Advanced Sensitive Feature Machine Learning for Aesthetic Evaluation Prediction of Industrial Products

TL;DR

This paper introduces a machine learning framework to predict the aesthetic evaluation of industrial products, using automotive design as a case study, by combining subjective and objective weighting methods and a novel predictive model.

Contribution

The novel ILPOBP model and integration of subjective/objective weights with sensitivity analysis advance aesthetic evaluation prediction in product design.

Findings

The ILPOBP model achieves a test set mean absolute relative error of 4.106%, outperforming baseline models.

Six key aesthetic indicators were identified through sensitivity analysis, forming a high-quality dataset.

SHAP explanations enhance model interpretability, providing insights for product optimization.

Abstract

As product aesthetics increasingly drive consumer preference, quantitative evaluation remains hindered by subjective evaluation biases and the black-box nature of modern artificial intelligence. This study proposes an advanced machine learning framework incorporating sensitivity-aware morphological features for the aesthetic evaluation of industrial products, with automotive design as a representative case. An aesthetic index system and its quantitative formulations are first developed to capture the morphological characteristics of product form. Subjective weights are determined via grey relational analysis (GRA), while objective weights are calculated using the coefficient of variation method (CVM) integrated with the technique for order preference by similarity to an ideal solution (TOPSIS). A game-theoretic weighting approach is then employed to fuse subjective and objective weights, thereby establishing a multi-scale aesthetic evaluation system. Sensitivity analysis is applied to identify six key indicators, forming a high-quality dataset. To enhance prediction performance, a novel model—improved lung performance-based optimization with backpropagation neural network (ILPOBP)—is proposed, where the optimization process leverages a maximin latin hypercube design (MLHD) to enhance exploration efficiency. The ILPOBP model effectively predicts aesthetic ratings based on limited morphological input data. Experimental results demonstrate that the ILPOBP model outperforms baseline models in terms of accuracy and robustness when handling complex aesthetic information, achieving a significantly lower test set mean absolute relative error (MARE = 4.106%). To further enhance model interpretability, Shapley additive explanations (SHAP) are employed to elucidate the internal decision-making mechanisms, offering reverse design insights for product optimization. The proposed framework offers a novel and effective approach for integrating machine learning into the aesthetic assessment of industrial product design.

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