Approaches in Computer-Aided Design Process

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Introduction

The field of this research is the computer-aided design. It is a non-linear decision-making process, the principle of which is a specific selection of configurations with optimized parameters.

Challenges:

  • The intricacy of the structure that resulted in the need to understand this process was the reason for this study.
  • It is also worth noting the desire and opportunity to improve the computer-aided design, which is also the motives for this work.

Goals/Contributions: This work’s findings may become a theoretical basis that will contribute to future improvements in the computer-aided design and the development of new approaches to interaction with the structure. The authors also believe that this work will close the skill gap between students and specialists.

Method/approach/Architecture/Framework

The chosen methodology was the semi-structured interview. This approach was chosen because, in order to understand the improvement of the computer-aided design process, it is essential to identify its configuration and parametric problems. The semi-structured interview allows the researchers to assess and define the behaviors and thought patterns of designers effectively. The volunteer group that participated in the semi-structured interview consisted of nine graduate students, five industrial CAD designers, and three Purdue University educators. It is essential to mention that all contributors have at least more than three years of experience in CAD design. Each interview’s duration was about thirty-five minutes in which the interviewer asked the participants the same questions regarding demographic data, work experience, design process, approaches, and software. Then, using a qualitative approach, the collected answers were transformed into specific topics. The reasonability of applying the qualitative approach can be explained by the non-quantitative variables and the aspiration to highlight essential nuances. The most prevalent and important issues were identified through the bottom-up grounded theory and theme analysis. It is important to mention that several irrelevant topics were removed in the process of data analysis. It was found that the selective codes are the most important ones affecting the overall user experiences.

Results and Discussion

The findings were grouped into three conceptual categories. They are the general engineering design process, detail design process, and common design problems and challenges. It was found that both students and specialists adhere to a general iterative design process. Its main steps are identifying the problem, creating a product concept, and choosing a final product concept. The most common procedural nuances are design problems, customer communication, and data interpretation. Kroll, Condoor, and Jansson identify these procedural nuances as conceptual design [1]. It is also essential to note that CAD students are more likely to work with new concepts, while CAD specialists use already established models and concepts. The most frequent and critical problems of both CAD graduates and CAD experts are multivariate data, pricing policy balancing, company’s conceptual standards, preventing the design mistakes, and parametric modeling. Poli offers another framework for categorizing these types of design problems, namely, conceptual, parametric, and configurational [2]. It is also important to mention that Myung and Han identify multivariate data and parametric modeling issues as parametric design issues [3]. The study may benefit the community as a theoretical basis. This paper can only be considered as initial support for further research.

References

Kroll, E., Condoor, S.S., Jansson, D.G.: Innovative Conceptual Design: Theory and Application of Parameter Analysis. Cambridge University Press, Cambridge (2001).

Poli, C.: Design for Manufacturing: A Structured Approach. Butterworth-Heinemann, Oxford (2001).

Myung, S., Han, S.: Knowledge-based parametric design of mechanical products based on configuration design method. Expert Syst. Appl. 21, 99–107 (2001).

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