Monitor and improve the efficiency of production processes.
Challenge:
The company is a FFP2 mask producer. His aim is to create a digital-twin of their process in order to better understand how they can improve both the production processes and the product itself.
Industry Sector:
IIoT
Challenge classification:
Real-time process monitoring and optimization; Detection of early signs of equipment malfunctions, allowing maintenance personnel to prevent further failure; Improving personnel working conditions based on real-time temperature, humidity and other environmental data
Time for Project Completion:
3 months
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Preliminary Analysis:
DT is indeed the best solution for optimising production processes and products. Nevertheless, it is a quite new technology, and as such, there is a lack of standardization in its implementation. The heterogeneity of machines/sensors, protocols, and of course use cases makes the conception and implementation of a DT strategy a complex and interdisciplinary development process. In order to address it we need to reply to the proposed research questions together with all the stakeholders of the DT, and follow a systematic approach to guarantee that the real needs of this company are covered. We based our solution on the work presented in [9].
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Solution Summary
The proposed solution consists of following a systematic approach for the conception and implementation of a DT. Indeed, the granularity of the sensing, the level of automation, the specific technologic solution for the simulator, sensors, protocols, etc. will depend on a more detailed analysis of the scope and objectives of this DT, as well as on the particular starting point of the company in this regard. Thus, the following procedure model for the conception and implementation of a Digital Twin extracted from [9] is our proposed solution.
The following Figure presents the Procedure model to follow:
Procedure model for the conception and implementation of a Digital Twin in industry [9].
Extra benefits:
- The standardized procedure of implementing this DT could be used for any other use cases that one could imagine, facilitating the scalability of this DT in the mid-long term.
- Many other digitization initiatives in the Company could be evaluated (both at an economic and performance levels) before affecting any real production unit or process.
- The use of DT is a clear proof that this Company is ahead of its competitors on innovation and technology, which could be used for convincing possible customers.
Possible issues:
- Any update on the Company must be reflected in the DT. Need of an expert on DT in the Company or continuous subcontracting for its maintenance.
- As a novel technology, better solutions may arise in the following months/years.
- As a novel technology it can be difficult to find expert and cheap IT providers for its implementation.
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Sources:
- Predictive maintenance/retrofitting, ejector https://www.mdpi.com/2071-1050/13/2/646 (Arduino, Deep learning, Optimization algorithms, Real-time web frameworks)
- Smart scheduling / equipment health predictions, laboratory assembly line environment https://www.sciencedirect.com/science/article/pii/S2405896319308791?via%3Dihub#! (Genetic algorithms, Cyber-physical Systems)
- smart Control Engineering / Realtime vision feedback infrared temperature uniformity control https://ieeexplore.ieee.org/document/9262203 (Raspberry Pi, Arduino Leonardo, Matlab Simulink and Simscape, Lattepanda board, Pulse Width Modulation)
- Fully automated facilities, predictive maintenance https://ieeexplore.ieee.org/document/9367549 (Robotics, Edge Computing)
- Floor plant energy consumption https://www.sciencedirect.com/science/article/pii/S2351978918313763 (Data models, ontology, DT simulations)
- Energy-efficient Manufacturing https://www.sciencedirect.com/science/article/pii/S0360835219300324 (Cloud, data models, cyber-physical systems)
- Quality control / Performance control / Turbine blades / geometries https://www.sciencedirect.com/science/article/pii/S2405896320335746 (Computer-aided engineering, Finite Element Method model, digital shadow, 3D modelling)
- prevention, prediction and control of nano-forms airborne emission and worker exposure https://www.researchgate.net/publication/352657484_Digital_Twins_applied_to_the_implementation_of_Safe-by-Design_strategies_in_nano-processes_for_the_reduction_of_airborne_emission_and_occupational_exposure_to_nano-forms_Digital_Twins_applied_to_the_i (ISO 23247, safe-by-design)
aluminium anomalies detection / extrusion process / cost reduction / production efficiency https://www.mdpi.com/2071-1050/13/18/10155/htm (Gaussian process, DT procedure model, UC template)