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TRANSCRIPT
Prof. Dr. -Ing. Hong–Seok Park
Laboratory for Production EngineeringSchool of Mechanical and Automotive Engineering
University of ULSAN
June 21st, 2012
a PLM 베스트 프랙티스 컨퍼런스 2012 a
인지 제조시스템- Cognitive Manufacturing System -
Lab. for Production Engineering
Contents
인지제조시스템- Cognitive Manufacturing System -
1. Introduction2. Classification of disturbances through analyzing current manufacturing system3. Elementary technology for developing self adapting manufacturing system
3.1. Cognitive agent3.2. Biology inspired strategy
4. Development of self adapting manufacturing system (SAMS)4.1. Concept of SAMS4.2. Information module of SAMS4.3. Algorithm of SAMS
5. Implementation of SAMS5.1. Hardware architecture of SAMS5.2. Software architecture of SAMS5.3. Communication network of SAMS
6. Conclusion.
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Arriving Work-piece
Loadingmachine
Begin Machining
Machining
FinishMachining
Unloadingmachine
Next Machines
Disturbance
Tool-wear
Tool-break
Current manufacturing
Parameterchange
Tool change
Intervention of human operator
Adjustingparameter
request
Agent #1Tool-wear
Tool-break
Agent #n
New manufacturing
Intelligent&Genetic
behaviours
Self-adaptiveCooperation
Stop machine ExperienceDowntime
vReducing productivityvDecreasing the utilization
of machining shopvMeasures depending on
the experience of operators
vSelf-adaptingvReasoning ability in decision
makingvSelf-controlling ability
Necessity for developing a new manufacturing concept
Stop machine
Self-adapting system
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Clutch housing
Machining ShopProduct
Clutch housing
vProcessing operations per product: 17
vMachines: 12
{ Downtime: 20-25% of total planned timeDisturbance
Recovery method
Current recovery method: Stop the machining shop to repair and reset
Centralized control system
§ Rigid Control: Top-down problem solving
§ Low scalability§ Low adaptability
Proposed method: Self-adaptive manufacturing system
MachineAgent 1
MachineAgent 2
Work-pieceAgent
TransporterAgent
Machine 1 Machine 2 Work-piece Transporter
…Self - Recovery
Analyzing current manufacturing in consideration of self adapting concept
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Disturbance Classification and Management methods
Rescheduling type:Recovering time > 1 hour
Non-negotiationNegotiationRescheduling
11.4%47.7%
40.9%
Event
Malfunction of machine(long recovering time)
ReschedulingAgents
EventTool-break
Agent
NegotiationNon-negotiation
Malfunction of machine(short recovering time)Event
Tool-wear AgentEvent
Disturbance Machining System
Disturbance Classification
MES
Disturbance InformationvData collection time: 2006.08.31-2009.08.18vDisturbance numbers: 685
Disturbance class Type of disturbanceRelated to resources Machine breakdown
Maintenance of machineTool breakdownTool wearOperator absenteeism
Related to orders Unavailability of raw materialCancellation orderReworkArrival of a new job orderUrgent jobDelay in transport using materialhandling systemOut sourcing
Related to measurement of data Process time variationVariation of set-up timesChange of priority
Control softwareand Communication networks
Malfunction
Non-Negotiation type:Recovering time < 30 mins
Negotiation type:30 mins < Recovering time < 1 hour
Disturbance
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Concept of cognitive agent
Rule- basedDecision makingConventional
agent
Percepts
Actions
Sensors
Effectors
Environment
Agent technology Cognitive technology
Perception
Reasoning
Actions
Synthesis of agent and cognitive technologies
Perception(Beliefs)
Interpretation
Learning
Decision Making
(Desires)
Action
Communication
Event
Knowledge& Experience(Intentions)
Reasoning
Eventrecognition
Informationinput
New situation
Updateknowledge
Familiar situation Plan
Commandoutput
Cooperation
CognitiveAgent
BDI ArchitectureBeliefsGrasping the information of the current states of an agent’s environment
DesiresAll the possible states of tasks that agent could carry out
IntentionsThe states of tasks that the agent has decided to work towards
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Ability Machine tool
Task1 Machine 1
Task 2 Machine 2
Task 3 Machine 3
Task 2 Machine 1
Task 2 Machine 3
Ability list
Cognitiveagent
Ability Pheromone
Task 1 Pheromone value 1
Task 2 Pheromonevalue 2
Information Node
M1,T1M2,T2 M3,T3
M1,T1 M3,T2,T3
Machinebreakdown
Route 2
Work-piece
Product
Ants lay pheromone on the trail when they moves food back to nest
Pheromones accumulate with multiple ants using same path
Pheromones evaporate when no ant pass
Ant travel rule: Each ant always try to chose the trail has higher pheromone concentration
From Natural to Manufacturing Systems
The machine with the shortest processing time for carrying out a specific operation will has the highest pheromone
Biology inspired strategy to adapt to disturbance
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Systematic procedure for developing a Self-Adaptive Manufacturing System (SAMS)
Product and Machining Shop
Inspired Biology : Ant Colony AlgorithmCognitive Agent
Elementary technologiesto develop a SAMS
Developing Architecture Of the machining shop
based on Cognitive agents
Model of the machining shop based on functional agentsInformation Model of SAMSMechanism of SAMS for adapting to disturbanceAlgorithm for making decision of SAMS Implementing
the Test-bed of SAMS
Architecture of the Test-bed
Mechanism of the implemented SAMS
for adapting to disturbances
Disturbance Analysis
Disturbance Classification
Finding measures against the
corresponding disturbances
Model of SAMSModel of SAMS Test-bedTest-bed Strategies for overcoming disturbancesStrategies for overcoming disturbancesNon-negotiation Negotiation
DiagnosisDisturbance
Cognitive Agent
Controller
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Machine Agent #i
Machine #iMachine #3
Perception
Decision Making
ControlCommunication
Machine Agent #1Reasoning
Signals Tasks
Plan
Machine #1
Disturbance
Work-piece
MES
Transporter
Machining system
Work-pieceAgent
TransporterAgent
Interpretation
Machine Agent #3
Machine #2
Machine Agent #2
vBehaviours policies§ Rule-based§ Reasoning mechanismvPheromone value
Knowledge
Concept of a self adapting manufacturing system
Machine #1Machining
Shop
Work-piece Transporter Machine #2 Machine #2
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Negotiation
Mean-endsreasoning
Deciding onHow to achieve this state
Previous stateI, B
Update stateb:=see
B:=update( B, b)
Comparisont:= compare (B, D)MES D:=process(task)
Inform normal state (t:=0)
Disturbance typec:=Diagnosis (type)
Type A: rescheduling
Disturbance
I:=filter(B,D,I)p:=plan(B, I, c)Rule-base
Type B: Reactive behavior
Execute (p)
End
NegotiationSelect (agents)+
Type C: Cooperative behavior
without (p) Agent (selected)
Execute (p)
End
MES
without (agent)
Deliberation reasoning
Deciding on what state to achieve
Deciding on what agent to be selected
rescheduling
Cognitive agent based disturbance handling
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Implementing the Test-bed of SAMS
Hardware architecture of SAMS
Working sequence for implementing the SAMS
Software architecture of SAMS
Developing the database of SAMS
Non-negotiation mechanism of SAMS
Negotiation mechanism of SAMS
Evaluation of SAMS
Demonstration of SAMS
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Hardware architecture of SAMS on the test-bed
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PLC S7-300
Disturbanceinputs
RS232 cable
lightindicator
DI D0 CP341 module
CP343-1 module
Internet cable
Internet cardIP: 192.168.0.30
RFIDTag
RFID Reader
Work-piece
Light
Read/Write
Message for SEND/RECEIVE data
Flowchart SEND/RECEIVEbetween PLC and RFID
The communication of wire network between the devices in SAMS
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Agent #1
IP: 192.168.1.1
IP: 192.168.1.2
IP: 192.168.1.n
Agent #2
Agent #n
Bridge in wireless network
IP: 192.168.1.3 Agent #3
Wireless
Access point
Access point
Access point
Access point
The communication bridge has function of the signal amplifier, and is amiddleware node in the communication wireless network.
The communication of wireless network between agents in SAMS
The communication mechanism between agents in the AMS
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Software architecture of SAMS
SQL KepserverEXOPC
PLC1
Agent#1Disturbance
Disturbanceinformation
Work-pieceinformation
ProcessInformation
Task
Knowledge
SQL KepserverEXOPC
PLC2
Agent#2
SQL KepserverEXOPC
PLC3
Agent#3
MES
Message Message
ProcessInformation
Disturbance
ResourceInformation
SQLWire
Wireless
Machine1RFID
Machine2RFID
Machine3RFID
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Machine agent state
Information collection
OPC items
A
A
Disturbance information
Disturbanceclassifying
Perception
Decision
Plan
Task InformationCooperation information
AB
A
B
A disturbance known
B disturbance unknown
Suggested method
Unknown Disturbances
Process Information
Machine Information
Database design and analysis
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Algorithm for making decision of SAMS
Non-Negotiation Negotiation
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11 Input disturbance
shown by turning on the red
light (alarm) at PLC 1
6
Simentic S7PLC #1
22 PLC 1 sends the signal to
agent
33
44
55
66The system overcomes the
disturbance shown by turning
on the green light at PLC 1
OPC protocol is used for
communicatingbetween PLC and
agent
In case of the disturbance
belongs to thenon-negotiation
type, agent generates a new
plan and sends the command to PLC
Collecting data
Agent diagnoses the disturbance
type
Machine agent
Reaction of the system in the case of non-negotiation
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Demonstration for non-negotiation mechanism
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PLC 1
Reaction of the system in the case of negotiation
MES
Registering network
Input disturbance
shown by turning on the red
light (alarm) at PLC 1
11
PLC 1 sends the signal to
agent
Collecting data
22
33
Agent diagnoses the disturbancebelonging to the negotiation type
Agents establish the wireless network
to server
44
55
Agent 1 Agent 2
(Selected agent)Agent 3
PLC 2
Agent negotiation
An appropriate agentis selected for carring
out the job of the failure machine
66
77
The system overcomes the
disturbance shown by turning
on the green light at PLC 2
88
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Demonstration for negotiation mechanism
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Conclusion
§ Summary§ The cognitive agent technology and the biology inspired strategy are applied to the SAMS
§ Disturbances and corresponding management methods in the machining shop of a clutch
housing are analyzed
§ Developing a SAMS to autonomously overcome these disturbances
§ Implementing a test-bed to demonstrate the functionalities of SAMS.
§ Implementing self-evolution mechanism for solving the new disturbance to be happened
§ Future work
§ This method could replace the traditional method that has been intervened by human operator
§ It has the functionalities of intelligent behaviour such as self-adapting, self-controlling, and
reasoning ability in decision making
§ Increasing the productivity and reducing downtime in the product line.
§ Benefits
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