Design Consideration for Additively Manufactured Components Through Topology Optimization and Generative Design for Weight Reduction

2021 ◽  
pp. 222-225
Author(s):  
Jian-Yuan Lee ◽  
Arun Prasanth Nagalingam ◽  
Swee Hock Yeo
Keyword(s):  
Topology Optimization ◽  
Weight Reduction ◽  
Design Consideration ◽  
Generative Design

scholarly journals Topology Optimization of Vehicle B-Pillar

2021 ◽  
Vol 9 (11) ◽  
pp. 384-392
Author(s):  
Manas Metar
Keyword(s):  
Topology Optimization ◽  
Structural Optimization ◽  
Weight Reduction ◽  
Optimization Techniques ◽  
Vehicle Body ◽  
Element Analysis ◽  
Reduction Techniques ◽  
Roof Structure ◽  
Original Weight ◽  
3D Software

Abstract: Weight reduction techniques have been practiced by automobile manufacturers for the purpose of long range, less fuel consumption and achieving higher speeds. Due to the numerous set objectives that must be met, especially with respect to of car safety, automotive chassis design for vehicle weight reduction is a difficult task. In passenger classed vehicles using a monocoque chassis for vehicle construction has been a great solution for reducing overall wight of the vehicle body yet the structure is more stiffened and sturdier. However, some parts such as A-pillar, B-pillar, roof structure, floor pan can be further optimized to reduce more weight without affecting the strength needed for respective purposes. In this paper, the main focus is on reducing weight of the B-pillar. The B-pillar of a passenger car has been optimized using topology optimization and optimum weight reduction has been done. The modelling and simulation are done using SOLIDWORKS 3D software. The B-pillar in this study has been subjected to a static load of 140 KN. Further by providing goals and constraints the optimization was caried out. The results of Finite Element Analysis (FEA) of the original model are explained. The Topology Optimization resulted in reducing 53% of the original weight of the B-pillar. Keywords: Structural optimization techniques, weight reduction techniques, weight reduction technologies, need for weight reduction, Topology optimization, B-pillar design, structural optimization of B-pillar, Topology optimization of B-pillar.

scholarly journals MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization

2021 ◽  
Vol 11 (24) ◽  
pp. 12044
Author(s):  
Nikos Ath. Kallioras ◽  
Nikos D. Lagaros
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Architectural Design ◽  
Parametric Design ◽  
Optimization Problems ◽  
Design Procedure ◽  
Performance Criteria ◽  
Design Framework ◽  
Generative Design ◽  
New Era

Design and manufacturing processes are entering into a new era as novel methods and techniques are constantly introduced. Currently, 3D printing is already established in the production processes of several industries while more are continuously being added. At the same time, topology optimization has become part of the design procedure of various industries, such as automotive and aeronautical. Parametric design has been gaining ground in the architectural design literature in the past years. Generative design is introduced as the contemporary design process that relies on the utilization of algorithms for creating several forms that respect structural and architectural constraints imposed, among others, by the design codes and/or as defined by the designer. In this study, a novel generative design framework labeled as MLGen is presented. MLGen integrates machine learning into the generative design practice. MLGen is able to generate multiple optimized solutions which vary in shape but are equivalent in terms of performance criteria. The output of the proposed framework is exported in a format that can be handled by 3D printers. The ability of MLGen to efficiently handle different problems is validated via testing on several benchmark topology optimization problems frequently employed in the literature.

Weight reduction of motorcycle frame by topology optimization

2018 ◽  
Vol 2 (92) ◽  
pp. 67-77
Author(s):  
C. Bala Manikandan ◽  
S. Balamurugan ◽  
P. Balamurugan ◽  
S. Lionel Beneston
Keyword(s):  
Topology Optimization ◽  
Weight Reduction ◽  
Metal Removal ◽  
Fuel Efficiency ◽  
Optimization Technique ◽  
Innovative Design ◽  
And Performance ◽  
Motorcycle Frame ◽  
Performance Research ◽  
Practical Implications

Purpose: of this paper is to improve the fuel efficiency of electrical motorcycle by reducing the weight of its frame without affecting the basic functionalities, dimensions and performance. Design/methodology/approach: Weight reduction of the frame was achieved by topology optimization technique. Initially the load and stresses acting on the frame was studied. Material of the frame was chosen as Aluminium and the frame was geometrically modelled using Autodesk Fusion 360. With the help of ANSYS AIM 18.2, weight of the frame was optimized by the design modifications suggested by the concept of topology optimization, for the corresponding loads and stresses induced on it. It was observed that the stress induced on the modified design was lesser than that of respective permissible yield stress of the frame material. After optimization, the weight of the frame was reduced from 3.0695 kg to 2.215 kg with the weight reduction of 27.84%. The weight reduction shows that the topology optimization is an effective technique, without compensate the performance of the frame. Approach used in the paper for the weight reduction of the frame is the topology optimization. The modelled frame was topology optimized by using ANSYS 18.2. After the topology optimization, the regions where the metal removal is possible, for weight reduction was identified. Findings: In this paper, the motor cycle frame was optimized and weight of the frame was reduced from 3.065 kg to 2.215 kg. Weight reduction of 27.84% was achieved without compensating the performance. Research limitations/implications: All the components of the automobile may be topology optimized for the weight reduction, thereby improving the fuel efficiency. Innovative design/Improvement in design also possible. Practical implications: By reducing the weight of the frame, weight of the automobile also reduces. Reduction in weight of the automobile leads to improved fuel efficiency. Originality/value: Weight of the motorcycle frame reduced by topology optimization. The regions of material removal at the frame, without compensating the performance was identified.

scholarly journals Deep Generative Design: Integration of Topology Optimization and Generative Models

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Sangeun Oh ◽  
Yongsu Jung ◽  
Seongsin Kim ◽  
Ikjin Lee ◽  
Namwoo Kang
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Research Area ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Design ◽  
Design Data ◽  
Research Areas ◽  
Design Integration ◽  
Design Options

Abstract Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based deep generative design framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

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