A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities
Abstract
:1. Introduction
- (i)
- for a continuous search for a better and better production plan, and
- (ii)
- the seamless conversion of an ongoing production to such a newly found plan without requiring a global and time-consuming restart of the entire production process.
2. Materials and Methods
2.1. AI Planning and Scheduling
2.2. Planning with PDDL
- Domain:
- specification of the types of entities that are available in this domain, their predicates and functions as well as all the available actions including their input parameters, their preconditions that must hold before a specific action begins, and their effects, which are changes in the state-space done immediately after a specific action is finalized. Effects can optionally have assigned advanced attributes such as costs/fitness and durations. Further, the used language extensions need to be specified in terms of requirements.
- Problem:
- specifies a particular instance of the problem, which contains a description of the initial state, i.e., the available object instances and their properties and relations to another (expressed through predicates) and the definition of the aspired goal state, i.e., the predicate expression that needs to be evaluated in boolean true.
2.3. Digital Twins for Industrial Systems
2.4. Manufacturing Execution Systems
2.5. Industry 4.0 Smart Manufacturing Enabled by PDDL and Digital Twins
3. Implementation and Results
3.1. Industry 4.0 Testbed
3.2. Manufacturing Execution System with Dynamically Generated Production Plans
3.3. Distributed Manufacturing Execution System with On-The-Fly Replanning Capability
Algorithm 1: Definition of an instance of a distributed MES |
- a
- to distinguish between a main LispPlan (source==None) or a redistributed part of that LispPlan on a specific sub-MES.
- b
- including all sub-tasks
- c
- any parent must be processed prior to its children
- d
- determined from task location
- e
- all tasks specified in requirements are in DONE state and the parent task is in PROCESSING state
- f
- sync is received from another MES
- g
- all tasks including all sub-tasks in plan are in DONE state
- If task contains no action but has sub-tasks, then (lines 34–38) task waits for the sub-tasks to be processed (lines 34–35) in the PENDING state, and then if everything succeeds (line 36), task is set to DONE. Otherwise, task is set to FAILED (line 38).
- If task contains an action (and has no sub-task, which is the only option according to the LispPlan format), then the process continues on the switch statement (lines 16–32). The switch control expression contains the response of the digital twin to action start with the following cases:
- (a)
- The action has already started in the past.Then, leave the switch statement (lines 17–18).
- (b)
- The action started successfully in the digital twin, but a new plan was computed in the digital twin.In this case, the action can start on the real production line because it is valid with the digital twin (the PDDL model and the new plan), but the new plan needs to be downloaded into all DMES instances.Because of that, this new plan availability is reported to the current instance of DMES (line 20), and then this case continues to the next subsequent case.
- (c)
- The action started successfully (and the current plan is still valid).The processing on the real production line is started (line 23) and then action done is committed to the digital twin (line 24). If a problem occurs, something unexpected must have happened, and the task state is set to FAILED (line 26). Otherwise, everything has been successful and task is set to DONE (line 25).
- (d)
- The action cannot be started (because it does not conform to the model or plan of the digital twin), and a new plan was computed in the digital twin.Then, this new plan availability is reported to the current instance of DMES, and then this case continues to the next subsequent case.
- (e)
- The action cannot be started because it does not conform to the model of the digital twin, and the current plan is still valid.Let the task state be set to FAILED (line 31).
3.4. Basic Digital Twin Based on PDDL Model
- action check—Checks whether the action can be executed.
- action start—Starts the action if possible. Otherwise, an error code is returned.
- action done—Completes the action if possible. Otherwise, an error code is returned.
- get state—Returns the current state of the basic digital twin in terms of a PDDL problem.
- set domain and problem—Sets up a new PDDL domain and problem.
3.5. Digital Twin with AI Planner
- action check—Checks if the action can be executed and also if the action is in accordance with the current plan.
- action start—Starts the action if possible and reports back whether the action is in accordance with the current plan (if not, a replanning is automatically triggered). Otherwise, an error code is returned.
- action done—Completes the action, if possible, and reports back whether the action is in accordance with the current plan (if not, a replanning is automatically triggered). Otherwise, an error code is returned. This action/operation can be enriched with various data measured from the real production line, such as operation time or power consumption.
- set goal—Sets a production goal in PDDL format and automatically starts the planning process.
- set domain and problem—Sets up a new PDDL domain and problem and then automatically starts the planning process if the goal is already specified.
- get state—Returns the current state of the digital twin as a PDDL problem.
- get plan—Returns the current LispPlan, if any, or reports the status of the planning process. Operations already performed are continuously reflected in the returned plan. A newly computed plan is identifiable by changing the unique hash tag of the plan (calculated as SHA256), which is stored in the plan’s metadata. The resulting LispPlan also contains in its metadata estimations of the remaining time, the total number of actions/operations, and the total energy consumption.
- Empirical ad-hoc approach, where each search strategy has its own time limit based on the empirical knowledge of the programmer. If this time limit is exhausted, the search stops and another search strategy is used instead. The pre-known time limit for each search strategy determines the appropriate starting point for beginning the planning process.
- Backward iterative deepening approach, where planning starts at the point of the last operation before the end of the current plan and then iteratively extends in time toward the beginning of the plan. Since the computation time of such a plan usually increases exponentially with the expected length of the plan, the time required to compute all previous plans usually does not significantly exceed the computation time of the new plan in the next iteration.
- Machine learning estimation approach where the task is to estimate the duration of the planning process. Inputs can be a suitable representation of the particular search strategy used, the PDDL goal, and possibly even the entire PDDL problem.
4. Evaluation and Discussion
- The main TESTBED.CIIRC.CVUT.CZ MES instance (marked by the outer dashed line with transparent fill in Figure 10).
- The MONTRAC.TESTBED.CIIRC.CVUT.CZ MES instance (marked by a dashed line with cyan fill in Figure 10).
- The R20.TESTBED.CIIRC.CVUT.CZ MES instance (marked by a dashed line with cyan fill in Figure 10).
5. Conclusions and Future Work
Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Operation/Action | Execution Time (Mean ± SD) [s] | Energy Consumption (Mean ± SD) [Wh] |
---|---|---|
(ROBOTIC_PICK R20 ... SVR20 ...R8 PART-BLACK-CHASSIS ...) | ||
(ROBOTIC_PLACE R20 ... S100 ... PART-BLACK-CHASSIS ...) | ||
(ROBOTIC_PICK R20 ... SVR20 ... PART-WHITE-CABIN ...) | ||
(ROBOTIC_PLACE R20 ... S100 ... PART-WHITE-CABIN ...) | ||
(ROBOTIC_PICK R20 ... SVR20 ... PART-YELLOW-DUMPER ...) | ||
(ROBOTIC_PLACE R20 ... S100 ... PART-YELLOW-DUMPER ...) | ||
(ROBOTIC_PICK R20 ... SVR20 ...R5 PART-BLACK-CHASSIS ...) | ||
(ROBOTIC_PICK R20 ... SVR20 ... PART-BLUE-CABIN ...) | ||
(ROBOTIC_PLACE R20 ... S100 ... PART-BLUE-CABIN ...) | ||
(ROBOTIC_PICK R20 ... SVR20 ... PART-WHITE-STAKEBED ...) | ||
(ROBOTIC_PLACE R20 ... S100 ... PART-WHITE-STAKEBED ...) | ||
(SHUTTLE_DEPART SHUTTLE2 S12 S100) | ||
(SHUTTLE_ARRIVE_AND_LOCK SHUTTLE2 S100) from S12 | ||
(SHUTTLE_DEPART SHUTTLE3 S100 S200) | ||
(SHUTTLE_ARRIVE_AND_LOCK SHUTTLE3 S200) from S100 | ||
(SHUTTLE_DEPART SHUTTLE2 S12 S200) | ||
(SHUTTLE_ARRIVE_AND_LOCK SHUTTLE2 S200) from S12 | ||
(SHUTTLE_DEPART SHUTTLE3 S23 S100) | ||
(SHUTTLE_ARRIVE_AND_LOCK SHUTTLE3 S100) from S23 |
Task ID | Operation/Action | Execution Time [s] | Energy Consumption [Wh] |
---|---|---|---|
(SHUTTLE_DEPART SHUTTLE2 S12 S100) | |||
(ROBOTIC_PICK R20 ... SVR20 ...R8 PART-BLACK-CHASSIS ...) | |||
0 | (SHUTTLE_ARRIVE_AND_LOCK SHUTTLE2 S100) from S12 | ||
1 | (ROBOTIC_PLACE R20 ... S100 ... PART-BLACK-CHASSIS ...) | ||
2 | (ROBOTIC_PICK R20 ... SVR20 ... PART-WHITE-CABIN ...) | ||
3 | (ROBOTIC_PLACE R20 ... S100 ... PART-WHITE-CABIN ...) | ||
4 | (ROBOTIC_PICK R20 ... SVR20 ... PART-YELLOW-DUMPER ...) | ||
5 | (ROBOTIC_PLACE R20 ... S100 ... PART-YELLOW-DUMPER ...) | ||
6 | (ROBOTIC_PICK R20 ... SVR20 ...R5 PART-BLACK-CHASSIS ...) | ||
7 | (SHUTTLE_DEPART SHUTTLE3 S100 S200) | ||
8 | (SHUTTLE_DEPART SHUTTLE3 S23 S100) | ||
9 | (SHUTTLE_ARRIVE_AND_LOCK SHUTTLE3 S100) from S23 | ||
10 | (ROBOTIC_PLACE R20 ... S100 ... PART-BLACK-CHASSIS ...) | ||
11 | (ROBOTIC_PICK R20 ... SVR20 ... PART-BLUE-CABIN ...) | ||
12 | (ROBOTIC_PLACE R20 ... S100 ... PART-BLUE-CABIN ...) | ||
13 | (SHUTTLE_ARRIVE_AND_LOCK SHUTTLE3 S200) from S100 | ||
14 | (ROBOTIC_PICK R20 ... SVR20 ... PART-WHITE-STAKEBED ...) | ||
15 | (ROBOTIC_PLACE R20 ... S100 ... PART-WHITE-STAKEBED ...) | ||
Sum total |
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Vyskočil, J.; Douda, P.; Novák, P.; Wally, B. A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities. Sustainability 2023, 15, 6251. https://doi.org/10.3390/su15076251
Vyskočil J, Douda P, Novák P, Wally B. A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities. Sustainability. 2023; 15(7):6251. https://doi.org/10.3390/su15076251
Chicago/Turabian StyleVyskočil, Jiří, Petr Douda, Petr Novák, and Bernhard Wally. 2023. "A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities" Sustainability 15, no. 7: 6251. https://doi.org/10.3390/su15076251