Digital Twin Material Flow Control for Intralogistics Throughput
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Solution Overview
Problem
Existing material-flow control systems in intralogistics struggle to anticipate and dynamically adjust to random issues such as slippage, collisions, and wear, leading to inefficiencies and system downtime, while simulations lack real-time monitoring and adaptation.
Innovation Solution
An intralogistics system with a controller and digital twin that continuously simulates and optimizes material flow using variable operating parameters, anticipating and resolving issues through cyclic simulation and parameter adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a classic material-flow control system is used to coordinate transport orders, then the system can initially plan and generate transport orders, but it cannot anticipate random issues such as slippage, collisions, and wear, leading to system downtime and inefficiencies
Solution Approach 1:
The digital twin simulates material flow in advance to identify potential issues before they occur in the real system. By performing preliminary simulations with varied operating parameters, the system anticipates problems such as slippage, collisions, and wear, and prepares optimized parameter sets to prevent these issues before they cause downtime or inefficiencies in the actual material flow system.
Solution Approach 2:
The system continuously compares real operating states with simulated states from the digital twin. Sensors monitor the actual material flow and transport devices, feeding this data back to the controller, which then adjusts operating parameters based on discrepancies detected between real and simulated behavior, enabling dynamic adaptation to random issues while maintaining system reliability.
2Productivity
If transport orders are implemented continuously with fixed parameters, then the material flow can be coordinated, but the system cannot dynamically adjust to random issues, resulting in throughput deterioration
Solution Approach 1:
A digital twin (virtual copy) of the material flow system is created and used to simulate various operating scenarios. This copying approach allows the system to test different operating parameters in the virtual model without disrupting the real system, identifying parameter sets that maximize throughput while accounting for random issues, thereby improving productivity without proportionally increasing physical system complexity.
Solution Approach 2:
The system varies operating parameters (such as transport speeds, acceleration rates, and timing) in the digital twin simulations to identify optimal parameter combinations. These parameter changes are then applied to the real transport devices through the controller, enabling dynamic adjustment that maintains high throughput while adapting to random issues like slippage and collisions without requiring complex hardware modifications.
3Loss of time
If the system reacts to problem messages after they occur, then it can address issues situationally, but it cannot prevent throughput deterioration that has already occurred
Solution Approach 1:
The digital twin performs preliminary simulations to identify potential problems before they manifest in the real system. By anticipating issues such as slippage, collisions, and wear through virtual testing with varied operating parameters, the system prepares optimized parameter sets in advance, enabling preventive action rather than reactive response, thus reducing both response time and throughput loss.
Solution Approach 2:
The system continuously monitors real operating states and compares them with simulated states from the digital twin. This feedback mechanism detects deviations early, allowing the controller to adjust operating parameters before problems escalate into throughput-deteriorating events, thereby improving both response time and throughput consistency.
Data Source
AI summary
There is disclosed an intralogistics system (10) comprising: a transport network (14) comprising a plurality of transport units (15; 16, 18) and being configured to implement a material flow, caused by transport orders (22), within the intralogistics system (10), wherein each of the transport devices (15) is operated with at least one preset variable operating parameter (36); a plurality of sensors (28) cyclically detecting the current operating states (34); and a controller (26) including a material-flow computer (30), which initially plans and generates the transport orders (22), and cyclically coordinates the implementation of the transport orders (22) based on the current operating states (34); wherein the controller (26) further includes a digital material-flow twin (32), which includes a material-flow simulation model (40), an operating-parameter optimization device (42) and an analysis device (44) and is configured: to cyclically simulate the material flow based on the respective current operating states (34) with and without varying operating parameters (36) of the transport devices (15), to analyze the simulated material flows with regard to throughput improvement, and in case that throughput improvement is analyzed, to transmit the correspondingly varied operating parameter (36) to the corresponding transport devices (15), which subsequently are operated based on the varied operating parameters (36). Further, there is disclosed a method for implementing material flow.