Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model
Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model
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DOI：
10.1115/1.4052366
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发表时间：
2021-09
影响因子：
3.3
通讯作者：
Chenxi Yuan;T. Marion;Mohsen Moghaddam
中科院分区：
文献类型：
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作者：
Chenxi Yuan;T. Marion;Mohsen Moghaddam
设计概念评估是新产品开发过程中的一个关键环节，对产品的成功与否和整个生命周期的总成本有着重要的影响。本文的动机是基于当前概念评估的两个局限性：(1)现有概念评估方法（如质量功能部署）所利用的用户反馈和见解的数量和多样性是有限的。(2)主观概念评价方法需要大量的人工努力，这反过来可能会限制用于评价的概念的数量。本文提出了一个深度多模态设计评估（DMDE）模型，通过为设计师提供基于大规模用户对现有设计的评论的新概念的总体和属性级可取性的准确和可扩展的预测，来弥合这些差距。首先从在线评论中提取和汇总用户的属性级情感强度。然后，开发了一个多模态深度回归模型，通过微调的ResNet-50模型从正字法产品图像中提取特征，通过微调的BERT模型从产品描述中提取特征，并使用一种新的基于自注意力的融合模型进行聚合，从而预测整体和属性级情感值。DMDE模型在概念开发过程中添加了一个数据驱动的、以用户为中心的循环，以便更好地为概念评估过程提供信息。在一家在线鞋店的大型数据集上进行的数值实验表明，DMDE模型具有良好的性能，MSE损失为0.001，准确率超过99.1%。
Design concept evaluation is a key process in the new product development process with a significant impact on the product's success and total cost over its life cycle. This paper is motivated by two limitations of the state-of-the-art in concept evaluation: (1) The amount and diversity of user feedback and insights utilized by existing concept evaluation methods such as quality function deployment are limited. (2) Subjective concept evaluation methods require significant manual effort which in turn may limit the number of concepts considered for evaluation. A Deep Multimodal Design Evaluation (DMDE) model is proposed in this paper to bridge these gaps by providing designers with an accurate and scalable prediction of new concepts' overall and attribute-level desirability based on large-scale user reviews on existing designs. The attribute-level sentiment intensities of users are first extracted and aggregated from online reviews. A multimodal deep regression model is then developed to predict the overall and attribute-level sentiment values based on the features extracted from orthographic product images via a fine-tuned ResNet-50 model and from product descriptions via a fine-tuned BERT model, and aggregated using a novel self-attention-based fusion model. The DMDE model adds a data-driven, user-centered loop within the concept development process to better inform the concept evaluation process. Numerical experiments on a large dataset from an online footwear store indicate a promising performance by the DMDE model with 0.001 MSE loss and over 99.1% accuracy.