Design fixation is related to the broad phenomenon of unconscious cognition bias that hinders the generation of creative solutions during the conceptual design process. While numerous research studies have gone into the study of design fixation, the experimental methods used were external to the cognitive process of designers; thus, there are some limitations. To address these limitations, the present study utilized electroencephalography (EEG) to explore the differences in neural activities between designers with different degrees of design fixation during creative idea generation. Fluency, flexibility, and the degree of copying were used to evaluate the design performance and fixation degrees of all participants; for the follow‐up analyses on brain activity patterns, participants were then divided into the Higher Fixation Group and the Lower Fixation Group according to the evaluation of the degrees of copying. Next, participants in each group were contrasted separately against the task‐related alpha power changes during creative idea generation. The comparison results revealed that participants with lower design fixation demonstrated stronger alpha synchronization in frontal, parietotemporal, and occipital regions during creative idea generation, while participants with higher design fixation showed stronger task‐related alpha desynchronization in frontal, centroparietal, and parietotemporal regions. Such findings suggested that participants with higher fixation showed lower solution flexibility because of the inability to inhibit the solutions generated overrelying on intuition. These results could contribute to a deeper understanding of design fixation from the neuroscience perspective and provide essential theoretical supports for the subsequent defixation methods and tool development.

Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions.

Improving existing products plays a vital role in enhancing customer satisfaction and coping with changes in the market. Analyzing user experience (UX) to find the deficiencies of existing products and establishing improved schemes is the key to UX-driven product improvement, especially at the conceptual design stage. Although some tools used in conceptual design, such as requirements analysis and knowledge reasoning, have advanced recently, they lack targeted goals and sufficient efficiency in identifying insufficient product attributes and improving existing functions and structures. The challenge lies in considering the influence imposed on design activities by the original product features (including attributes, functions, and structure). In this study, a knowledge-enabled approach and framework that integrates the conceptual design process, online reviews for UX, and knowledge is proposed to support product improvement. Specifically, a decision-making algorithm based on UX analysis is proposed to identify to-be-improved product attributes. Then, through optimizing the previous knowledge application model from knowledge requirement transformation, knowledge modeling, and knowledge reasoning, a smart knowledge reasoning model is established to push knowledge for functional solving of the to-be-improved attributes. A knowledge configuration method is used to modify product features to generate an improved scheme. To demonstrate the feasibility of the proposed approach, a case study of improving an agricultural sprayer is conducted. Through discussion, this study can help to regulate design activities for product improvement, enhance data and knowledge application, and promote divergent thinking during scheme modification.

The key to successful product development is better understanding of customer requirements and efficiently identifying the product attributes. In recent years, a growing number of researchers have studied the mining of customer requirements and preferences from online reviews. However, since customer requirements often change dynamically on multi-generation products, most existing studies failed to discover the correlations between customer satisfaction and continuous product improvement. In this work, we propose a novel dynamic customer requirement mining method to analyze the dynamic changes of customer satisfaction of product attributes based on sentiment and attention expressed in online reviews, aiming to better meet customer requirements and provide the direction and content of future product improvement. Specifically, this method is divided into three parts. Firstly, text mining is adopted to collect online review data of multi-generation products and identify product attributes. Secondly, the attention and sentiment scores of product attributes are calculated with a natural language processing tool, and further integrated into the corresponding satisfaction scores. Finally, the improvement direction for next-generation products is determined based on the changing satisfaction scores of multi-generation product attributes. In addition, a case study on multi-generation phone products based on online reviews was conducted to illustrate the effectiveness and practicality of the proposed methodology. Our research completes the field of requirements analysis and provides a new dynamic approach to requirements analysis for continuous improvement of multi-generation products, which can help enterprises to accurately understand customer requirements and improve the effectiveness and efficiency of continuous product improvement.