An approach towards energy and material efficient additive manufacturing: Multi-objective optimization of stellite-6 deposition on SS304

2022 ◽  
Vol 148 ◽  
pp. 107799
Author(s):  
Anitesh Kumar Singh ◽  
Abhijit Sadhu ◽  
Amit Kumar Das ◽  
Dilip Kumar Pratihar ◽  
Asimava Roy Choudhury
Keyword(s):  
Additive Manufacturing ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Stellite 6

scholarly journals Multi-Objective Optimization of Support Structures for Metal Additive Manufacturing

2021 ◽  
Author(s):  
Wadea Ameen Qaid ◽  
Abdulrahman Al-Ahmari ◽  
Muneer Khan Mohammed ◽  
Husam Kaid
Keyword(s):  
Additive Manufacturing ◽  
Optimal Solution ◽  
Beam Current ◽  
Support Structures ◽  
Multi Objective Optimization ◽  
Metal Additive Manufacturing ◽  
Multi Objective ◽  
Removal Time ◽  
Tooth Base ◽  
Metal Additive

Abstract Electron-beam melting (EBM) is a rapidly developing metal additive manufacturing (AM) method. It is more effective with complex and customized parts manufactured in low volumes. In contrast to traditional manufacturing it offers reduced lead time and efficient material management. However, this technology has difficulties with regard to the construction of overhang structures. Production of overhangs using EBM without support structures results in distorted objects, and the addition of a support structure increases the material consumption and necessitates post-processing. The objective of this study was to design support structures for metal AM that are easy to remove and consume lower support material without affecting the quality of the part. The design of experiment methodology was incorporated to evaluate the support parameters. The multi-objective optimization minimizing support volume, support removal time along with constrained deformation was performed using multi objective genetic algorithm (MOGA-II). The optimal solution was characterized by a large tooth height (4 mm), large tooth base interval (4 mm), large fragmented separation width (2.5 mm), high beam current (6 mm), and low beam scan speed (1200 mm/s).

scholarly journals Multi-Objective Optimization of Additive Manufacturing Process

2018 ◽  
Vol 51 (11) ◽  
pp. 152-157 ◽  
Author(s):  
Elnaz Asadollahi-Yazdi ◽  
Julien Gardan ◽  
Pascal Lafon
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

2020 ◽  
Vol 10 (15) ◽  
pp. 5159
Author(s):  
Kasin Ransikarbum ◽  
Rapeepan Pitakaso ◽  
Namhun Kim
Keyword(s):  
Decision Support ◽  
Additive Manufacturing ◽  
Analytic Hierarchy Process ◽  
Supply Chain Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Multi Objective ◽  
Trade Offs ◽  
Hierarchy Process

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

Pressure Loss and Multi-Objective Optimization of Three-Way Spatial Flow Channel Based on Additive Manufacturing

2021 ◽  
Author(s):  
Jing Yao ◽  
Yiman Duan ◽  
Yingzhe Song ◽  
Hao Zhang ◽  
Mandi Li ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Pressure Loss ◽  
Path Length ◽  
Optimization Design ◽  
Structural Parameters ◽  
Mathematic Model ◽  
Flow Channel ◽  
Structure Model ◽  
Multi Objective Optimization ◽  
Multi Objective

Abstract Three-way spatial fluid channel (TSFC) is commonly used in spatial fluid channels of the high hydraulic integrated system. However, the mathematic model of TSFC pressure loss is not clear, and what TSFC structural parameters in a certain space can get the minimum pressure loss and weight is also vague. Therefore, TSFC pressure loss mathematic model and multi-objective optimization about pressure loss, axis path length, and mass are studied in this paper. First, the mathematic model of TSFC pressure loss is established based on the response surface methodology and pressure loss models of fluid dynamics. Then, the optimized mathematic model for the TSFC structural parameters is built by the multi-objective optimization design method to achieve low pressure loss, short axis path length, and lightweight. According to the simulation, the results of optimized structure model show that the mass has been reduced 5.68%, and the pressure loss has been reduced 70.75% compared with the original model. Besides, the optimized TSFC structure model is manufactured by additive manufacturing, and the experiment is carried out to measure TSFC pressure loss. It shows that the error of pressure loss between the mathematic model and the experiment is only 1.6%, which verifies the accuracy of mathematic model of the pressure loss. This research lays a foundation for the design and optimization of the spatial flow channel in highly integrated hydraulic systems.

scholarly journals Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Mark A. Tschopp ◽  
Prahalad K. Rao ◽  
Linkan Bian
Keyword(s):  
Experimental Data ◽  
Additive Manufacturing ◽  
Process Optimization ◽  
Process Parameters ◽  
Process Conditions ◽  
Optimization Approach ◽  
Geometric Accuracy ◽  
Multi Objective Optimization ◽  
Fused Filament Fabrication ◽  
Multi Objective

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

Sign in / Sign up

Export Citation Format

Share Document