Neuro-Fuzzy Inference System for Accurate Prediction of Z-Axis Values in Screw Installation

High precision in automated screw installation is crucial for ensuring product quality and operational efficiency in modern manufacturing. A key challenge is accurately predicting the Z-axis value based on screwing depth and torque, which exhibit complex nonlinear relationships. This study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) model to enhance predictive accuracy. Experimental data are collected by varying the mounting depth and torque and then preprocessed through normalization before training the model. The ANFIS model is designed with fuzzy membership functions and trained using a hybrid learning algorithm. The performance evaluation using the root mean squared error (RMSE) has a value of 2.52 × 10^-3, indicating high prediction accuracy. Residual error analysis revealed a near-normal distribution after transformation, with skewness of 0.2672 and kurtosis of 3.1112. Error analysis on extreme Z values revealed a mean residual error of 1.34 × 10^-2 for low Z (< 47.1) and 0.0091247 for high Z (> 47.4), confirming the model’s reliability. The Kruskal-Wallis test further validates ANFIS’s superiority over Support Vector Regression (SVR) and Random Forest Regression (RFR), with an H-value of 115.62 and a p-value of 0.000. The results demonstrate that ANFIS effectively captures the dependencies between input parameters and Z values, achieving minimal deviation from the actual values. This research contributes to intelligent manufacturing by enabling predictive monitoring and adaptive control. Additionally, it aligns with Sustainable Development Goal (SDG) 9 by promoting resilient infrastructure and sustainable industrialization. Future work may explore the integration of real-time sensor feedback or the hybridization of ANFIS with deep learning for enhanced adaptability in dynamic industrial settings.

ANFIS; Mounting depth; Mounting torque; Predictive modelling; Precision assembly

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