Packing 3D-Models of Products in Build Space of Additive Manufacturing Machine by Genetic Algorithm

2021 ◽  
pp. 67-77
Author(s):  
Yaroslav Garashchenko ◽  
Jurii Vitiaziev ◽  
Igor Grimzin
Keyword(s):  
Genetic Algorithm ◽  
Additive Manufacturing ◽  
3D Models ◽  
Manufacturing Machine

Automatic Parametric Modeling From Non-Feature Based Designs for Additive Manufacturing

2021 ◽  
Author(s):  
Xinyi Xiao ◽  
Byeong-Min Roh
Keyword(s):  
Additive Manufacturing ◽  
3D Models ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Performance Simulation ◽  
Cad Modeling ◽  
3D Cad ◽  
Feature Based ◽  
And Performance ◽  
The Given

Abstract The integration of Topology optimization (TO) and Generative Design (GD) with additive manufacturing (AM) is becoming advent methods to lightweight parts while maintaining performance under the same loading conditions. However, these models from TO or GD are not in a form that they can be easily edited in a 3D CAD modeling system. These geometries are generally in a form with no surface/plane information, thus having non-editable features. Direct fabricate these non-feature-based designs and their inherent characteristics would lead to non-desired part qualities in terms of shape, GD&T, and mechanical properties. Current commercial software always requires a significant amount of manual work by experienced CAD users to generate a feature-based CAD model from non-feature-based designs for AM and performance simulation. This paper presents fully automated shaping algorithms for building parametric feature-based 3D models from non-feature-based designs for AM. Starting from automatically decomposing the given geometry into “formable” volumes, which is defined as a sweeping feature in the CAD modeling system, each decomposed volume will be described with 2D profiles and sweeping directions for modeling. The Boolean of modeled components will be the final parametric shape. The volumetric difference between the final parametric form and the original geometry is also provided to prove the effectiveness and efficiency of this automatic shaping methodology. Besides, the performance of the parametric models is being simulated to testify the functionality.

scholarly journals A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

2020 ◽  
Vol 12 (14) ◽  
pp. 2283
Author(s):  
Rushikesh Battulwar ◽  
Garrett Winkelmaier ◽  
Jorge Valencia ◽  
Masoud Zare Naghadehi ◽  
Bijan Peik ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
High Resolution ◽  
Power Consumption ◽  
Fitness Function ◽  
Empirical Studies ◽  
3D Models ◽  
Open Pit ◽  
High Resolution Imagery ◽  
High Resolution Images ◽  
Using Data

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

Channel design for 3D models with applications in powder-bed additive manufacturing

2019 ◽  
Vol 25 (9) ◽  
pp. 1536-1544
Author(s):  
Xiangzhi Wei ◽  
Xianda Li ◽  
Shanshan Wen ◽  
Yu Zheng ◽  
Yaobin Tian
Keyword(s):  
Additive Manufacturing ◽  
3D Model ◽  
3D Models ◽  
Linear Constraints ◽  
Channel Design ◽  
Channel Network ◽  
Content Type ◽  
Powder Bed ◽  
Shortest Network ◽  
Manufacturing Techniques

Purpose For any 3D model with chambers to be fabricated in powder-bed additive manufacturing processes such as SLM and SLS, powders are trapped in the chambers of the finished model. This paper aims to design a shortest network with the least number of outlets for efficiently leaking the trapped powders. Design/methodology/approach This paper proposes a nonlinear objective with linear constraints for solving the channel design problem and a particle swarm optimization algorithm to solve the nonlinear system. Findings Structural optimization for the channel network leads to fairly short channels in the interior of the 3D models and very few outlets on the model surface, which achieves the cleaning of the powders while causing almost the least changes to the model. Originality/value This paper reveals the NP-harness of computing the shortest channel network with the least number of outlets. The proposed approach helps the design of lightweight models using the powder-bed additive manufacturing techniques.

Parallel non-dominated sorting genetic algorithm-II for optimal part deposition orientation in additive manufacturing based on functional features

2017 ◽  
Vol 232 (19) ◽  
pp. 3384-3395 ◽  
Author(s):  
Renkai Huang ◽  
Ning Dai ◽  
Dawei Li ◽  
Xiaosheng Cheng ◽  
Hao Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Additive Manufacturing ◽  
Surface Quality ◽  
Surface Finish ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Industrial Grade ◽  
Build Time ◽  
Functional Features

Surface finish, especially the surface finish of functional features, and build time are two important concerns in additive manufacturing. A suitable part deposition orientation can enhance the surface quality of functional features and reduce the build time. This article proposes a novel method to obtain an optimum part deposition orientation for industrial-grade 3D printing based on fused deposition modeling process by considering two objective functions at a time, namely adaptive feature roughness (the weighted sum of all feature roughnesses) and build time. First, mesh segmentation and level classification of features are carried out. Then, models for evaluation of adaptive feature roughness and build time are established. Finally, a non-dominated sorting genetic algorithm-II based on Compute Unified Device Architecture is used to obtain the Pareto-optimal set. The feasible of the algorithm is evaluated on several examples. Results demonstrate that the proposed parallel algorithm obtains a limiting solution that enhances the surface quality of functional features significantly and reduces average running time by 94.8% compared with the traditional genetic algorithm.

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