Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing

Kingsley Yeboah Gyabaah, Bernard Mahoney, Anthony Kwasi Martey, Cheng Yan, Patrick F. Mensah, Guoqiang Li

AI Materials 2026 article cited by 13

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Machine Learning in Materials Science additive manufacturing stage: research

Problem

Predicting the final properties of 3D-printed polymer and polymer composite parts is difficult because material composition, processing conditions and microstructure vary.

Why it matters

Printed polymer parts serve customized, lightweight applications across industries, where unpredictable properties limit reliability.

What the AI did

A review of ML approaches (supervised, unsupervised, semi-supervised, self-supervised, reinforcement learning) for predicting mechanical, thermal and electrical properties, plus hybrid ML with physics-informed modeling and digital twins for process control.

Results

not stated in source

Impact

Evidence supports only a synthesis of existing ML applications; no measured impact is stated.

Record

Venue	AI Materials
Published	2026
Work type	article
Authors	Kingsley Yeboah Gyabaah, Bernard Mahoney, Anthony Kwasi Martey, Cheng Yan, Patrick F. Mensah, Guoqiang Li
Citations	13 (OpenAlex, at import time)
References	330
Model or method	Various ML approaches; no specific named architecture
Organization	not stated in source
Primary source	https://doi.org/10.3390/aimater1010002
Open access	diamond
Keywords	machine learning, additive manufacturing, polymer composites, property prediction, digital twins

Importance rating

4 / 10

real but narrower advance

Review of ML for predicting properties of 3D-printed polymer parts across industries; broad relevance but only a synthesis with no measured impact stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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