A survey of feature modeling methods: Historical evolution and new development

Li, Lei; Zheng, Yufan; Yang, Maolin; Leng, Jiewu; Cheng, Zhengrong; Xie, Yanling; Jiang, Pingyu; Ma, Yongsheng

doi:10.1016/j.rcim.2019.101851

Cited by 36 publications

(9 citation statements)

References 132 publications

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“…Due to the fact that the material is built layer by layer, a typical staircase error is caused when the shape does not align with the printing orientation [4] (Figure 1). The staircase error can be reduced by using thinner slices at the cost of more printing layers and building time [15][16][17]. [16]; (c) model proposed by Kumar and Kumar [17].…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Investigation of Poly-Jet 3D Printing

et al. 2020

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An experimental investigation of the surface quality of the Poly-Jet 3D printing (PJ-3DP) process is presented. PJ-3DP is an additive manufacturing process, which uses jetted photopolymer droplets, which are immediately cured with ultraviolet lamps, to build physical models, layer-by-layer. This method is fast and accurate due to the mechanism it uses for the deposition of layers as well as the 16 microns of layer thickness used. Τo characterize the surface quality of PJ-3DP printed parts, an experiment was designed and the results were analyzed to identify the impact of the deposition angle and blade mechanism motion onto the surface roughness. First, linear regression models were extracted for the prediction of surface quality parameters, such as the average surface roughness (Ra) and the total height of the profile (Rt) in the X and Y directions. Then, a Feed Forward Back Propagation Neural Network (FFBP-NN) was proposed for increasing the prediction performance of the surface roughness parameters Ra and Rt. These two models were compared with the reported ones in the literature; it was revealed that both performed better, leading to more accurate surface roughness predictions, whilst the NN model resulted in the best predictions, in particular for the Ra parameter.

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Section: Introductionmentioning

confidence: 99%

Surface Roughness Investigation of Poly-Jet 3D Printing

et al. 2020

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“…In this system, the modeling scheme and design parameters should be firstly defined based on the characteristics of directed energy deposition (DED) and optimization goals. According to the model features [7] and initial conditions, the intelligent allocation of fluid physics models is achieved, and the accurate CFD simulation results of different design points are obtained after computation. Based on the CFD results, the design optimization of flow channels under constraints is conducted to achieve the optimization objectives.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Zou

et al. 2022

Micromachines

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Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

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“…The compact representation of the identified reusable functions assists more in apprehending the software requirements when we place them as features in a feature model. A feature model defines the features and their dependencies in a tree structure [23]. This model-based software development helps to visualize required features in a tree structure considering various aspects, including testability, code generation, traceability, reusability, testing workflow, constraints verification, and validation.…”

Section: Introductionmentioning

confidence: 99%

Facilitating Reuse of Functions in Embedded Software

2022

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Developing software-intensive embedded systems is a significant challenge as embedded systems have become more complex and dynamic for integrating various constraints (e.g., real-time performance, design, and functional safety). In general, software practitioners reuse code to reduce development time for implementing required functionalities. However, embedded software development still lacks code reuse on a large scale due to its intricate constraints. This paper presents an approach to identifying reusable functions and their relationships from the legacy embedded software for faster development reducing functionality testing time. A python tool is developed to automatically extract the reusable functions and present them in a feature model. The search time for finding reusable functions speeds up when they are placed as features in a feature model. We conducted experiments on three GitHub embedded software projects (elevator, infotainment, and health monitoring systems). The experimental results find that our proposed approach reduces the feature search time by 59.0%, 49.8%, and 74.5%, respectively, compared to the manual searching approach for 50 features.

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A survey of feature modeling methods: Historical evolution and new development

Cited by 36 publications

References 132 publications

Surface Roughness Investigation of Poly-Jet 3D Printing

Surface Roughness Investigation of Poly-Jet 3D Printing

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Facilitating Reuse of Functions in Embedded Software

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