Wavelet-Based Surface Analysis for Form Feature Extraction

A new procedure for extracting form features from solid models with non-planar surfaces is presented in this paper. In the procedure, a surface is selected as the unit for feature representation, i.e. “feature primitive.” Three-dimensional wavelet transforms are applied to code and classify surfaces in a CAD model. Form features are then extracted by clustering the coded surfaces. Two wavelet bases, Harr and Daubechies with different vanishing moments, have been implemented. An example is presented to demonstrate the proposed procedure.

Automatic Calculation of Product Architecture Metrics Within a Solid Modeler

The architecture of a product is defined as the scheme in which functions are mapped to physical components. Architecture has a strong impact on how a product satisfies design objectives. There are several ways to measure architecture, and one is implemented into a solid modeling program, that will do the quantification automatically. The software used is the I-DEAS solid modeling package, for which an internal program file was created to automatically perform the calculations. This program works by counting the parts that the user has created, then uses an internal I-DEAS function to find all of the joined parts. The program counts the joints and then prompts the user for the strength of each joint. With this information, an adjusted parts connectivity and average joint strength is calculated and can be used to evaluate the degree to which the architecture of a product is either integral or modular. Four case studies are presented that were used to evaluate the effectiveness of the program. Three of these yielded excellent results, but the final case study failed because of model input problems. With further development this program could be a very important design tool in the future.

Octree Based Recognition of Assembly Features

This paper reviews the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features, these are features which arise from these “spatial” and “contact” face adjacency relationships (known as s- and c-adjacency respectively). The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. Once all the adjacent faces which form features have been located, they are used to partition the original faces of the assembly into adjacent and non-adjacent portions. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. By way of illustration, the algorithm is applied to a trial component.

Approximate Life-Cycle Assessment in Conceptual Product Design

Prior work has demonstrated the integration of detailed life-cycle assessment into a traditional design modeling process. While a full life-cycle assessment provides insight into a product’s potential impact on the environment, it is often too time consuming for analysis during conceptual product design, where ideas are numerous and information is scarce. The work presented in this paper explores an approximate method for preliminary life-cycle assessments without detailed modeling requirements. Learning algorithms trained on the known characteristics of existing products allow the environmental impacts of new products to be approximated quickly during conceptual design. Artificial neural networks train on product attributes and environmental impact data from pre-existing life-cycle assessment studies. The product design team queries the trained artificial model with new high-level product attribute data to quickly obtain an approximate impact assessment for a new product concept. Tests based on simplified inventory data have shown it is possible to predict impacts on life-cycle energy consumption, and that there is a basis for the method to be used in also predicting solid material, greenhouse effect, ozone layer depletion, acidification, eutrophication, winter smog, and summer smog.

Machining: Quality, Cost and Environmental Estimation and Tradeoffs

The machining process affects manufacturing cost, product quality and the environment. This paper presents a method for formulating a mathematical model for first estimating quality, cost and environmental impacts of two machining operations (end milling and drilling), and then for tradeoff decision making. The milling quality estimation model is developed through virtual experimentation on a simulation model and the drilling quality estimation model through physical experimentation. Cost is estimated through an activity based costing approach. Environmental BOD and FOG impacts are estimated through stoichiometric analysis of cutting fluids. Inputs include material choice, feed rate, speed and cutting fluids. A case study of an automotive steering knuckle is presented.

A Tool-Centric Approach to Generating Redesign Suggestions for Improved Machinability

Providing early feedback on the manufacturability of a part design can greatly improve the quality of the product while reducing the time and cost of production. However, the necessary manufacturing knowledge is not always available. Computer tools that can provide this manufacturing knowledge by analyzing a design suggesting changes to improve its manufacturability would be a valuable asset to a designer. To this end, we present an approach to automatically generate redesign suggestions to improve the manufacturability of machined parts. Novel aspects of this approach include the ability to identify un-machinable shapes in a part and transform them into machinable features and to automatically identify the possible shape transformations based on properties of the machining equipment. This increases the scope of redesign generation tools by allowing them to be applied to parts that are not already machinable.

Design for Production: A Tool for Reducing Manufacturing Cycle Time

This paper describes a decision support tool that can help a product development team reduce manufacturing cycle time during product design. This design for production (DFP) tool determines how manufacturing a new product design affects the performance of the manufacturing system by analyzing the capacity requirements and estimating the manufacturing cycle times. Performing these tasks early in the product development process can reduce product development time. The paper presents a comprehensive DFP approach and describes the components of the DFP tool, which gives feedback that can be used to eliminate manufacturing cycle time problems. We present an example that illustrates the tool’s functionality.

Performance Distribution Analysis and Robust Design

The paper presents the theory of performance sensitivity distribution and a novel robust parameter design technique. In the theory, a Jacobian matrix describes the effect of the component tolerance to the system performance, and the performance distribution is characterized in the variation space by a set of eigenvalues and eigenvectors. Thus, the feasible performance space is depicted as an ellipsoid. The size, shape, and orientation of the ellipsoid describe the quantity as well as quality of the feasible space and, therefore, the performance sensitivity distribution against the tolerance variation. The robustness of a design is evaluated by comparing the fitness between the ellipsoid feasible space and the tolerance space, which is a block, through a set of quantitative and qualitative indexes. The robust design can then be determined. The design approach is demonstrated in a mechanism design problem. Because of the generality of the analysis theory, the method can be used in any design situation as long as the relationship between the performance and design variables can be expressed analytically.

Graph Grammar Based Product Variety Modeling

This paper discusses the issue of product variety modeling, i.e. the means to organize the data of a family of products according to the underpinning logic among them. The targeted product families are characterized by providing user-selectable product features and feature values and achieving variety by combining parameterized functional or physical modules. A graph grammar based (GGB) model is proposed for the purpose of enhancing the comprehensiveness and manipulability of the data of product families for different functional departments in a company in order to facilitate effective order processing as well as direct customer-manufacturer interaction. To deal with variety effectively, both structural and non-structural family data are represented as family graphs whereas order-specific products are represented as variant graphs derived by applying predefined graph rewrite rules to the family graphs. The most important characteristics of the GGB model are three folds. While emphasizing the distinctiveness of the information that different users are concerned about, it provides cross view data transferring mechanisms. It also supports data manipulation for variety generation. Finally, taking advantage of the graph grammar based language of PROGRES, GGB is a model to be easily implemented as a visualized computer system. The specification of an office chair product family illustrates the principles and construction process of GGB models.

Decomposition Based Feature Recognition Using Constrained and Aggregated Half-Space Partitioning

Decomposition based feature recognition (DBFR) has drawn attention over the years. It has two stages: decomposition and aggregation. At the decomposition stage, the CAD model is partitioned into minimal cells. At the aggregation stage, the decomposed individual cells are composed in different combinations and these combinations are matched with predefined feature patterns to retrieve features in the model. The DBFR technique shows promises to deal with interactive features. However, DBFR algorithms suffer from the combinatorial problem in both the partitioning and the composing stages. This paper proposes a novel decomposition based feature recognition technique using the constrained and aggregated half-space partitioning. The constrained and aggregated half-space is defined in the occupation of a volume in the Euclidean space, bounded by multiple surfaces. The decomposition approach based on this concept can largely avoid over-cuttings. It tends to produce partitions that can be directly matched with feature patterns. Different from other DBFR algorithms, pattern matching is also introduced in the decomposition stage. Thus it further shrinks the space of combination and feature determination. Some algorithms are also proposed to do efficient volume combinations at the aggregation stage.