The multi-objective genetic algorithm optimization, of a superplastic forming process, using ansys<sup>®</sup>

Grebenişan, Gavril; Salem, Nazzal

doi:10.1051/matecconf/201712603003

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…The most significant candidate design point is obtained by searching for the best objective function value over the vast feasible region of the design spaces [26,44]. After optimization runs, the three (3) design candidates are determined, as shown in Table 7.…”

Section: Response Surface Optimization (Rso) In the Staticmentioning

confidence: 99%

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Addisu

Koricho

2022

Modelling and Simulation in Engineering

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Midibuses are medium-sized buses widely used for transportation purposes in Asia and Africa. However, most midibuses are locally built and indirectly regulated through inspecting the end product (finished bus) during licensing for the public transport business in Ethiopia. Due to lack of engineering analysis and testing, low stiffness and overweight of midibus were compromised. This research was aimed at analyzing and optimizing the midibus structure using the reinforcement and response surface optimization (RSO) method for pure bending and torsion loading cases. Results show that the maximum deformation occurred at the roof section of the original structure during both loading cases. Furthermore, the reinforcement design was found by replacing the cross section and layouts of structural members and adding reinforcements for the most suitable location of the original structure. Response surface optimization with the multiobjective genetic algorithm (MOGA) method in ANSYS DesignXplorer was performed on the reinforced structure to maximize the bending and torsional stiffness with reduced weight. The bending stiffness of the reinforced and optimized structure increased by 41.65% (1911.4 N/m) and 10.02% (651.7 N/m), respectively. In addition, the torsional rigidity or stiffness of the bus structure was improved by 12.56% (173.31 Nm/deg) via reinforcement design. Moreover, the torsional stiffness of the optimized (RSO) model was increased by 3.29% (51.07 Nm/deg). Reinforcement design was effectively reduced by 5.23% of the structure’s weight. Moreover, the RSO method has also decreased the weight of the reinforced structure by 2.64%.

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Section: Response Surface Optimization (Rso) In the Staticmentioning

confidence: 99%

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Addisu

Koricho

2022

Modelling and Simulation in Engineering

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“…The system was thus tested to detect between the "n" elements to be taken into account simultaneously, and to evaluate in real time both the evolution and the trend of the monitored parameters, being "learned" to detect essential anomalies or possible failures . The overcoming of this early phase allowed the decision to install the system designed and physically installed on industrial installations (pilot systems) at EMSIL Techtrans SA-Romania, as well as in four other locations in Italy, to Mecoil partners, [6].…”

Section: Operational Objective: Integration Of the Whole System Protmentioning

confidence: 99%

“…In this respect, consideration was given to the evaluation of a Raspberry type interface, with emphasis on data acquisition and sensing response checking. In addition, TecService Italy's TecService Italy consultancy has been used to build a first-ever version of the Machine Intelligence for Diagnosis Automation (MIDA), with both industrial and IP67 protection restrictions for these work environments, [6], (Fig. 2).…”

Section: Fig 1 Monitoring System Monitoilmentioning

confidence: 99%

The lubricants' parameters monitoring and data collecting

2018

Self Cite

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This approach is focused on Machine Intelligence for Diagnosis Automation, a research program, which promotes « preventative maintenance in manufacturing plants through the development of a fully automated prototype for oil analysis and fault prediction. The prototype is based on Artificial Intelligence (A.I.) software and online hardware ». Monitoring the condition of lubricants requires the examination of the physical, chemical and additive states, which maintain the quality of the lubricants, which is necessary for the proper functioning of the equipment. A lubricant monitoring program, especially from a qualitative point of view, will need to focus on both machine tool wear and degradation of lubricants, as well as on detecting and describing abnormal working conditions for both lubricants and machine parts. This goal can be satisfied by examining all the oils used in a company by completing laboratory tests to generate steps and acceptance classes, as well as unplanned contingency analyzes. These laboratory tests can be concentrated and classified into technology-based data sheets based on test-based information and test results, ultimately constituting consistent databases needed to generate monitoring and prevention reports. Data on the condition of the oil parameters used in the hydraulic system for lubricating machine tools have been collected during six months. The data as matrix organized, with 258648 rows (observations) and 21 columns (parameters).

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“…This method was used in a study for the optimization of a super plastic forming process where the input parameters that were chosen were the sample diameter and thickness. This study highlighted the influence of inputs on the outputs, and prediction of the shape of product, optimal initial geometry, cracks and fracture [14]. There has been research on the use of MOGA in the design of pressure swing absorption, the effect of the parameters of the MOGA was studied and a comparison was made with different search methods to determine the efficiency of the algorithm [15].…”

Section: Introductionmentioning

confidence: 99%

Direct Optimization of an Automotive Sheet Metal Part Using ANSYS

Khalid

Mustafa

Naeem

et al. 2020

IJEMS

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Optimization of automotive parts nowadays is mainly used to design lightweight and cost-effective vehicle parts in order to improve the cost and efficiency. In this research, a sheet metal part was taken into consideration and optimized using direct optimization module in ANSYS to evaluate the process. An initial Finite Element Analysis (FEA) was done on the sheet metal part by adding forces and constraints in order to initiate direct optimization. The purpose of the optimization is to minimize the mass of the sheet metal part and maintaining a certain Factor of Safety (FOS) by automatically modifying the sheet thickness and the dimension of the side holes. As a result, the best candidate point with 23% mass reduction was found which complied with FOS value was selected for optimal geometry.

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The multi-objective genetic algorithm optimization, of a superplastic forming process, using ansys^®

Cited by 8 publications

References 5 publications

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

The lubricants' parameters monitoring and data collecting

Direct Optimization of an Automotive Sheet Metal Part Using ANSYS

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The multi-objective genetic algorithm optimization, of a superplastic forming process, using ansys®

Cited by 8 publications

References 5 publications

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

The lubricants' parameters monitoring and data collecting

Direct Optimization of an Automotive Sheet Metal Part Using ANSYS

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Product

Resources

About

The multi-objective genetic algorithm optimization, of a superplastic forming process, using ansys^®