Structural optimization of as-built parts using reverse engineering and evolution strategies

In industry, some parts are prone to failures or their design is simply sub-optimal. In those critical situations, one would like to be able to make changes to the part, making it lighter or improving its mechanical resistance. The problem of as-built parts is that the original computer-aided design (CAD) model is not available or is lost. To optimize them, a reverse engineering process is necessary to capture the shape and topology of the original design. This paper describes how to capture the original design geometry using a semi-automated reverse engineering process based on measurement provided by an optical 3D sensor. Following this reverse engineering process, a Fixed Grid Finite Element method and evolutionary algorithms are used to find the optimum shape that will minimize stress and weight. Several examples of industrial parts are presented. These examples show the advantages and disadvantages of the proposed method in an industrial scenario. Presented at the 7th World Congress on Computational Mechanics, Los Angeles 2006.     

The recovery of design intent in reverse engineering problems

This article presents a method of reverse engineering applied to the particular case of a cam in order to recover the form and dimensions of the design of the original piece, which take into account: design intent, general knowledge of the problem, different geometric and dimensional restrictions, and the digitized point cloud. Rather than by employing complex mathematical algorithms, a fit is achieved by drawing a parametric outline that complies with the design intent, and by adjusting the different parameters through successive approximations using commercial CAD software commands.     

STEP-based product modeling system for remote collaborative reverse engineering

Production of high-quality products with lower cost and shorter time-to-market is an important challenge in the face of increased global competition, and reverse engineering plays an important role in accelerating product and process development. With the advent of new technologies such as network, multimedia and product data exchange standard STEP (STandard for Exchange of Product model data), there are many advantages to adopt these technologies to enhance the competitiveness of an enterprise. In this paper, a product information recording module for reverse engineering is developed to enhance the performance of product development. A STEP development tool, ST-Developer, and Visual C++ were used to develop this module, which can be used to record key information expeditiously during a collaborative process, and can also be used for further exchange of information, or as the basis for manufacturability evaluation. In this paper, the developed STEP-based information recording system is further integrated with the conventional Computer Supported Cooperative Work methods such as videoconferencing and application-sharing to form a remote collaborative reverse engineering system, which can provides a new strategy for an enterprise to speed up the product development cycle, reducing production cost, as well as sharing knowledge and experience.     

Decomposing the problem of constrained surface fitting in reverse engineering

This paper presents a practical solution for surface fitting problems with prioritized geometry constraints in reverse engineering. The approach allows prioritizing constraints and uses them for decomposing the problem into a set of sequentially solved, manageable sub-problems. The result of each solution step is trade-off between satisfying the set of constraints and fitting of the surfaces to the measured points. The overall solution process trades off solution quality with complexity of the problem. Solution quality is checked against pre-defined tolerances assigned to the geometry constraints. Results on a benchmark problem demonstrate the suitability of the approach to solving large problems with hundreds of surfaces and constraints.     

A sensing strategy for the reverse engineering of machined parts

The reverse engineering of machined parts requires sensing an existing part and producing a design (and perhaps a manufacturing process) for it. We have developed a reverse engineering system that has proven effective with a set of machined parts. This paper describes the system, presents some results, and discusses strategy for a new system.This work was supported by ARPA under ARO grant number DAAH04-93-G-0420, DARPA grant N00014-91-J-4123, NSF grant CDA 9024721, and a University of Utah Research Committee grant. All opinions, findings, conclusions or recommendations expressed in this document are those of the authors and do not necessarily reflect the views of the sponsoring agencies.     

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.     

An algorithm for filling complex holes in reverse engineering

The existence of holes in meshes makes it difficult for mesh operations, especially when comes to model rebuilding, rapid prototyping and finite element analysis. Existing hole-filling algorithms are capable of filling holes on small and smooth regions of a model. For large holes with complex boundaries or in curved region, they may not result in satisfactory results. This paper proposes an algorithm which first split the holes into flatter ones and then split the complex holes based the concept of edge expansion. It incrementally splits a complex hole into simple ones by respecting the 3D shape of its boundary and the neighboring meshes, and then fills each resulting simple hole with planar triangulation. The proposed algorithm works well for a variety of complex holes and can better preserve the detailed features of the original mesh.     

Towards recovery of complex shapes in meshes using digital images for reverse engineering applications

When an object owns complex shapes, or when its outer surfaces are simply inaccessible, some of its parts may not be captured during its reverse engineering. These deficiencies in the point cloud result in a set of holes in the reconstructed mesh. This paper deals with the use of information extracted from digital images to recover missing areas of a physical object. The proposed algorithm fills in these holes by solving an optimization problem that combines two kinds of information: (1) the geometric information available on the surrounding of the holes, (2) the information contained in an image of the real object. The constraints come from the image irradiance equation, a first-order non-linear partial differential equation that links the position of the mesh vertices to the light intensity of the image pixels. The blending conditions are satisfied by using an objective function based on a mechanical model of bar network that simulates the curvature evolution over the mesh. The inherent shortcomings both to the current hole-filling algorithms and the resolution of the image irradiance equations are overcome.     

Multiresolution model based extraction of product feature lines in reverse engineering

In this paper, multiresolution models are employed in the context of reverse engineering for feature line extraction. Starting with a proper triangulation of the cloud point data as a priori, our feature line extraction algorithm has three steps: (1) establishing a Gauss normal sphere and creating multiresolution models for the Gauss sphere based on different levels of subdivisions for the sphere regions; (2) mapping the unit normal vectors of triangular faces in the multiresolution Gauss sphere and merging those connected triangular faces whose unit normal vectors fallen on the same Gauss sphere region with a given resolution to form super-faces (the collection of triangular facets with similar normal); and (3) extracting the boundaries from the super-faces and generating the feature lines from the extracted boundaries. We then use these feature lines as a base for tracing boundary curves for B-spline surface construction. Since feature lines maintain the characteristics of the original product models, in this way, we have a good chance to reconstruct B-spline surfaces with high quality. Examples are given in the paper to show the feature line extraction, the influence of the feature lines extracted under different resolutions, and the final reconstructed B-spline surfaces based on these feature lines.     

A framework for 3D model reconstruction in reverse engineering

We present a framework for 3D model reconstruction, which has potential applications to a spectrum of engineering problems with impacts on rapid design and prototyping, shape analysis, and virtual reality. The framework, composed of four main components, provides a systematic solution to reconstruct geometric model from the surface mesh of an existing object. First, the input mesh is pre-processed to filter out noise. Second, the mesh is partitioned into segments to obtain individual geometric feature patches. Then, two integrated solutions, namely solid feature based strategy and surface feature based strategy, are exploited to reconstruct primitive features from the segmented feature patches. Finally, the modeling operations, such as solid boolean and surface trimming operations, are performed to “assemble” the primitive features into the final model. The concepts of “feature”, “constraint” and “modeling history” are introduced into the entire reconstruction process so that the design intents are retrieved and exhibited in the final model with geometrical accuracy, topological consistency and flexible editability. A variety of industrial parts have been tested to illustrate the effectiveness and robustness of our framework.     

Three-dimensional optical measurements and reverse engineering for automotive applications

The paper describes a very special and suggestive example of optical three-dimensional (3D) acquisition, reverse engineering and rapid prototyping of a historic automobile, a Ferrari 250 Mille Miglia, performed primarily using an optical 3D whole-field digitiser based on the projection of incoherent light (OPL-3D, developed in our laboratory). The entire process consists in the acquisition, the point cloud alignment, the triangle model definition, the NURBS creation, the production of the STL file, and finally the generation of a scaled replica of the car.The process, apart from the importance of the application to a unique, prestigious historic racing car, demonstrates the ease of application of the optical system to the gauging and the reverse engineering of large surfaces, as automobile body press parts and full-size clays, with high accuracy and reduced processing time, for design and restyling applications.     

A comprehensive process of reverse engineering from 3D meshes to CAD models

In an industrial context, most manufactured objects are designed using CAD (Computer-Aided Design) software. For visualization, data exchange or manufacturing applications, the geometric model has to be discretized into a 3D mesh composed of a finite number of vertices and edges. However, the initial model may sometimes be lost or unavailable. In other cases, the 3D discrete representation may be modified, e.g. after numerical simulation, and no longer corresponds to the initial model. A retro-engineering method is then required to reconstruct a 3D continuous representation from the discrete one.In this paper, we present an automatic and comprehensive retro-engineering process dedicated mainly to 3D meshes obtained initially by mechanical object discretization. First, several improvements in automatic detection of geometric primitives from a 3D mesh are presented. Then a new formalism is introduced to define the topology of the object and compute the intersections between primitives. The proposed method is validated on 3D industrial meshes.     

Empirical studies in reverse engineering: state of the art and future trends

Starting with the aim of modernizing legacy systems, often written in old programming languages, reverse engineering has extended its applicability to virtually every kind of software system. Moreover, the methods originally designed to recover a diagrammatic, high-level view of the target system have been extended to address several other problems faced by programmers when they need to understand and modify existing software. The authors’ position is that the next stage of development for this discipline will necessarily be based on empirical evaluation of methods. In fact, this evaluation is required to gain knowledge about the actual effects of applying a given approach, as well as to convince the end users of the positive cost–benefit trade offs. The contribution of this paper to the state of the art is a roadmap for the future research in the field, which includes: clarifying the scope of investigation, defining a reference taxonomy, and adopting a common framework for the execution of the experiments.     

Automatic least-squares projection of points onto point clouds with applications in reverse engineering

A novel method for projecting points onto a point cloud, possibly with noise, is presented based on the point directed projection (DP) algorithm proposed by Azariadis P., Sapidis N. [Drawing curves onto a cloud of points for point-based modelling. Computer-Aided Design 2005; 37(1): 109–22]. The new method operates directly on the point cloud without any explicit or implicit surface reconstruction procedure. The presented method uses a simple, robust, and efficient algorithm: least-squares projection (LSP), which projects points onto the point cloud in a least-squares sense without any specification of the projection vector. The main contribution of this novel method is the automatic computation of the projection vector. Furthermore, we demonstrate the effectiveness of this approach through a number of application examples including thinning a point cloud, point normal estimation, projecting curves onto a point cloud and others.     

Re-engineering of the design process for concurrent engineering

According to the requirements of concurrent engineering, three interdependency relationships (uncoupled relationship, coupled relationship and decoupled relationship) between design activities are presented in this paper. And the coupled relationship plays an important role in concurrent product design process. To represent the precedence relationships among design activities, a directed graph is used to describe the design process. And the interdependency relationship between activities is illustrated by a Design Structure Matrix which is the transpose of the accessibility matrix of the corresponding graph. Using the DSM, an algorithm of recognizing the coupled activities during the design process is presented. Moreover, an algorithm to figure out the order levels of activities during the design process is proposed. And both algorithms are illustrated with a die design example.     

逆向工程中测量数据曲面重构技术研究

文中首先介绍了自由曲面的描述方式,并提出了一种对点云数据进行NURBS曲面重建的改进算法。该算法通过对目标函数的修正及迭代处理,将测点数据的有理B样条曲线拟合问题转化为一单变量的优化问题。从而容易求得控制点三维坐标、权值、各测点所对应的参数值及最优控制点的数目,进而有效地压缩测量点云数据量。该方法克服了现有方法存在的需要求解多变量的非线性优化问题,不能对最优控制点数目进行求取等缺陷。实践表明,文中提出的方法是行之有效的。     

An empirical study of a reverse engineering method for the aggregation relationship based on operation propagation

One of the major obstacles in reverse engineering legacy object-oriented systems is the identification of aggregation relationships. An aggregation relationship, also called whole–part relationship, is a form of association relationship where an object is considered as a part of another object. This characteristic is mostly of semantic nature; therefore, it is difficult to distinguish aggregation from association relationships by implementation mechanism. Most reverse engineering methods for aggregation relationships are based on the lifetime dependence of an object on another object since many implementations of aggregation relationships result in such dependence. However, research literature shows that lifetime dependence is not really a primary property of the aggregation relationships. A reverse engineering approach is proposed on the basis of a primary characteristic for aggregation relationship—propagation of operations. To compare the propagation-based method with the lifetime-based method, we apply both methods to ten class libraries, collect their output, and perform statistical analysis to determine the effectiveness of the two methods. The analysis results show that the propagation-based method performs significantly better than the lifetime-based method and by combining both methods simultaneously the complete aggregation relationships can be uncovered for the class libraries in our experiment.

Point estimates for variance-structure parameters in Bayesian analysis of hierarchical models

Markov chain Monte Carlo (MCMC) made Bayesian analysis feasible for hierarchical models, but the literature about their variance parameters is sparse. This is particularly so for point estimators of variance-structure parameters, which are useful for simplifying tables and sample-size calculations, and as “plug-in” estimators in complex calculations. This paper uses simulation experiments to compare three such point estimators, the posterior mode, median, and mean, for three parameterizations of the variance structure, as precisions, standard deviations, and variances. We first consider simple linear regression, where fairly explicit expressions are possible, and then three more complex models: crossed random effects, smoothed analysis of variance (SANOVA), and the conditional autoregressive (CAR) model with two classes of neighbor relations. We illustrate the latter results using periodontal data. The posterior mean often performs poorly in terms of bias and mean-squared error, and should be avoided. The posterior median never performs worse than the mean and often performs far better. The surprise is that, on the whole, the posterior mode performs best regardless of the variance structure's parameterization, although the potential for multi-modality may make it unattractive for general use.     

Machine learning-based optimization of process parameters in selective laser melting for biomedical applications

Journal of Intelligent Manufacturing - Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high...