| 陈淑鑫,李精宇,张宏斌,张辉.基于BP神经网络的个性化多功能茶几设计研究[J].包装工程,2022,43(18):247-254. |
| 基于BP神经网络的个性化多功能茶几设计研究 |
| Personalized Multifunctional Tea Table Design Based on BP Neural Network |
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| DOI：10.19554/j.cnki.1001-3563.2022.18.029 |
| 中文关键词: 感性工学 BP神经网络 产品设计 因子分析法 多功能茶几 |
| 英文关键词: product personalized design Kansei Engineering BP neural network factor analysis multifunctional coffee table |
| 基金项目:国家自然科学基金联合项目(U2031142);黑龙江省农业多维传感器信息感知工程技术研究中心开放课题项目(DWCGQKF202107) |
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| 中文摘要: |
| 目的 通过分析消费者感性需求和多功能茶几产品形态设计要素，建立二者之间回归的联系模型，完成多功能茶几产品的个性化设计，解决茶几产品无法按照用户消费需求设计制造的难题。方法 首先运用语义差异法获取消费者对茶几产品的感性意象评价值，并利用因子分析法对评价值进行归纳整理，其次按照茶几产品设计要素对其进行模块解构，并对各部分模块进行数值化编码，再次根据整理的感性意象评价值和模块数值训练茶几产品BP神经网络，建立二者间映射关系，最后实施二次语义差异法问卷实验，验证BP神经网络的准确性。结果 根据训练的茶几产品BP神经网络可预测出感性评价值最大的茶几产品造型，实验结果验证了茶几产品BP神经网络模型的准确性，为茶几产品的个性化设计提供了有利的支撑。结论 此方法提高了茶几产品的设计效率，提升了茶几产品设计的合理性，解决了家具设计者无法精准按照用户主观需求完成客观产品设计的难题，为以消费者需求市场为导向的产品设计制造提供了有益的参考和指导。 |
| 英文摘要: |
| By analyzing the consumer's perceptual demand and the design elements of the multifunctional tea table product form, the paper aims to establish the regression relationship model between the two to complete the personalized design of the multifunctional tea table product and solve the problem that the tea table product cannot be designed and manufactured according to the user's consumption demand. Firstly, semantic differential method is used for consumer products for tea table perceptual image value, and the factor analysis is used to summarize the value of sorting. Secondly, according to the product design elements on the tea table module deconstruction, and each part of the module is numerically coded. And BP neural network of tea table products is trained according to the perceptual image evaluation value and module value, and the mapping relationship between them is established. Finally, the accuracy of the BP neural network is verified by the questionnaire experiment with the second semantic difference method. According to the BP neural network of tea table products trained, the model of tea table products with the highest perceptual evaluation value can be predicted. The accuracy of BP neural network model of tea table products is verified by the experimental results of the second semantic difference method, which provides favorable support for the personalized design of tea table products. This method improves the design efficiency and rationality of coffee table products, solves the problem that furniture designers cannot accurately complete objective product design according to users' subjective needs, and provides beneficial reference and guidance for product design and manufacturing based on consumer demand and market. |
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