Intelligent Recognition Method for Form-Imagery Coupling in Automotive Wheel Hub Design

ZHANG Yutong; SUN Li; WU Jiantao; ZHANG Shuo; SUN Xi; LI Changrun; ZHAO Jianing; JIA Xiaolu; HAN Xu

doi:10.19554/j.cnki.1001-3563.2026.12.007

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (12) : 83-99. DOI: 10.19554/j.cnki.1001-3563.2026.12.007

Industrial Design

Intelligent Recognition Method for Form-Imagery Coupling in Automotive Wheel Hub Design

ZHANG Yutong¹, SUN Li¹, WU Jiantao¹, ZHANG Shuo¹, SUN Xi¹, LI Changrun¹, ZHAO Jianing¹, JIA Xiaolu^1,*, HAN Xu²

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Abstract

To address the semantic mismatch between designers' intentions and users' perceptions due to "form-imagery" decoupling in automotive wheel hub design, the work aims to propose an intelligent recognition method integrating cross-modal contrastive learning (CL) and a multi-expert query network (MEQN). Firstly, a biterm topic model was employed to mine latent semantic structures from user reviews, and representative design imagery features were rigorously identified and quantified through an analytic hierarchy process, thus constructing a multimodal image-text dataset for automotive wheel hub form-imagery analysis. Secondly, a cross-modal dual encoder comprising a 12-layer Transformer and ResNet-50 was developed to deeply encode textual semantics and visual features, respectively. Thirdly, the cosine similarity between modal features was optimized through Contrastive Learning (CL) to form a unified, fine-grained cross-modal representation space, thus constructing a Transformer and ResNet-based contrastive learning model (TRCL). Finally, MEQN was embedded into the TRCL framework, where each expert sub-network independently focused on specific semantic dimensions of visual modality features, and cross-modal attention mechanisms dynamically associated visual and textual information, achieving fine-grained cross-modal feature interaction and fusion. Experimental results demonstrated that the proposed MEQN-TRCL method significantly outperformed mainstream models. Meanwhile, ablation experiments further verified its rational structural design and advanced performance. The method proposed effectively facilitates precise coupling between wheel hub form features and Kansei imagery, thereby offering a novel methodological pathway for quantitative emotional design decision-making and cross-modal form-imagery matching in automotive wheel hub design.

Key words

automotive wheel hub design / form-imagery coupling / cross-modal feature fusion / Contrastive Learning (CL) / multi-expert query network

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ZHANG Yutong, SUN Li, WU Jiantao, ZHANG Shuo, SUN Xi, LI Changrun, ZHAO Jianing, JIA Xiaolu, HAN Xu. Intelligent Recognition Method for Form-Imagery Coupling in Automotive Wheel Hub Design[J]. Packaging Engineering. 2026, 47(12): 83-99 https://doi.org/10.19554/j.cnki.1001-3563.2026.12.007

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