Interactive animation of parametric models

This paper describes a program which allows parametric models of three-dimensional characters and scenes to be interactively controlled for computer animation. The system attempts to span the two most common approaches to animation: language-driven or programmed and visually-driven or interactive. Models are designed in a geometry language which supports vector and matrix arithmetic, transformations and instancing of primitive parts. As a result, constraints and functional dependencies between different parts can be programmed. Control is achieved by parameterizing the model. Subsets of parameters can be connected to different logical input devices, establishing an input mode to control the model's shape. Parameter sets can be stored to form a database of positions. Positions then can be mapped to frames and interpolated to animate the model.     

Order reduction for nonlinear dynamic models of distributed reacting systems

Detailed first-principles models of transport and reaction (based on partial differential equations) lead, after discretization, to dynamical systems of very high order. Systematic methodologies for model order reduction are vital in exploiting such fundamental models in the analysis, design and real-time control of distributed reacting systems. We briefly review some approaches to model order reduction we have successfully used in recent years, and illustrate their capabilities through (a) the design of an observer and stabilizing controller of a reaction-diffusion problem and (b) two-dimensional simulations of the transient behavior of a horizontal MOVPE reactor.     

A decision-support method for multi-parameter editing of parametric CAD models

Parametric modeling is a computer-aided design (CAD) paradigm where a design can be created by defining geometric constraints with parameters. In design change as well as design optimization, the design is often edited by modifying the values of relevant parameters. Without guidance on allowable parameter ranges that can guarantee the intrinsic solvability of the geometric constraint system, the user could assign improper values to the model’s parameters, which would further lead to a failure in model updating. However, current commercial CAD systems provide little support on such guidance. Existing methods, though able to compute allowable ranges for individual parameters, face difficulties in handling multi-parameter situations. To solve this problem, a systematic method is proposed, supporting decision-making in multi-parameter model editing by computing allowable parameter ranges. In the method, a set of variable parameters from the geometric constraint system are first selected by the user; these variable parameters are to be sequentially edited within several editing operations. Before each editing operation, 1D allowable ranges of the variable parameters are computed. By editing parameter values within the provided ranges, the solvability of the geometric constraint system can be guaranteed. The effectiveness and efficiency of the proposed approach is verified by several experimental results.     

A parametric feature-based CAD system for reproducing traditional pierced jewellery

In this paper, we introduce ByzantineCAD, a parametric CAD system for the design of pierced medieval jewellery, which is jewellery created by piercing, a traditional Byzantine technique. ByzantineCAD is an automated parametric system where the design of a piece of jewellery is expressed by a collection of parameters and constraints and the user's participation in the design process is through the definition of the parameter values. We present an approach to designing traditional pierced jewellery using a voxel-oriented feature-based Computer Aided Design paradigm: a large complex pierced design is created by appropriately placing elementary structural elements. We also present a scaling algorithm for enlarging pierced designs without altering the size of the elementary structural elements used to construct them.     

智能化门窗CAD系统的研究和开发

本文将人工智能的思想引入门窗CAD领域,给出了此系统的具体架构,同时介绍了一些相关的技术问题及其解决方案。最后给出了一套实际的集产品设计、报价分析、材料管理、参数绘图、工程管理等为一体的智能化门窗CAD系统,并取得用户好评。     

A decision-support method for information inconsistency resolution in direct modeling of CAD models

Direct modeling is a very recent CAD paradigm that can provide unprecedented modeling flexibility. It, however, lacks the parametric capability, which is indispensable to modern CAD systems. For direct modeling to have this capability, an additional associativity information layer in the form of geometric constraint systems needs to be incorporated into direct modeling. This is no trivial matter due to the possible inconsistencies between the associativity information and geometry information in a model after direct edits. The major issue of resolving such inconsistencies is that there often exist many resolution options. The challenge lies in avoiding invalid resolution options and prioritizing valid ones. This paper presents an effective method to support the user in making decisions among the resolution options. In particular, the method can provide automatic information inconsistency reasoning, avoid invalid resolution options completely, and guide the choice among valid resolution options. Case studies and comparisons have been conducted to demonstrate the effectiveness of the method.     

Knowledge acquisition techniques for feature recognition in CAD models

Automatic Feature Recognition (AFR) techniques are an important tool for achieving a true integration of design and manufacturing stages during the product development. In particular, AFR systems offer capabilities for recognising high-level geometrical entities, features, in Computer-Aided Design (CAD) models. However, the recognition performances of most of the existing AFR systems are limited to the requirements of specific applications. This paper presents automatic knowledge acquisition techniques to support the development of AFR systems that could be deployed in different application domains. In particular, a method to generate automatically feature recognition rules is proposed. These rules are formed by applying an inductive learning algorithm on training data consisting of feature examples. In addition, a technique for defining automatically feature hints from such rule sets is described. The knowledge acquisition techniques presented in this study are implemented within a prototype feature recognition system and its capabilities are verified on two benchmarking parts.     

A survey of CAD model simplification techniques for physics-based simulation applications

Automated CAD model simplification plays an important role in effectively utilizing physics-based simulation during the product realization process. Currently a rich body of literature exists that describe many successful techniques for fully-automatic or semi-automatic simplification of CAD models for a wide variety of applications. The purpose of this paper is to compile a list of the techniques that are relevant for physics-based simulations problems and to characterize them based on their attributes. We have classified them into the following four categories: techniques based on surface entity based operators, volume entity based operators, explicit feature based operators, and dimension reduction operators. This paper also presents the necessary background information in the CAD model representation to assist the new readers. We conclude the paper by outlining open research directions in this field.     

Assessing local structural identifiability for environmental models

The local structural identifiability problem is investigated for the general case and demonstrated for a well-known microbial degradation model that includes 13 unknown parameters and 3 additional states. We address the identifiability question using a novel algorithm that can be used for large models with many parameters to be identified. A key ingredient in the analysis is the application of a singular value decomposition of the normalized parametric output sensitivity matrix that is obtained through a simple model integration. The SVD results are further analysed and verified in a complementary symbolic computation. It is especially the swiftness and accuracy of the suggested method that we consider to be a substantial advantage in comparison to existing methods for a structural identifiability analysis. The method also opens, in a natural way, the analysis of (parametric) uncertainty in general, and this is demonstrated in more detail in the results section.     

Analyzing and handling local bias for calibrating parametric cost estimation models

ContextParametric cost estimation models need to be continuously calibrated and improved to assure more accurate software estimates and reflect changing software development contexts. Local calibration by tuning a subset of model parameters is a frequent practice when software organizations adopt parametric estimation models to increase model usability and accuracy. However, there is a lack of understanding about the cumulative effects of such local calibration practices on the evolution of general parametric models over time.ObjectiveThis study aims at quantitatively analyzing and effectively handling local bias associated with historical cross-company data, thus improves the usability of cross-company datasets for calibrating and maintaining parametric estimation models.MethodWe design and conduct three empirical studies to measure, analyze and address local bias in cross-company dataset, including: (1) defining a method for measuring the local bias associated with individual organization data subset in the overall dataset; (2) analyzing the impacts of local bias on the performance of an estimation model; (3) proposing a weighted sampling approach to handle local bias. The studies are conducted on the latest COCOMO II calibration dataset.ResultsOur results show that the local bias largely exists in cross company dataset, and the local bias negatively impacts the performance of parametric model. The local bias based weighted sampling technique helps reduce negative impacts of local bias on model performance.ConclusionLocal bias in cross-company data does harm model calibration and adds noisy factors to model maintenance. The proposed local bias measure offers a means to quantify degree of local bias associated with a cross-company dataset, and assess its influence on parametric model performance. The local bias based weighted sampling technique can be applied to trade-off and mitigate potential risk of significant local bias, which limits the usability of cross-company data for general parametric model calibration and maintenance.     

油压减振器计算机辅助优化设计系统的研制

为了优化可调式线性油压减振器的性能，在白行研制的广义优化设计支持平台上对其线性阻尼系统进行广义优化建模与设计，并基于MATLAB开发了动态仿真软件包，对该广义优化设计结果进行分析与评价．样机开发与试验研究结果表明：文中建立的广义优化设计数学模型和动态仿真模型是准确的，研制的计算机辅助优化设计系统高效、实用．     

An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique

This paper presents a new hybrid (graph + rule based) approach for recognizing the interacting features from B-Rep CAD models of prismatic machined parts. The developed algorithm considers variable topology features and handles both adjacent and volumetric feature interactions to provide a single interpretation for the latter. The input CAD part model in B-Rep format is preprocessed to create the adjacency graphs for faces and features of associated topological entities and compute their attributes. The developed FR system initially recognizes all varieties of the simple and stepped holes with flat and conical bottoms from the feature graphs. A new concept of Base Explicit Feature Graphs and No-base Explicit Feature Graphs has been proposed which essentially delineates between features having planar base face like pockets, blind slots, etc. and those without planar base faces like passages, 3D features, conical bottom features, etc. Based on the structure of the explicit feature graphs, geometric reasoning rules are formulated to recognize the interacting feature types. Extracted data has been post-processed to compute the feature attributes and their parent-child relationships which are written into a STEP like native feature file format. The FR system was extensively tested with several standard benchmark components and was found to be robust and consistent. The extracted feature file can be used for integration with various downstream applications like CAPP.     

Structure-preserving model reduction of nonlinear building thermal models

This paper proposes an aggregation-based model reduction method for nonlinear models of multi-zone building thermal dynamics. The full-order model, which is already a lumped-parameter approximation, quickly grows in state space dimension as the number of zones increases. An advantage of the proposed method, apart from being applicable to the nonlinear thermal models, is that the reduced model obtained has the same structure and physical intuition as the original model. The key to the methodology is an analogy between a continuous-time Markov chain and the linear part of the thermal dynamics. A recently developed aggregation-based method of Markov chains is employed to aggregate the large state space of the full-order model into a smaller one. Simulations are provided to illustrate tradeoffs between modeling error and computation time.     

Meshless methods for physics-based modeling and simulation of deformable models

As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects’ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point samples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meshless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations. Supported by the National Science Foundation (Grant Nos. CCF-0727098, IIS-0710819)     

Response-time simulation of multivariate point process models for multiprogrammed jobstreams

We consider simulation methods for particular marked multivariate point processes, calledR-processes, which have recently been proposed as models for multiprogrammed jobstreams. Using workload marks on events, such models facilitate the incorporation of realistic workload characteristics into computer system performance predictions. We consider R-processes in which workload marks for an individual jobstream form a stationary sequence of discrete random variables having a (generally non-Markovian) mixed moving average-autoregressive dependency structure. For such models we provide a method for obtaining from a single simulation run point and interval estimates for general characteristics of job response times.     

Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models

Model reduction techniques have shown to constitute a valuable tool for real-time simulation in surgical environments and other fields. However, some limitations, imposed by real-time constraints, have not yet been overcome. One of such limitations is the severe limitation in time (established in 500 Hz of frequency for the resolution) that precludes the employ of Newton-like schemes for solving non-linear models as the ones usually employed for modeling biological tissues. In this work we present a technique able to deal with geometrically non-linear models, based on the employ of model reduction techniques, together with an efficient non-linear solver. Examples of the performance of the technique over some examples will be given.     

Model reduction for multidisciplinary optimization - application to a 2D wing

Multidisciplinary optimization (MDO) is a growing field in engineering, with various applications in aerospace, aeronautics, car industry, etc. However, the presence of multiple disciplines leads to specific issues, which prevent MDO to be fully integrated in industrial design methodology. In practice, the key issues in MDO lie in the management of the interconnections between disciplines, along with the high number of simulations required to find a feasible multidisciplinary (optimal) solution. Therefore, in this paper, a novel approach is proposed, combining proper orthogonal decomposition to decrease the amount of data exchanged between disciplines, with surrogate models based on moving least squares to reduce disciplines. This method is applied to an original 2D wing demonstrator involving two disciplines (fluid and structure). The numerical results obtained for an optimization task show its benefits in diminishing both the interfaces between disciplines and the overall computational time.     

3-D object decomposition with extended octree model and its application in geometric simulation of NC machining

The paper discusses the development of a prototype solid modeling system based on the extended octree modeling approach and its applications in 3-D NC machining simulation and automatic verification. Along with a simple hierarchical data structure, the extended octree model uses the face boundary information (i) to represent complex objects, (ii) to improve object representational accuracy, and (iii) to accelerate the model updating procedures in a graphic simulation process. The improved representational accuracy makes it possible to carry out automatic NC program verification by generating the machined model of the part through the simulation and comparing it with the designed model of the part. The paper also addresses the issues of model conversions from CSG and B-Rep schemes to the corresponding extended octree models and the issues of carrying out Boolean operations on those extended octree models. A prototype system has been implemented and is integrated with AutoCAD AME solid modeler for object modeling and for NC simulation purpose.