The field of solid modeling has developed a variety of techniques for unambiguous representations of three-dimensional objects. Feature recognition is a sub-discipline of solid modeling that focuses on the design and implementation of algorithms for detecting manufacturing information from solid models produced by computer-aided design (CAD) systems. Examples of this manufacturing information include features such as holes, slots, pockets and other shapes that can be created on modern computer numerically controlled machining systems. Automated feature recognition has been an active research area in solid modeling for many years and is considered to be a critical component for integration of CAD and computer-aided manufacturing. The paper gives an overview of the state-of-the-art in feature recognition research. Rather than giving an exhaustive survey, we focus on the three of the major algorithmic approaches for feature recognition: graph-based algorithms, volumetric decomposition techniques, and hint-based geometric reasoning. For each approach, we present a detailed description of the algorithms being employed along with some assessments of the technology. We conclude by outlining important open research and development issues.

Manufacturing feature recognition from solid models: a status report

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.

/pdf/a-decision-support-system-for-material-and-manufacturing-3tvxdiin2a.pdf

A Decision Support System for Material and Manufacturing Process Selection

This paper 1 describes an architecture for a system for machining and inspecting mechanical piece parts and an implementation of it called the Feature-Based Inspection and Control System (FBICS). In FBICS, the controller of a machining center or coordinate measuring machine uses a standard feature-based description of the shape of the object to be made as a principal input for machining and/or inspection. FBICS is a hierarchical control system and performs automated hierarchical process planning. FBICS serves: (1) to demonstrate feature-based inspection and control in an open-architecture control system; (2) as a testbed for solving problems in feature-based manufacturing; and (3) to test the usability of STEP methods and models.

A feature-based inspection and machining system

The objective for this research is to streamline design for manufacturing (DfM) analysis across all manufacturing domains and to provide transparency for DfM measures and evaluation process used. Generic steps are identified for performing manufacturability analysis and the design of a customizable manufacturability evaluation shell is presented. The shell covers several stages of manufacturability analysis (technical, economic) and can perform analyses at different levels of abstraction (qualitative, quantitative). The shell provides feedback to the designer at each level during the design process. By separating domain-specific knowledge from domain independent knowledge and creating an open architecture, the shell can be customized and expanded by the knowledge engineer and the user. The architecture of the shell is presented and its applications to manufacturability analysis for two domains, sheet metal and injection molding, are illustrated.

Domain independent shell for DfM and its application to sheet metal forming and injection molding

The tools and processes available in a given shop greatly influence the way in which manufacturers view a part and the way in which they decompose it into machinable volumes, or features. Likewise, a feature recognizer should be able to produce a different feature decomposition when different tools are available. Additionally, it should be easy for a user to add new tool descriptions to the system in order to maintain and customize the feature recognizer. We address this challenge in two parts. First, we present an extensible representation which allows users to easily add their own custom tool descriptions to a feature recognizer’s knowledge base. Second, we present C ustom -C ut , a tool-based, resource-adaptive feature recognizer. C ustom -C ut accepts the user-defined cutting tools as input and automatically identifies the areas of the part that can be cut using the custom tools. We call C ustom -C ut resource-adaptive because the features it identifies will be different if it is given different cutting tools. The advantages of this is that it is easier for the user to maintain and customize and provides greater assurance that the features identifies are actually machinable with the given set of equipment.

Mary B. Algeo

Papers

Modeling of Manufacturing Resource Information, Requirements Specification | NIST