Optimizing Polydimethylsiloxane-Embedded Polymer Optical Fiber Tactile Sensors: A Comprehensive Experimental Analysis of the Sensitive Region and Embedding Substrate Parameters

Abstract

Polymer optical fibers (POFs) are ideal for tactile sensing due to their flexibility, durability, and ease of integration. Optimizing POF-based sensors requires precise control of design parameters, such as polydimethylsiloxane (PDMS) mixing ratio, sensitive region length (SRL), sensitive region depth (SRD), Sanding Grit Size (SGS), and embedding depth. These parameters significantly affect linearity, sensitivity, and hysteresis, which are crucial for accurate tactile measurements. This study presents a comprehensive analysis of the interplay between these design parameters and their impact on sensor performance. A sensing pad was fabricated for each set of experiments by embedding POF in PDMS, with a cutout-sensitive region produced using the Targeted Precision Mold-Guided Abrasive Profiling fabrication method for accurate and reproducible sensitive region parameters. In the presence of applied force, surface scattering loss increases, significantly amplifying the sensor’s output signal. Experimental results show strong correlations between design parameters and performance metrics modeled with first- or second-order polynomials. Using the NSGA-II multiobjective optimization algorithm, the exact optimal parameters were determined as a PDMS mixing ratio of 100:10, SRL of 15 mm, SRD of 0.66 mm, SGS of 320 Cw, and an embedding depth of 4.34 mm. This optimized configuration achieved high linearity (R² ≈ 0.9938), high sensitivity (78.25 mV/N), and low hysteresis (1%) across the force range (0–26 N). Performance remained stable within the optimal range (0–16 N), with slight degradation at higher forces due to strain saturation. While strain saturation limits performance beyond 16 N, this range suffices for most tactile sensing applications. This work provides valuable insights for optimizing tactile sensors with applications in robotics, healthcare, and industrial automation.