David G. Ullman

The importance of drawing in the mechanical design process

Abstract This paper is a study on the importance of drawing (both formal drafting and informal sketching) during the process of mechanical design. Five hypotheses, focused on the types of drawings, their necessity in mechanical problem solving, and their relation to the external representation medium, are presented and supported. Support is through referenced studies in other domains and the results of protocol studies performed on five mechanical designers. Videotapes of all the marks-on-paper made by designers in representative sections of the design process were studied in detail for their type and purpose. The resulting data is supportive of the hypotheses. These results also give requirements for future computer aided design tools and graphics education, and goals for further studies.

A model of the mechanical design process based on empirical data

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.

The information requests of mechanical design engineers

Abstract Design documentation does not typically include all of the information sought by mechanical design engineers. This paper reports on a study of practicing engineers making modifications to existing designs. Particular attention is paid to the design information required to answer questions about the design and to verify and refute conjectures about the design. A taxonomy of the questions asked by the designers in this study and the conjectures they formed is presented. It is proposed that an intelligent CAD system be developed to capture, structure, and re-play this information.

Robust decision-making for engineering design

This paper develops the importance of decision-making in the engineering design. It makes the argument that the decisions made, and the information contained in the decision-making effort, are key to managing the design process. Based on this importance, the concept of robust decision-making is developed and 12 steps that are necessary to make robust decisions are itemized. Activities that support each step will also be listed.

Fundamental Processes of Mechanical Designers Based on Empirical Data

SUMMARY The fundamental processes of problem solving during the mechanical design activity are presented based on a study of human designers. The designers in this study solve problems by applying 10 specialized actions, called operators, which are grouped into the categories of ‘generate’, ‘evaluate’ and ‘decide’. These operators are applied in unique sequences of which 95% constitute the following four local methods: ‘generate and test’ (23%), ‘generate and improve’ (8%), ‘deductive thinking’ (33%) and ‘means end analysis’ (36%). Furthermore, cognitive activity is reduced to a hierarchical architecture of tasks and episodes which reflect the goal structure of the designer. These descriptions demonstrate the abilities and limitations of the human designers problem-solving performance, yielding an insight into how the man-machine interaction of design automation can be improved.

A taxonomy for mechanical design

This paper presents a taxonomy that provides a basis for characterizing mechanical design methods and theories. The taxonomy has three primary divisions: the environment, the problem, and the process. Each of these factors is further subdivided into its important characteristics. For example, the process is divided into plan, processing action, effect, and failure action. This paper discusses the options for each characteristic. An overview of the proposed taxonomy is given in section 2 of this paper. Section 3 describes details of the design environment; section 4 gives details on the description of the design problem itself; and section 5 provides details on the design process. In section 6, the taxonomy is applied in two ways: it is first used to clarify the meaning of differing, commonly used design terms, such as selection design, configurational design, parametric design, and redesign; and, second, the taxonomy is used to classify a representative sample of design process research efforts.

Taxonomy for classifying engineering decision problems and support systems

The design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis ? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams-some are about the product and others about the processes that support the product - some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

A comparison of the results of empirical studies into the mechanical design process

Abstract Six investigations are compared in which the mechanical design process was evaluated by studying human designers. These studies are summarized on the basis of their purpose and evidence followed by a brief discussion. The conclusions reached in all six studies are then compared to show areas of agreemtnt or disagreement and potential for further research needed to gain a better understanding of the mechanical design process.

The Design Capture System: Capturing Back-of-the-envelope Sketches

SUMMARY A system which allows the computer to capture sketches made by a mechanical designer is described. The system not only recognizes basic design features as they are sketched, but it also builds a feature-based solid model of the artifact. The temporal nature of the capture, one feature at a time, serves to form a feature graph that allows for parametric redesign. The system is composed of three inference systems: a two-dimensional freehand primitive recognition system, a three-dimensional feature recognition system and a spatial reasoning system. Each is described in the paper.

What to do next: Using problem status to determine the course of action

Formal decision support tools are little used in engineering design. This paper explores the reasons for this and presents a method which is tailored to problems characterized by teams of stakeholders with inconsistent views who generate multiple alternatives and criteria, and who work to reach consensus. This method is especially designed to support activity when much of the information is qualitative, immature, and there is a diversity of views. The methodology assists the team in determining which alternative attributes to invest time in refining in their effort to reach consensus. The underlying mathematical structure (a Bayesian model of multi-attribute team decision making) is presented. This model supports team member belief about an alternatives ability to meet a criteria on two dimensions, knowledge and confidence. The methodology forces recording the rationale used to reach the final decision. A running example is used to explain the details.