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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
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
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
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
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
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
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
CHALLENGEObtaining algorithms for FMS stacker crane not impossible due to IP
SOLUTIONMethod for collection of primary data from shop floor through videoing
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.
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 rulesFlexibility on: • Key production elements (no of machines, no of
pallets)• Cycle times • Product mix in production • Further plans: levels of flexibility in routing
Part Sequencing
• Stage and location• 5 operations in CNC and manual operations• 44 steps in production
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)
Experimentation
Experimental Factors Responses
Sequence of parts (S1, S2)Number of pallets (N2,N3,N4)Machine breakdown (M4,M3)
Machine utilisationThroughput
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
Design of Experiments
Scenario Parameters
No. Sequence (S) Number of pallets (N) Number of machines (M)
Base Case 1 3 41 2 3 42 1 2 43 2 2 44 1 4 45 2 4 46 1 3 37 2 3 38 1 2 39 2 2 3
10 1 4 311 2 4 3
The design of experiments set-up.
N - parameter
2 3 40
100
200
300
400
500
600
Sum of M2 Utilisation % Sum of M1 Utilisation %
N- parameter
Tota
l Util
isat
ion
2 3 4720
740
760
780
800
820
840
860
880
900
N- parameter
Ave
rage
Thr
ough
put
M - parameter
3 40
100
200
300
400
500
600
700
800
900
Sum of M2 Utilisation % Sum of M1 Utilisation %
M - parameter
Tota
l Util
isat
ion
3 41100
1150
1200
1250
1300
1350
M- parameter
Ave
rage
Thr
ough
put
Combined scenarios utilisation
3 4 3 4 3 4 3 4 3 4 3 42 3 4 2 3 4
1 2
020406080
100120140160180200
Sum of M2 Utilisation % Sum of M1 Utilisation %
M, N, S - parameters
Tota
l util
isat
ion
Combined scenarios throughput
3 4 3 4 3 4 3 4 3 4 3 42 3 4 2 3 4
1 2
0
50
100
150
200
250
300
350
M,N,S- parameters
Ave
rage
Thr
ough
put
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
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.
Thank You
Justyna RybickaPhD in Manufacturing Systems
Cranfield UniversityCranfield, MK430AL
Email: [email protected]
