Knowledge-Based Engineering–based method for containership lashing bridge optimization design and structural improvement with functionally graded thickness plates

Abstract

This article presents a Knowledge-Based Engineering application method for the optimization design of containership lashing bridge. To reduce the structural vibration response of lashing bridges, a structural improvement method was proposed based on the results of optimization. The adoption of design specifications, expertise and other knowledge accumulated in design have been ignored in the development of structure design and optimization. To face the increasingly fierce competition in the marine market, the optimal solution is establishing a knowledge base containing expertise, design specification and successful design cases. In addition, the advanced techniques for design and optimization shall be incorporated with Knowledge-Based Engineering to automate new designs and optimizations. Furthermore, finite element method is employed to carry out the strength analysis. Lightweight design and vibration damping design of the lashing bridge are deemed as two critical issues having nagged designers. The Knowledge-Based Engineering–based design and optimization approach not only ensure the performance of the lashing bridge structure to be reasonable but also minimize the weight target and evidently improves the design quality and efficiency. The functionally graded thickness structure is applied to the improved scheme, which not only makes the design of the lashing bridge structure more reasonable but also reduces the vibration response of the structure.

Keywords

Knowledge-Based Engineering lashing bridge design optimization structures functionally graded thickness

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References

Wolf

Darie

Rathjed

Rule development for container stowage on deck. In: Proceedings of the 3rd international conference on marine structures, Hamburg, 28–30 March 2011. London: Taylor & Francis Group, vol. 1, pp.715–722.

Kirkayak

Suzuki

Numerical analysis of container stacks dynamics. In: Proceedings of the Third PAAMES and AMEC, Chiba, Japan, 20–22 October 2008. Japan: JASNAOE, vol. 3, pp.205–208.

Suzuki

Kirkayak

Sueoka

et al . Model test of container lashing dynamic behavior. In: Proceedings of the conference of the Japan Society of Naval Architects and Ocean Engineers, 1–2 September 2009. Japan: JASNAOE, vol. 8, pp.465–466.

Kirkayak

Suzuki

Sueoka

et al . Dynamic simulation of container lashing behavior. In: Proceedings of the conference of the Japan Society of Naval Architects and Ocean Engineers, 1–2 September 2009. Japan: JASNAOE, vol. 8, pp.467–468.

Aguiar de Souza

Kirkayak

Suzuki

et al . Experimental and numerical analysis of container stack dynamics using a scaled model test. Ocean Eng 2012; 39: 24–42.

Zhou

Qiu

Hua

et al . A feasible approach to the integration of CAD and CAPP. Comput Aided Design 2007; 39: 324–338.

Sanya

Shehab

EM.

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry. Int J Prod Res 2014; 52: 6192–6215.

Kulon

Mynors

Broomhead

A knowledge-based engineering design tool for metal forging. J Mater Process Tech 2006; 177: 331–335.

Van der Velden

Bil

. Adaptable methodology for automation application development. Adv Eng Inform 2012; 26: 231–250.

10.

Amadori

Tarkian

Ölvander

et al . Flexible and robust CAD models for design automation. Adv Eng Inform 2012; 26: 180–195.

11.

Baxter

Gao

Case

et al . An engineering design knowledge reuse methodology using process modelling. Res Eng Des 2007; 18: 37–48.

12.

Verhagen

WJC

Bermell-Garcia

van Dijk

REC

et al . A critical review of knowledge-based engineering: an identification of research challenges. Adv Eng Inform 2012; 26: 5–15.

13.

La Rocca

. Knowledge based engineering: between AI and CAD. Review of a language based technology to support engineering design. Adv Eng Inform 2012; 26: 159–179.

14.

Chen

Liu

Z-L

Xie

Y-B.

A knowledge-based framework for creative conceptual design of multi-disciplinary systems. Comput Aided Design 2012; 44: 146–153.

15.

Park

Storch

RL.

Overview of ship-design expert systems. Expert Syst 2002; 19: 136–141.

16.

Yang

Chen

et al . Implementation of knowledge-based engineering methodology in ship structural design. Comput Aided Design 2012; 44: 196–202.

17.

Guo

Wen

Shao

et al . Implementation of knowledge-based engineering methodology in hydraulic generator design. Adv Mech Eng 2015; 7(5): 1–13.

18.

Campana

Peri

Tahara

et al . Shape optimization in ship hydrodynamics using computational fluid dynamics. Comput Method Appl M 2006; 196: 634–651.

19.

Shin

Song

Jang

CD.

Optimum structural design of tankers using multi-objective optimization technique. Ships Offshore Struc 2006; 1: 213–219.

20.

Richir

Caprace

Losseau

et al . Least cost optimization of large passenger vessels. Ships Offshore Struc 2007; 2: 339–345.

21.

Turkmen

Turan

A new integrated multi-objective optimisation algorithm and its application to ship design. Ships Offshore Struc 2007; 2: 21–37.

22.

XB.

Multiobjective optimization and multiattribute decision making study of ship’s principal parameters in conceptual design. J Ship Res 2009; 53: 83–92.

23.

Caprace

Bair

Rigo

Scantling multi-objective optimisation of a LNG carrier. Mar Struct 2010; 23: 288–302.

24.

Gou

Cui

WC.

Application of collaborative optimisation in the structural system design of underwater vehicles. Ships Offshore Struc 2010; 5: 115–123.

25.

Hinnenthal

Clauss

Robust Pareto-optimum routing of ships utilising deterministic and ensemble weather forecasts. Ships Offshore Struc 2010; 5: 105–114.

26.

Gammon

MA.

Optimization of fishing vessels using a Multi-Objective Genetic Algorithm. Ocean Eng 2011; 38: 1054–1064.

27.

Peng

et al . Robust optimization based on knowledge discovery from metal forming simulation. J Mater Process Tech 2007; 187–188: 698–701.

28.

Yang

Chen

et al . Application of knowledge-based engineering for ship optimisation design. Ships Offshore Struc 2012; 9: 64–73.

29.

Cui

J-J

Wang

D-Y.

Application of knowledge-based engineering in ship structural design and optimization. Ocean Eng 2013; 72: 124–139.

30.

Sun

et al . A comparative study on thin-walled structures with functionally graded thickness (FGT) and tapered tubes withstanding oblique impact loading. Int J Impact Eng 2015; 77: 68–83.

31.

Fang

Gao

et al . Design of transversely-graded foam and wall thickness structures for crashworthiness criteria. Compos Part B: Eng 2016; 92: 338–349.

32.

Shaker

Abdelrahman

Tawfik

et al . Stochastic finite element analysis of the free vibration of functionally graded material plates. Comput Mech 2008; 41: 707–714.

33.

Sun

et al . Crashing analysis and multiobjective optimization for thin-walled structures with functionally graded thickness. Int J Impact Eng 2014; 64: 62–74.

34.

Roh

M-I

Lee

K-Y.

Generation of the 3D CAD model of the hull structure at the initial ship design stage and its application. Comput Ind 2007; 58: 539–557.

35.

Hwang

H-J

Han

Kim

Y-D.

Mapping 2D midship drawings into a 3D ship hull model based on STEP AP218. Comput Aided Design 2004; 36: 537–547.

36.

China Classification Society (CCS). Rules and regulations for the construction and classification of sea-going steel ships. Beijing, China: China Communications Press, 2015.