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Reverse engineering by CAD template fitting: study of a fast and robust template-fitting strategy

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Abstract

This paper considers the fitting of a CAD template model to tessellated data as strategy to implement a reverse engineering process that aims at the reconstruction of a parametric associative CAD model. The reconstruction methodology, called Template-Based CAD Reconstruction (TCRT), has been presented and fully discussed in a previous paper Buonamici et al. (J Comput Des Eng 5:145–159, 2018). The present paper focuses on the study of a fast and robust strategy to perform the fitting of the Template CAD Model to reference data. The study explores how different optimization strategies and evaluation metrics can affect a parametric CAD-fitting methodology. Two different optimization algorithms (PSO and GA) and three formulations of the objective function are tested to find the most effective combination. Reconstruction test cases are presented and discussed in the text.

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Notes

  1. This could happen for multiple reasons: some dimensions could be standard or derived from the function of the object.

  2. The description of implementation details falls outside the scopes of this paper.

  3. NF is estimated as the minimum error that is significant for the specific reconstruction.

  4. Upper/Lower bounds are actually imposed for some parameters: all linear dimensions, for example, have zero as lower bound; for the upper bound, the main diagonal’s length of the Reference data’s bounding box is used as reference value. Angles, on the other hand, are bounded in a [0, 360]° interval. No particular bounds are imposed for the positional parameters. The TCRT automatically identifies each type of parameter and assigns its correct options.

  5. A population of possible solutions is updated at each iteration according to the results obtained by the population at previous iterations. A fixed number of function evaluations is performed at each iteration.

  6. Software: Microsoft operating system (i.e. Windows 7), Siemens NX 10 and MATLAB R2018a. Hardware: 128 GBs RAM workstation supplied with two six-core Intel® Xeon® E5-2643 v3 processors, each of which can manage up to 12 threads simultaneously at 3.40 GHz. All the tests have been executed using 22 simultaneous NX instances running in parallel.

  7. GFE values that are under the goal line in Fig. 11 (\({f}_{3}\) metric) are caused by the post-processing of data (uniformization in corresponding \({f}_{1}\) values).

  8. Gradient-based algorithms are not suited for the final refinement, due to the presence, even at this stage, of local minima. Pattern Search was chosen by the authors as it seemed fit to tackle this specific reconstruction problem.

Abbreviations

TCRT:

Template-based CAD Reconstruction Tool

GFE:

Global Fitting Error

PSO:

Particle Swarm Optimization

GA:

Genetic Algorithm

References

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Florence, Via di Santa Marta 3, 50139, Florence, Italy

    Francesco Buonamici, Monica Carfagni, Rocco Furferi, Yary Volpe & Lapo Governi

Authors
  1. Francesco Buonamici
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  2. Monica Carfagni
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  3. Rocco Furferi
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  4. Yary Volpe
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  5. Lapo Governi
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Correspondence to Francesco Buonamici.

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Buonamici, F., Carfagni, M., Furferi, R. et al. Reverse engineering by CAD template fitting: study of a fast and robust template-fitting strategy. Engineering with Computers 37, 2803–2821 (2021). https://doi.org/10.1007/s00366-020-00966-4

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