Inverse input optimization for tuning two-ply yarn processing parameters using feedforward neural network

Abstract

Abstract

In this study, inverse input optimization (IIO) was employed as a simple, efficient, and novel approach for tuning industrial processes, specifically for optimizing two-ply cotton yarn parameters using a feedforward artificial neural network (ANN). Initially, the ANN model was trained using experimental data to capture the nonlinear relationship between yarn process parameters and tenacity. After training, all network weights and biases were fixed, and the embedded knowledge of the ANN was exploited to guide optimization. Using the IIO framework, input parameters were iteratively adjusted within the normalized design space based on the deviation between the predicted output and a predefined target. The adjustments were guided by a numerical gradient estimation, combined with an adaptive update rate and momentum term to balance exploration and exploitation. Sensitivity analysis using the Garson weight method (GWM) indicated that the twist direction of the two-ply yarn is the most influential factor, contributing 32.61% to the predicted tenacity. Applying IIO, the optimal yarn parameters were identified as first- and second-ply twists of 1000 TPM, a two-ply twist of 790 TPM, and a Z-twist direction, increasing yarn tenacity from 28.72 cN/tex for the initial sample to 35.62 cN/tex for the optimized sample. While the Genetic Algorithm (GA) achieved a minimum cost value similar to that obtained by IIO (approximately 3.8 × 10⁻⁵), IIO required only 271 function evaluations compared to 1020 for GA, demonstrating significantly higher computational efficiency. These findings establish IIO as a simple, low-cost, and competitive optimization strategy for industrial processing.