Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Modelling and Simulation of Crankcase Cover Manufacturing in the Automobile Industry


Affiliations
1 Applied Science Department (Mechanical), Bharati Vidyapeeth’s College of Engineering, Delhi 110 063, India
2 Mechanical, Production, and Industrial Engineering Department, Delhi Technological University, Delhi 110 042, India
 

The simulation creates the virtual production model which is exactly like the real environment, and it provides future insights before laying down the actual production plant layout. With the help of simulation, we can simulate the complex and costly manufacturing system without investing money physically and check the system’s real-life behavior. In this study, for the first time, the modelling and simulation of two-wheeler crankcase cover manufacturing are done with the help of Flexsim. This study deals with the development of a simulation model for crankcase cover manufacturing systems in the automobile industry. Flexsim simulation tool is used as an optimization tool for analyzing the effect of varying the number of operators and process stations on system performance using various scenarios. The results indicate that scenario 2 is best for crankcase cover manufacturing which reduces the overall idle time of all process stations and optimizes the number of Die-Casting Machines (DCM) and Vertical Milling Center (VMC). These results are useful for all industries for simulating their process or product layout.

Keywords

Flexsim, Optimization, Plant Layout, Production Model, System Performance.
User
Notifications
Font Size

  • Bjorklund S, Yamani N & Lloyd T, Simulation software for real time forecasting as an operational support, Proc Second World Conf POM and 15th Annual POM Conf (Cancun, Mexico) 2004, 1–30.

  • Krishna L S R, Saketh T S S & Kumar M V S, A simulation based approach for studying the effect of buffers on the performance of an FMS, Int J Eng Sci Invent, 4(1) (2015) 5–9.

  • Fauadi M, Azimi N Z, Anuar N I, Ali M M, Sued M K & Ramlan S, Simulation approach for logistical planning in a warehouse : a review, Orient J Comput Sci Technol, 11(4) (2018) 201–208.

  • Jarernram J & Samattapapong N, Parallel machine scheduling using simulation software, Proc ICMEIT (Pattaya, Thailand) 2018, 150–155.

  • Rodrigues R P, Pinho A F D & Sena D C, Application of hybrid simulation in production scheduling in job shop systems, Simul Trans Soc Model Simul Int, 96(3) (2019) 1–16.

  • Nie X & Wang L, Simulation process design for scheduling mode of railway container terminals based on Flexsim, Proc J Phys: Conf Ser, 1176(5) (2019) 1–6.

  • Badri M A & Hollingsworth J, A simulation model for scheduling in the emergency room, Int J Oper Prod Manag, 13(3) (1993) 13–24.

  • Cheng H C & Chan D Y K, Simulation optimization of part input sequence in a flexible manufacturing system, Proc Winter Simulat Conf (IEEE) 2011, 2374–2382.

  • Peng Y & Jiang Y C, Production process scheduling based on simulation with global optimization strategy: a case study, Inf Technol J, 12(5) (2013) 983–989.

  • Gong L, Zou B & Kan Z, Modeling and optimization for automobile mixed assembly line in industry 4.0, J Control Sci Eng, 2019 (2019) 1–10.

  • Chandika S & Asokan R, Design and analysis of piezoactuated micropump for fuel delivery in automobiles, J Sci Ind Res, 70(6) (2011) 448–454.

  • Kesen S E & Sert M, A simulation analysis of a serial line pull system producing orders with various part types and volumes, Int J Model Simul, 40(3) (2019) 184–200.

  • Lin C K Y, Ling T W C & Yeung W K, Resource allocation and outpatient appointment scheduling using simulation optimization, J Healthc Eng, 2017 (2017) 1–19.

  • Azab A, Karam A, Eltawil A, Azab A & Karam A, A simulation-based optimization approach for external trucks appointment scheduling in container terminals scheduling in container terminals, Int J Model Simul, 40(5) (2019) 1–18.

  • Huynh N, Reducing truck turn times at marine terminals with appointment scheduling, Transp Res Rec J Transp Res Board, 2100(1) (2009) 47–57.

  • Kim S, Meng C & Son Y J, Simulation-based machine shop operations scheduling system for energy cost reduction, Simul Model Pract Theory, 77 (2017) 68–83.

  • Phani K, Kumar R, Kamala V & Kumar A, Simulation of job shop scheduling problem, Int J Eng Res Technol, 2(6) (2013) 831–834.

  • Steinhauer D, Sikorra J N, Haux M A, Friedewald A & Lodding H, Processing incomplete data for simulation-based production planning in shipbuilding, J Simul, 11(1) (2017) 30–37.

  • Wang Y R & Chen A N, Production logistics simulation and optimization of industrial enterprise based on Flexsim, Int J Simul Model, 15(4) (2016) 732–741.

  • Zhang Y, Zhao Z, Bi L, Wang L & Gu Q, Determination of truck–shovel configuration of open-pit mine: a simulation method based on mathematical model, Sustainability, 14(19) (2022) 12338.

  • Kliment M, Trojan J, Pekarcíková M, & Kronová J, Creation of simulation models using the Flexsim software module, ALS - Theory Pract, 16(1) (2022) 41–50.

  • Kessel D, Jeon J, Jung J, Oh E & Kim C, Logistical simulation modeling for planning a soil remediation process, Sci Technol Nucl Install, 2019 (2019) 1–13.

  • Krynke M, Personnel management on the production line using the Flexsim simulation environment, Manuf Technol, 5 (2021) 657–67.


Abstract Views: 119

PDF Views: 86




  • Modelling and Simulation of Crankcase Cover Manufacturing in the Automobile Industry

Abstract Views: 119  |  PDF Views: 86

Authors

Sumit Chawla
Applied Science Department (Mechanical), Bharati Vidyapeeth’s College of Engineering, Delhi 110 063, India
Ranganath M Singari
Mechanical, Production, and Industrial Engineering Department, Delhi Technological University, Delhi 110 042, India

Abstract


The simulation creates the virtual production model which is exactly like the real environment, and it provides future insights before laying down the actual production plant layout. With the help of simulation, we can simulate the complex and costly manufacturing system without investing money physically and check the system’s real-life behavior. In this study, for the first time, the modelling and simulation of two-wheeler crankcase cover manufacturing are done with the help of Flexsim. This study deals with the development of a simulation model for crankcase cover manufacturing systems in the automobile industry. Flexsim simulation tool is used as an optimization tool for analyzing the effect of varying the number of operators and process stations on system performance using various scenarios. The results indicate that scenario 2 is best for crankcase cover manufacturing which reduces the overall idle time of all process stations and optimizes the number of Die-Casting Machines (DCM) and Vertical Milling Center (VMC). These results are useful for all industries for simulating their process or product layout.

Keywords


Flexsim, Optimization, Plant Layout, Production Model, System Performance.

References