The SMC FIC-400 Teaching Learning Factory (TLF), part of PBN’s Digital Innovation Hub in Hungary, has integrated an innovative Digital Twin of its new recycling module, under the DigiTwinGreen project funded by the EIT Regional Innovation Scheme. The Digital Twin simulates the recycling process in a Mixed-Reality environment, allowing for hands-on training and optimization of the recycling module's efficiency.

The SMC FIC-400 Teaching Learning Factory (TLF), located in PBN’s Digital Innovation Hub in Hungary, offers the opportunity to design, develop, and test successfully working solutions for the industry. The TLF is a critical component of a 15-module system including 5 Industry 4.0 modules. This assembly line system features a pallet and container feeder, pellet feeder, cupping station, warehouse, as well as a labelling and dispatching station. These are all connected to a Manufacturing Engineering System, capable of managing orders, initiating manufacturing process, monitoring warehouse state, etc.

As part of the DigiTwinGreen project, funded under the EIT Regional Innovation Scheme, the TLF has been extended with a new recycling module capable of sorting balls of different colour. Specifically, balls from the container disk fall into the nests of a rotating dosing disc, where a sensor identifies the colour of the balls triggering the corresponding latch to open. Subsequently, the balls are delivered to the appropriate container by pipes that continue beneath the worktable. The recycling module also includes a dedicated User Interface (UI), featuring a slider to set the speed of the disk (in RPM), start/stop buttons and a numeric display which displays the current speed value (RPM), the cycle time and the number of sorted pieces (per colour and total number).

Under this context, a Digital Twin simulation environment for the recycling module has been developed to enable the digital representation and operation of the recycling procedure. For the visualisation and control of the virtual twin module, a Mixed-Reality (MR) approach has been employed to blend the real and virtual aspects of the module and facilitate their interaction in semi real-time. In this way, the Digital Twin can provide hands-on training to new workers, enabling them to learn the manufacturing process and gain practical knowledge quickly and efficiently. The MR technology has been implemented through the Oculus Quest Pro headset, while the developed application carried out in Unity 3D software. The whole solution was deployed at the edge. For the seamless accommodation of the virtualization and simulation task’s demands, a powerful workstation has been utilised, encapsulating processing power for both CPU and GPU, as well as meeting the essential memory requirements for deploying MR applications.

Furthermore, to ensure the green modality of the recycling module, a solar panel was utilised to supply the energy along with storage unit to store the energy produced. Through the simulation environment the process efficiency of the module was enhanced by identifying the theoretical optimal rotational speed (RPM) of the rotating disk which maximizes the throughput (pieces/time) of the module. Finally, CORE Innovation Centre has verified that the Digital Twin’s features comply with the user requirements, and the Digital Twin’s accuracy has been validated by data-driven techniques, proving that it is capable of accurately replicating the operational behaviour of the real-world recycling module.

<p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><a href="https://digitwingreen.eu/" target="_blank">https://digitwingreen.eu/</a><a href="https://digitwingreen.eu/" target="_blank"></a></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><br></p><p class="MsoNormal" style="margin: 0cm; text-align: justify;"><a href="https://www.pbn.hu/" target="_blank">https://www.pbn.hu/</a><font face="Aptos, sans-serif"><span style="font-size: 14.6667px;">&nbsp;</span></font></p><p class="MsoNormal" style="margin: 0cm; text-align: justify;"><font face="Aptos, sans-serif"><span style="font-size: 14.6667px;"><br></span></font></p><p class="MsoNormal" style="margin: 0cm; text-align: justify;"><font face="Aptos, sans-serif"><span style="font-size: 14.6667px;"><br></span></font><br></p><p class="MsoNormal" style="margin: 0cm; text-align: justify;"><font face="Aptos, sans-serif"><span style="font-size: 14.6667px;"><br></span></font></p><p class="MsoNormal" style="margin: 0cm; text-align: justify;"><font face="Aptos, sans-serif"><span style="font-size: 14.6667px;"><br></span></font><br></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-US"><o:p></o:p></span></p>

A digital twin of the Hyundai Konas battery pack was created to make data sets for computer vision models and teach the battery pack's robotic disassembly

There is a pressing need to develop an efficient method for disassembling battery packs and reclaiming rare earth materials for recycling. Despite this imperative, numerous challenges persist, requiring significant advancements to establish an effective solution. Currently, battery pack disassembly is predominantly a manual process, a practice that necessitates reconsideration in the near future.

Map of battery recycling

To overcome this problem, we are using metaverse and digital twins to teach the robot to disassemble the batteries. This is made possible by Using Nvidia Omniverse software, which enables the creation of realistic-looking renders and data sets and teaches robots with reinforcement learning.

For Omniverse, the digital twin just needs to be a CAD file of the system, and in our case, we created the CAD by reverse engineering it to Onshape, which is browser-based CAD software that allows users to directly import the CAD models to Omniverse.

The CAD models

After the import to Omniverse, it's possible to put realistic-looking materials to the part and create date sets from the model with Omniverses replicator and simple Python scripts. And to simulate complex geometries like unscrewing with the signed distance field collisions.

Battery pack in omniverse

<p>Switzerland battery research center<br></p>

A digital twin has been created for the process of thermo-moulding within the manufacturing of shoe insoles. The large number of orders, added to the large amount of variety that an order can have, added to the numerous customizations that customers want for each of their orders makes our resource management impossible to manage by human beings. As a result, every day we have more than 21,000 different orders going around our warehouse and being processed by our machines. Achieving a perfect organization of resources and time has become a huge challenge that is collapsing one of our main processes.

The objective is to develop a scalable algorithm fed with information in real time that is capable of distributing orders in the machines, optimizing waiting time, electricity consumption and the availability of limited resources. All these variables and casuistics make it impossible for a conventional mathematical algorithm to find a favorable solution in a short time. So we need computing power and more sophisticated algorithms and techniques. In principle we are going to try to address the problem with genetic algorithms, they are a type of algorithms that can explore many search spaces in a short time and can address multifactorial problems.

After developing the solution, we will have a great impact at the organizational level, so we will not need so many resources for the management and organizational part and thus allocate more resources to what is really important, which is to scale and increase manufacturing. We will reduce the use of electricity, which is a great incentive in these times of energy crisis. We will optimize the use of space in our warehouses, since everything will be cleaner and orderlier and it will be easier to locate the assets since we will gain more speed in the manufacturing flow.

<p><a href="https://digitbrain.eu/3rd-wave-of-digitbrain-experiments/experiment-18-insotwin-an-algorithm-to-autonomously-and-intelligently-plan-all-the-tasks-that-our-machines-have-to-perform/" target="_blank">https://digitbrain.eu/3rd-wave-of-digitbrain-experiments/experiment-18-insotwin-an-algorithm-to-autonomously-and-intelligently-plan-all-the-tasks-that-our-machines-have-to-perform/</a>&nbsp;</p><p><a href="https://www.itainnova.es" target="_blank">https://www.itainnova.es</a>&nbsp;<br></p>

A rotary dryer reduces the humidity in particulate matter through direct contact with combustion gases from fossil or renewable fuels (biomass). The design and operation of a rotary dryer present significant challenges, mainly because it must be flexible enough to adapt to a multitude of operating modes. Under this context, we want to prove the benefits for the manufacturer of the industrial product of the development of a digital twin for the rotary dryer. The digital twin will support the design, production and operation phases of the rotary dryer. It will contribute to the faster and more reliable design, better integration of the equipment in the customers’ general process and provide an optimal configuration of the process.

This DT uses the detailed simulation of a process to determine the optimal control of an installation. This methodology, applied to continuous processes, we believe is innovative and applicable to multiple manufacturing operations. From the economic point of view, as indicated above, the proposed tool is positioned in markets that will experience considerable growth in the coming years. In addition, this digital twin has multiple types of users: from rotary dryer manufacturing companies to companies that use these dryers in their processes. This will facilitate the exploitation of the tool. Finally, this DT is built under the concept of a sustainable and digital transition since the original application of the digital twin will focus on a rotary dryer to produce biomass pellets, one of the expected impacts being the improvement of the energy efficiency in the process.

<p><a href="https://youtu.be/LGz12-6UDb8" target="_blank">https://youtu.be/LGz12-6UDb8</a><a href="https://youtu.be/LGz12-6UDb8" target="_blank"></a>&nbsp;</p><p><a href="https://digitbrain.eu/2nd-wave-of-digitbrain-experiments/dt4dryer/" target="_blank">https://digitbrain.eu/2nd-wave-of-digitbrain-experiments/dt4dryer/</a>&nbsp;</p><p><a href="https://www.itainnova.es" target="_blank">https://www.itainnova.es</a>&nbsp;<br></p>

Inymon,'s core business is the injection and decoration of small to medium-sized plastic parts. For every new part to put in production by INYMON, process set-up and optimization is a key phase to keep the company competitiveness in an extremely time and cost demanding market. Today, this adjustment is performed based on experience and through trial and error loops. The use of simulation tools to design the injection process of thermoplastic parts is common practice in the plastics processing industry. Basically, these tools available in commercial packages such as Moldflow, Moldex3D etc. are used in the mould design stage, with the main objective of anticipating manufacturing problems and finding an “a priori” solution analysing alternatives of part and mould design, thereby reducing development costs and development time. However, the use of these tools for process set-up and parameter optimization is much less common and based on a trial-and-error approach. When the mould is already built and the piece goes into production, it is no longer feasible to perform such simulations to make decisions about the adjustments to be made on the line in the event of any variation produced, due to the excessive time it would take to make them. This constitutes a significant loss of opportunity since the power provided by simulation tools reproducing the physics of processes stops being useful because of the impossibility of having results immediately, which is the necessary requirement in decision-making to control the process in the injection line.

The main impact for the injection moulding industry is that the process setup is guided by the knowledge provided by the simulation tools, which account for the physics of the process. That is, the knowledge that the physics-based simulation tools provide extends from just being used in the mould design stage, to the process set-up and the part production stages.

This approach complements other commonly used approaches purely based on data captured in the injection line and applying Machine Learning techniques. The main contribution of the present approach is that it provides insight to the process engineer from the very beginning when the mould is set up and there is still no real data available in the monitoring system. In addition, it also provides a layer of interpretability of what happens inside the mould, complementing usually black-box approaches based on neuronal networks.

To achieve this point, the developer previously has to complete a sequence of activities. First to establish the proper knowledge-based simulation model that represents the injection process steps (filling-packing-cooling-part release) and which outputs are estimations of the part quality indicators. These simulations will be exploited through a simulation DOE that will allow exploring the processing window. Reduced-order techniques will allow encapsulating these results in simple models that run on the fly and that will constitute a Digital Twin of the process, being operable through the user interface. Based on data collected directly in the injection line, the developer can also update/correct the Digital Twin for achieving higher accuracy.

<p><a href="https://youtu.be/gqpN4FkkxGg" target="_blank">https://youtu.be/gqpN4FkkxGg</a><a href="https://youtu.be/gqpN4FkkxGg" target="_blank"></a>&nbsp;</p><p><a href="https://digitbrain.eu/1st-wave-of-digitbrain-experiments/digital-brain-for-injection-moulding/" target="_blank">https://digitbrain.eu/1st-wave-of-digitbrain-experiments/digital-brain-for-injection-moulding/</a><a href="https://digitbrain.eu/1st-wave-of-digitbrain-experiments/digital-brain-for-injection-moulding/" target="_blank"></a>&nbsp;<a href="https://digitbrain.eu/1st-wave-of-digitbrain-experiments/digital-brain-for-injection-moulding/" target="_blank"></a></p><p><a href="https://www.sciencedirect.com/science/article/pii/S2590005622000352" target="_blank">https://www.sciencedirect.com/science/article/pii/S2590005622000352</a>&nbsp;<br></p><p><a href="https://github.com/caeliaITAINNOVA/" target="_blank">https://github.com/caeliaITAINNOVA/</a><a href="https://github.com/caeliaITAINNOVA/" target="_blank"></a>&nbsp;</p><p>https://www.itainnova.es&nbsp;<br></p>