Optimization of conductive PETG-based composite for strain-sensing prosthetic sockets using 3D printing

This work focuses on optimizing the mechanical and electromechanical properties of a 3D-printed strain-sensing prosthetic socket fabricated using a conductive PETG-based composite (P15LT35 G) via Fused Deposition Modelling (FDM). A full factorial design (3 3 ) was implemented to study the influence of nozzle diameter (0.4, 0.5, 0.6 mm), infill pattern (grid, lines, gyroid), and infill density (30%, 50%, 70%) on performance characteristics. The socket was evaluated through tensile, flexural, impact, and compression testing, with the best configuration (0.4 mm nozzle, grid pattern, 70% infill) yielding a peak compressive strength of 25.62 MPa and a flexural strength of 31.72 MPa. Electrical characterization revealed that the optimized sensor exhibited a gauge factor of 1.52 with a nonlinearity coefficient of 4.01 under cyclic loading, indicating stable piezoresistive behavior. The low sensitivity, denoting controlled resistance changes under uniform loading, enhances signal reliability for limb-socket interface monitoring. ANOVA and Response Surface Methodology (RSM) were used to develop predictive models with high significance ( p < .05) and adjusted R 2 values exceeding 95%. The final prosthetic socket design includes embedded sensor paths and ventilation zones, validated through load simulations and experimental tests. The study demonstrates a robust approach for integrating sensing capability within structural components, offering a scalable solution for smart prosthetic systems.

Authors

Institutions

Publication Details

Journal
Journal of Thermoplastic Composite Materials
Published
2025-08-29
DOI
https://doi.org/10.1177/08927057251371595
Citations
2
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
1.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of conductive PETG-based composite for strain-sensing prosthetic sockets using 3D printing

Aman Kumar, Pushpendra Yadav, S.K. Sharma, Lakshya Bhardwaj et al.
2 citations
Journal of Thermoplastic Composite Materials
Additive Manufacturing and 3D Printing Technologies
1.37
article

Optimization of conductive PETG-based composite for strain-sensing prosthetic sockets using 3D printing

Aman Kumar, Pushpendra Yadav, S.K. Sharma, Lakshya Bhardwaj, Ankit Sahai, Abhishek Raj
article en
2 citations

Abstract

This work focuses on optimizing the mechanical and electromechanical properties of a 3D-printed strain-sensing prosthetic socket fabricated using a conductive PETG-based composite (P15LT35 G) via Fused Deposition Modelling (FDM). A full factorial design (3 3 ) was implemented to study the influence of nozzle diameter (0.4, 0.5, 0.6 mm), infill pattern (grid, lines, gyroid), and infill density (30%, 50%, 70%) on performance characteristics. The socket was evaluated through tensile, flexural, impact, and compression testing, with the best configuration (0.4 mm nozzle, grid pattern, 70% infill) yielding a peak compressive strength of 25.62 MPa and a flexural strength of 31.72 MPa. Electrical characterization revealed that the optimized sensor exhibited a gauge factor of 1.52 with a nonlinearity coefficient of 4.01 under cyclic loading, indicating stable piezoresistive behavior. The low sensitivity, denoting controlled resistance changes under uniform loading, enhances signal reliability for limb-socket interface monitoring. ANOVA and Response Surface Methodology (RSM) were used to develop predictive models with high significance ( p < .05) and adjusted R 2 values exceeding 95%. The final prosthetic socket design includes embedded sensor paths and ventilation zones, validated through load simulations and experimental tests. The study demonstrates a robust approach for integrating sensing capability within structural components, offering a scalable solution for smart prosthetic systems.

Journal of Thermoplastic Composite MaterialsVol. 39(4)
M.J.P. Rohilkhand University (IN), Dayalbagh Educational Institute (IN), Bard College (US), Indian Institute of Petroleum (IN)
Openalex Percentile: Top 17%
Additive Manufacturing and 3D Printing Technologies
1.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.