Characterizing the effects of additive manufacturing process settings on part performance using approximation-assisted multi-objective optimization

2018 ◽  
Vol 3 (3) ◽  
pp. 123-143 ◽  
Author(s):  
J. M. Hamel ◽  
C. Salsbury ◽  
A. Bouck
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Multi Objective Optimization ◽  
Multi Objective

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

Multi-Objective Implementation of Additive Manufacturing in Make-to-Stock Production

2020 ◽  
Author(s):  
Ping Chong Chua ◽  
Seung Ki Moon ◽  
Yen Ting Ng
Keyword(s):  
Additive Manufacturing ◽  
Performance Measures ◽  
Manufacturing Process ◽  
Rule Based ◽  
Multi Objective ◽  
Implementation Problem ◽  
Conventional Production ◽  
Volume Production ◽  
Make To Stock ◽  
Production Orders

Abstract As additive manufacturing (AM) develops and matures over the years, it has reached a stage where implementation into a conventional production system becomes possible. With additive manufacturing currently suitable for product personalization/high customization yet small volume production, there are various ways of implementation in a conventional production line. This aim of this paper is to explore the implementation of additive manufacturing in a complementary manner to process internal job orders of large quantities in make-to-stock (MTS) production. Splitting of production orders is allowed and production can be carried out by both injection moulding and additive manufacturing processes simultaneously, with the latter being able to produce various MTS parts in a single build. NSGA-III together with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process is used to solve the multi-objective implementation problem, with performance measures being cost, scheduling and sustainability. The algorithm will be incorporated with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process. An experiment using an industry case study is conducted to compare the performance measures with and without implementing additive manufacturing.

scholarly journals Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67576-67588 ◽  
Author(s):  
Gelayol Golkarnarenji ◽  
Minoo Naebe ◽  
Khashayar Badii ◽  
Abbas S. Milani ◽  
Ali Jamali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Carbon Fiber ◽  
Manufacturing Process ◽  
Multi Objective Optimization ◽  
Artificial Intelligence Techniques ◽  
Multi Objective

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).

Multi-Objective Optimization Problems in Taguchi Parameter Design – A Literature Review

2015 ◽  
Vol 813-814 ◽  
pp. 1188-1192
Author(s):  
S. Rajarasalnath ◽  
K. Balasubramanian ◽  
N. Rajeswari
Keyword(s):  
Real Time ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Taguchi Methods ◽  
Quality Characteristic ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Minimum Number ◽  
Set Up ◽  
Single Response

With the advent of latest technology, manufacturing process becomes so sophisticated and complicated that a single response variable (quality characteristic) can not reflect the true product quality and there is intense competition between market participants for cost and delivery (productivity) as well. Any manufacturing process in the present state requires multi objective optimization model to optimize quality, cost and productivity simultaneously. Optimum prediction is critical as it requires lot of experiments for data capturing which involves time and cost. In the present manufacturing set up, it is essential to identify optimum parametric combination for multi objective function real time problems with lesser experiments and lesser effort with better accuracy. Taguchi method is a well known fraction factorial design, which requires minimum number of trials for Identifying optimum parametric combination in real time problems.In this paper an attempt is made to review the literatures of various methods used by researchers for multi - objective optimization problems using Taguchi methods.

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.

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