Predictive Reinforcement and Mechanical Characterization of Copper Wire Embedded Polymer Composites Toward Advanced Multifunctional Applications
Source abstract
Abstract
ABSTRACT Polymer‐based material extrusion limits to provide the mechanical robustness and unit‐level reinforcement control needed for advanced structural or multifunctional applications. To address this limitation, this work introduces a programmable continuous copper‐wire embedding strategy that enables spatially controlled reinforcement within PLA using a standard extrusion printhead. A process‐derived embedding guideline is established to ensure stable wire deposition preventing drift, breakage, and interfacial defects, thereby enabling reliable fabrication of architected metal‐polymer composites. To generalize reinforcement behavior, a dimensionless Effective Reinforcement Index (ERI) is formulated, integrating metal volume share, modulus mismatch, and geometric stress factors into a predictive design descriptor. ERI correlates strongly with tensile and flexural strength ( R 2 = 0.81 and 0.91), providing a physics‐grounded rule for reinforcement targeting. Mechanical performance is systematically evaluated across wire diameters (0.1–0.3 mm) and hatch spacings using tensile, flexural, and ILSS tests, supported by SEM and X‐ray microscopy of fracture modes and embedding quality. The embedded copper wires yield significant improvements, up to 107% in tensile strength, 203% in modulus, 65% in flexural strength, 285% in flexural modulus, and 36% in ILSS relative to PLA. This study establishes a design‐driven platform for continuous metal‐wire embedding reinforcement in polymer, enabling programmable strengthening and predictive performance guidance for advanced functional composites.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Aluminum Alloys Composites Properties · Advanced Welding Techniques Analysis · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering Xi'an Jiaotong University Xian Shaanxi China
School of Automation and Intelligent Manufacturing (AIM) Southern University of Science and Technology Shenzhen People's Republic of China
Funding
National Key Research and Development Program of China · Fundamental Research Funds for the Central Universities
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