Justyna Rybicka discuss using WITNESS software to model support decision making tool for flexible manufacturing system optimisation
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The document discusses the use of Witness software as a decision-making tool for optimizing flexible manufacturing systems (FMS). It highlights the significance of operational flexibility in manufacturing and presents a case study that investigates optimal production setups in FMS environments. Key findings emphasize the impact of operation sequences and pallet management on FMS performance and throughput.
![FMS challenges
Flexibility of FMS is a major argument for its
benefits to industry
Joseph (2011) defines flexibility as the ability
of a system to respond effectively to changes
[…]
GAP: limited insight into systems that assume
total flexibility in FMS
This research…
…investigation into optimal production set-up
with total flexibility on CNC machines in FMS
context is explored.](https://image.slidesharecdn.com/justyna-cranfield-160607171136/85/Justyna-Rybicka-discuss-using-WITNESS-software-to-model-support-decision-making-tool-for-flexible-manufacturing-system-optimisation-8-320.jpg)
Justyna Rybicka discuss using WITNESS software to model support decision making tool for flexible manufacturing system optimisation
- 1. Use of WITNESS software to model support decision making tool for flexible manufacturing system optimisation Justyna Rybicka, PhD Researcher Lanner User Group Event 28th of April, MTC, Coventry
- 2. AGENDA • WITNESS role in research • Background • FMS definition • Problems in modelling FMS • Methods of data collation- for better modelling • FMS case study on optimisation of flexible production line • Acknowledgements
- 3. WITNESS role in research Simulation as reconfiguration capability development tool for FMS based production Provision of a simulation environment to test complex FMS configuration where: • “Black box” activities need to be accurately modelled • Mix-model production needs to be addressed • Production requirements change rapidly
- 4. Background • Customisation and product diversification is becoming standard • Manufacturers seek solutions to unique capabilities where there is a need for product range diversification providing line efficiency and production flexibility • Flexible manufacturing systems (FMS) provide a unique capability to manufacturing organisations where there is a need for product range diversification by providing line efficiency through production flexibility • Discrete event simulation is a simulation approach considered as successful in addressing real world problems in manufacturing sector
- 5. Flexible Manufacturing System • A flexible manufacturing system (FMS) is a group of numerically controlled machine tools, interconnected by a central control system. • Operational flexibility is enhanced by the ability to execute all manufacturing tasks on numerous product designs in small quantities and with faster delivery. Flexible manufacturing system basic layout
- 6. Data driven DES modelling for FMS Due to the logic being proprietary to the system designer, some of the behaviour of the system’s hardware components cannot be accurately replicated in coding. To overcome this, system behaviour has been observed and the logic inferred in the model Limited understanding of the machine behaviour and therefore inaccurate modelling can affect the results of the simulation run The quality of data fed in to the simulation affects the quality of outputs which in consequence translates to the trust that the simulation is reliable source of analysis MHS - Logic process flow
- 7. Approach for data collection Observe the behaviour Identify distinctive actions Collect the data related to distinctive actions Convert data into simulation friendly format Use data as simulation input CHALLENGE Obtaining algorithms for FMS stacker crane not impossible due to IP SOLUTION Method for collection of primary data from shop floor through videoing
- 8. FMS challenges Flexibility of FMS is a major argument for its benefits to industry Joseph (2011) defines flexibility as the ability of a system to respond effectively to changes […] GAP: limited insight into systems that assume total flexibility in FMS This research… …investigation into optimal production set-up with total flexibility on CNC machines in FMS context is explored.
- 9. Case Study FMS • PLC with 2 types of CNC machines • Parts on pallets • Two types of parts processed • 68 storage spaces Modelling Approach Full control over the process flow – functions and rules Flexibility on: • Key production elements (no of machines, no of pallets) • Cycle times • Product mix in production • Further plans: levels of flexibility in routing
- 10. Part Sequencing • Stage and location • 5 operations in CNC and manual operations • 44 steps in production
- 11. Conceptual Model Included in the model Excluded from the model FMS and surrounding it manual operations Total flexibility of FMS operation Two parts are machined on each pallet Shift time– 24/5 4 type 1 machines (M1) 1 type 2 machines (M2) Manual operations dedicated to stations (no flexibility) Raw material is always available Labour Statistical Breakdowns Transportation of parts Set-up times Robinson (2011)
- 12. Experimentation Experimental Factors Responses Sequence of parts (S1, S2) Number of pallets (N2,N3,N4) Machine breakdown (M4,M3) Machine utilisation Throughput Summary of the model experimental factors and responses Experiment set-up • Deterministic model • 1 simulation run per experiment • Warm-up period: 10 weeks • Run time: 52 weeks
- 13. Design of Experiments Scenario Parameters No. Sequence (S) Number of pallets (N) Number of machines (M) Base Case 1 3 4 1 2 3 4 2 1 2 4 3 2 2 4 4 1 4 4 5 2 4 4 6 1 3 3 7 2 3 3 8 1 2 3 9 2 2 3 10 1 4 3 11 2 4 3 The design of experiments set-up.
- 14. N - parameter 0 100 200 300 400 500 600 2 3 4 TotalUtilisation N- parameter Sum of M2 Utilisation % Sum of M1 Utilisation % 720 740 760 780 800 820 840 860 880 900 2 3 4 AverageThroughput N- parameter
- 15. M - parameter 0 100 200 300 400 500 600 700 800 900 3 4 TotalUtilisation M - parameter Sum of M2 Utilisation % Sum of M1 Utilisation % 1100 1150 1200 1250 1300 1350 3 4 AverageThroughput M- parameter
- 16. Combined scenarios utilisation 0 20 40 60 80 100 120 140 160 180 200 3 4 3 4 3 4 3 4 3 4 3 4 2 3 4 2 3 4 1 2 Totalutilisation M, N, S - parameters Sum of M2 Utilisation % Sum of M1 Utilisation %
- 17. Combined scenarios throughput 0 50 100 150 200 250 300 350 3 4 3 4 3 4 3 4 3 4 3 4 2 3 4 2 3 4 1 2 AverageThroughput M,N,S- parameters
- 18. Conclusions The developed modeling demonstrate how WITNESS can support flexible set-up in flexible manufacturing system General conclusions can be drawn about the FMS behavior to support flexibility: • The sequence of operation around the FMS had impact on the FMS performance • Optimisation of the number of pallets in the system is key as its shortage can lead to FMS starvation and its oversubscription creates bottlenecks in the system affecting throughput
- 19. Acknowledgement Many thanks to Advanced Manufacturing Supply Chain Initiative (AMSCI) for supporting and funding the research in automotive industry. Also, great thanks to the industry collaborators who supported us in this work - Cosworth.
- 20. Thank You Justyna Rybicka PhD in Manufacturing Systems Cranfield University Cranfield, MK430AL Email: j.e.rybicka@cranfield.ac.uk
Editor's Notes
- #5: In the age of automation and computation aiding manufacturing it is clear that manufacturing systems become more complex as it has ever been before Technological advances provide the capability to gain more value with fewer resources sometimes ustilisation of the manufacturing capabilities available to orgainsation is difficult to achieve