Abstract:
The dimensional shrinkage of knitted fabrics has been extensively investigated; however, existing predictive models remain limited in their ability to accurately estimate shrinkage solely from fabric properties. This study reports on the development of an Artificial Neural Network (ANN) model specifically designed to predict the dimensional shrinkage of single-jersey knitted fabrics composed of 100% cotton and polyester–elastane blends.
The model integrates parameters from the knitting, pre-setting, and finishing stages, thereby providing a comprehensive framework for prediction. The training dataset was systematically compiled through controlled experimental trials on a range of knitted fabric samples, ensuring consistency and reliability of input variables.
The model was trained using twenty-three input variables, including yarn count, loop shape factor, tightness factor, stitch density, course density, wale density, machine settings, and areal density. These inputs were chosen based on their known influence on shrinkage, as identified in previous literature and empirical observations. The ANN model was trained on experimental data and validated using samples not used for testing, demonstrating high prediction accuracy and a strong correlation between actual and predicted shrinkage values. The ANN was built using TensorFlow-Keras with a feed- forward backpropagation architecture, and its performance was evaluated using statistical measures, including correlation coefficients between the observed and predicted values, mean square error, mean absolute error, and mean absolute percentage error.
This study demonstrates the superiority of ANN over conventional predictive models in both accuracy and scalability. Once trained, the ANN model can rapidly estimate fabric shrinkage using known input parameters, enabling proactive quality control at the production planning stage. This approach reduces reliance on physical sampling and post- compacting shrinkage testing, conserving time and material resources. The results establish ANN as a robust and practical solution to the persistent challenge of predicting shrinkage in knitted fabrics. By integrating machine learning with empirical textile knowledge, the textile industry can advance toward predictive manufacturing, improved productivity, and enhanced product performance. Furthermore, the proposed framework can be extended to incorporate parameters such as finishing and thermal treatments, and to forecast shrinkage in other knitted structures, including rib and interlock. Future research may also explore hybrid models combining ANN with fuzzy logic or genetic algorithms to strengthen predictive capability.
