Background and Defining Characteristics

Given a computerized representation of the design and a set of manufacturing resources, the automated manufacturability analysis problem can be defined as follows:

1. Determine whether or not the design attributes (e.g., shape, dimensions, tolerances, surface finishes) can be achieved.
2. If the design is found to be manufacturable, determine a manufacturability rating, to reflect the ease (or difficulty) with which the design can be manufactured.
3. If the design is not manufacturable, then identify the design attributes that pose manufacturability problems.

Three of the primary characteristics that distinguish various manufacturability systems from each other include what approach they take, what measure of manufacturability they use and what level of automation they achieve. These are described further below:

1. Approach. For analyzing the manufacturability of a design, the existing approaches can be classified roughly as follows:
   • In direct or rule-based approaches [15,16,17], rules are used to identify infeasible design attributes from direct inspection of the design description. This approach is useful in domains such as near-net shape manufacturing. However, it is less suitable for machined or electro-mechanical components, in which interactions among manufacturing operations can make it difficult to determine the manufacturability of a design directly from the design description.
   • In indirect or plan-based Approaches [18,19,20] the first step is to generate a manufacturing plan, and modifying various portions of the plan in order to reduce its cost. If there is more than one possible plan, then the most promising plan should be used for analyzing manufacturability. These systems have wider applicability than do direct systems.

2. Measure of Manufacturability. There are many different scales---or combinations of scales---on which manufacturability can be measured:
   • Binary measures. This the most basic kind of manufacturability rating: it simply reports whether or not a given set of design attributes is manufacturable.
   • Qualitative measures. Here design are given qualitative grades based on their manufacturability by a certain production process. For example, Ishii et al. [15] rated designs as ``poor,'' ``average,'' ``good,'' or ``excellent,''. Such measures are hard to interpret---and in situations where the designer employs multiple manufacturability analysis tools (for example, one for machining and the other one for assembly), it becomes difficult to compare and combine the ratings from the two systems to obtain an overall rating.

   • Abstract quantitative. This type of scheme involves rating a design by assigning numerical ratings along some abstract scale. For example, Shankar et al. [22] proposed a scheme in which each design attribute was assigned a manufacturability index between 1 and 2. Just as with qualitative measuring schemes, it can be difficult to interpret such measures or to compare and combine them.
   • Time and cost. In general, a design's manufacturability is a measure of the effort required to manufacture the part according to the design specifications. Since all manufacturing operations have measurable time and cost, these can be used as an underlying basis to form a suitable manufacturability rating. Ratings based on time and cost can easily be combined into a overall rating. Moreover, they present a realistic view of the difficulty in manufacturing a proposed design and can be used to aide management in making make-or-buy decisions.

3. Level of automation. This involves how designer interacts with the system and what type of information is provided to the designer as feedback.
   • Amount and type of designer interaction. In some systems (e.g., [23]), the designer may need to enter a feature-based representation of the design in terms of the particular feature library used by the system. In more sophisticated systems, [24] the system works directly from the solid model of the design. If needed, feature-based representations are generated automatically.

   • Amount and type of feedback information. Most manufacturability analysis systems provide some kind of manufacturability rating of the design. Some systems provide detailed decomposition of the manufacturability ratings of various design attributes [25]. A few systems provide, along with the manufacturability rating, redesign suggestions to improve the design. Usually these are suggestions to change parameters of various design features [26], but some systems [18] present redesign suggestions as complete redesigned parts.

Figure 1 illustrates the characteristics of manufacturability analysis systems and their trade offs.

  
Figure 1: Characteristics of manufacturability analysis systems and their trade offs.