via dual-classifier semi-supervised learning for vision-language models

Low-melting-point alloy/polyurethane auxetic composite foam for outstanding impact protection with favorable shape memory effect

Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives

Data-Driven and Physics-Assisted Machine Learning Approach for Warpage Classification and Process Parameter Optimization in a 3-D-Printed BeltClip

Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes

Topology Optimization of Self-supporting Structures for Additive Manufacturing via Implicit B-spline Representations

Unlocking the potential of low-melting-point alloys integrated extrusion additive manufacturing: insights into mechanical behavior, energy absorption, and electrical conductivity

Big data, machine learning, and digital twin assisted additive manufacturing: A review

Nature-inspired interlaced printing strategies for additive manufacturing highly improved mechanical properties

High energy dissipation and self-healing auxetic foam by integrating shear thickening gel

Machine learning enabled 3D printing parameter settings for desired mechanical properties

Unveiling the structure-activity relationship of hollow spindle-like α-Fe2O3 nanoparticles via phosphorus doping engineering for enhanced lithium storage

Design for reversed additive manufacturing low-melting-point alloys

3D printing in materials manufacturing industry: A realm of Industry 4.0

Special issue on machine learning in additive manufacturing

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