In a production system, production processes demanding high human skills, such as forming processes, require a readjustment of the process parameters of all production steps as a new product evolves. The deficiencies can be mainly attributed to the lack of efficient ways for trusted data sharing and reconfiguration resilience. FLEX4RES has received funding from Horizon Europe (Project number: 101091903) for proving an open platform that supports reconfiguration of production networks to support resilient manufacturing value chains. It will utilize platform-based manufacturing that builds on the state-of-the-art Gaia-X and IDS technologies for data-sharing in the horizontal supply chain and the Asset Administration Shell (AAS) to implement intra-factory reconfiguration practices.
FLEX4RES considers the Digital Twin of the value-adding network a key enabling technology to achieve reconfiguration processes in highly flexible production systems and networks. The key element of technology linkage is represented by the Self-Descriptions with linked, standardized information models, especially in terms of resilience.
The developed platform and specialized hardware aim to improve the existing industry-established lean management approaches related to Reconfiguration Management through the digitalization of the production, characterized as Industry4.0, by allowing for information sharing between value chain stakeholders.
The University of Siegen contributes mainly to multi-stage progressive forming process reconfiguration use case scenario. This involves developing a reconfigurable smart tool system with embodied sensors in collaboration with other partners from the project. The smart tool system comprises a fault detection system and AI-based strategy for effectively adjusting smart tools at different operation stages. Accordingly, it assists a human worker in detecting, identifying, and removing faults at the earliest stage. This includes the physical display devices, their setup, the devices used to detect human feedback, the communicated content, and the interaction concept between the worker and the AI system. This project is crucial for achieving highly flexible production systems and networks where reconfiguration of processes is critical.
Im Produktportfolio, welches zur Herstellung i. d. R. von OEMs an Zulieferer weitergegeben wird, befinden sich sehr oft Karosseriebauteile, die mit der vorherrschenden Fertigungstechnik bei zuliefernden Mittelständlern nicht immer kostendeckend gefertigt werden können. Ursache ist eine hohe Anzahl an Varianten bei geringen Losgrößen. Statt solche Teile als Teilumfang großer Aufträge teuer und unprofitabel mitzuproduzieren, könnte eine varianten- und kapazitätsflexiblere Produktion geometrisch individualisierter Bauteile für mittelständische Zulieferer attraktiv und gewinnbringend machen.
Hierzu hat ein Konsortium mit 8 Partnern im Rahmen des Förderprogramm KoPa 35c des Bundesministeriums für Wirtschaft und Energie (BWMi) erfolgreich das Projekt SkaLaB beantragt (Förderkennzeichen: 13IK025B). Die Universität Siegen ist mit den Lehrstühlen FAMS, UTS und WiNeMe am Projekt SkaLaB beteiligt. Projektstart ist der 01.01.2023.
Ziel ist die Entwicklung und Erprobung hochflexibler, in Serie herstellbarer Herstellungscenter für in allen Dimensionen skalierbare Karosserieblechbauteile (Geometrie, Halbzeug, Werkstoff, Fertigungsmenge). Die Herstellcenter sollen erstmals ermöglichen, die Prozessreihenfolge in der Serienproduktion bauteilindividuell verändern zu können. Damit sollen die Herstellkosten für neue, geometrisch unterschiedliche Karosserievarianten gesenkt werden.
Bislang verwendet die Folgeverbundfertigung von Blechbauteilen fest verknüpfte Umformstufen zur Herstellung eines Bauteils. Mehrere Bauteile werden später im Zusammenbau zu Baugruppen gefügt. Stand heute werden praktisch keine Fertigungsfolgen in der Blechteileherstellung, die Fügen und Umformen koppeln bzw. mischen, genutzt. Ebenso erfolgt die Aufteilung von Umformschritten fest verknüpft unter einem Stößel, so dass Optimierungen an einer Stelle auch immer Auswirkungen auf alle anderen Stufen haben.
Durch ein technisches Auflösen der Einzelschritte und Fertigungsfolgen kann eine deutliche Erhöhung der Flexibilität erreicht werden. So kann z. B. eine variantenbildende Unterscheidung durch Schweißen eines bereits gebogenen Teils in der Prozesskette nach hinten verlagert werden, um Losgrößeneffekte zu erzielen. Notwendig ist dann jedoch eine intelligente Verknüpfung durch einen Prozessgenerator. Dieser wahrt zum einen die Produktivität und stellt zum anderen die geforderten Teileeigenschaften sicher. alt: Trennen → Umformen 1 → Umformen 2 → … → Spannen → Fügen neu: Trennen → Umformen 1→ Spannen → Fügen → Umformen 5 → Umformen 4 → Trennen Fügen → Umformen 2 → Fügen
Human-robot collaboration (HRC) combines the capabilities of humans and robots in order to create a more inclusive and human-centered future in the production industry. The capabilities of HRC have been investigated extensively for manufacturing processes, mainly in laboratory environments. However, they are not widely applied in production systems. Performance and safety can be primary challenge that limits collaboration efficiency, particularly for motions at higher speeds. The collaborative handling of an object is one of the current challenges facing HRC’s real collaboration capabilities. In this context, how robots and humans should behave to handle an object and anticipate motion for mutual care is still unclear. One approach is to create robot motions that avoid risks to human body parts caused by high speeds of the robot tool center point (TCP), which might result from reactions to human motions. Coupling motion models for such approaches is difficult because, unlike industrial robots, human motion is not easily modeled using geometric or analytical formulations such as rigid body dynamics. Instead, data-driven models for human motion generation are employed in various research and industrial applications such as sports, health, film, ergonomics, and production.
HiSMoT has received funding from DFG (Project number: 500490184) in collaboration with Ruhr University of Bochum to investigate real-time capable approaches to coupling data-driven human motion models with robot motion models in HRC applications.
In this project the role of the Chair for Production automation and Assembly (FAMS) is to investigate in what way human and robot motion models can be coupled and implemented using motion capture-driven approaches. In this context, latent space control approaches are investigated for the capability to follow the high mutual support and anticipation principle.
Corrosion is an electrochemical process with the environment, leading to the gradual deterioration of metallic components. To prevent corrosion, various methods and procedures are employed, such as hot-dip galvanizing or painting, which are collectively known as corrosion protection. Hot-dip galvanizing is a batch process in which pre-manufactured iron and steel parts are dipped into a molten zinc bath.
Particularly for components with demanding and complex geometries, such as scaffold tubes with galvanized inner surfaces, manual post-processing is often required. However, the manual rework process is highly labor-intensive, leading to a high degree of monotony and fatigue for workers. Furthermore, a minimum layer thickness must be maintained, which cannot be compromised during the manual finishing process. This necessitates the use of highly skilled professionals with significant experience in the precise post-processing of hot-dip galvanized components. As a result, there is a significant shortage of skilled labor in the industry.
The goal of this research and development project, in collaboration with Jabertools & Robotics, is to develop a robot-based, self-learning, and automated finishing process for hot-dip galvanized components. The aim is to fully automate the detection and finishing of defects on the surface of hot-dip galvanized parts.
Additive manufacturing systems and Industry 4.0 are recent major innovations that have great potential to revolutionize production. The conventional manufacture of complex parts from individual components followed by joining them to assemblies could decrease significantly if 3D printing solutions with integrated functions become mainstream. At the same time, there will be new challenges, such as in upstream and downstream processes. In particular, this will affect medium-sized companies that specialize in managing current manufacturing processes and then have to prepare for this new situation early on. However, it is precisely small and medium-sized companies that shy away from the initial investments required to establish knowhow because they do not trust the relevance and usability of new technologies in their environment.
SmaPS is a research infrastructure project that allows five chairs at the University of Siegen to utilize new technologies (3D printing, rigidity measurements of tools, movements of workers by means of motion capture). Companies, and in particular small and medium-sized companies, have the opportunity to collaborate with the university at the SmaPS Center that will be created as part of this project and simultaneously avoid large investments of their own that are often not feasible. SmaPS can be used as an extended experimentation bench allowing experiments to be conducted between the phases of technology development and technical usability and free from all production pressure.
The FAMS Chair is involved in two planned investment in the project:
Sensors for the capture of human motions during production The FAMS Chair has acquired three inertial sensor-based systems for the capture
of human motions. These include two xsens MVN Link systems in which the sensors are connected to cables and which operate at 240 Hz as well as a more robust xsens Awinda system that has cable-free sensors and operates at 60 Hz. The xsens systems are complemented by two pairs of Manus VR gloves used to track finger motions as well as two Pupil Invisible glasses to track eye movements. These systems also allow workers’ motions to be captured precisely in the production environment.
Benefit: Digital work planning based on real motion sequences, optimized workstation efficiency and ergonomics
Additive manufacturing system for laser metal deposition The FAMS Chair is currently in the process of acquiring an additive manufacturing
system for metals in a collaboration with the Chair of Production Development (Prof. Reinicke). The system in question is a laser beam melting system (LBM method) that can be used to deposit metal on existing parts, to join parts by means of deposition and to laser cut parts.
Benefit: Deposition of complicated and delicate geometries, sensor integration in components and reconditioning of used tools
Technological innovations must inevitably be introduced to the market by means of suitable production. New manufacturing techniques such as additive manufacturing or process automation enable the cost-effective implementation of previously uneconomical ideas.
To support local industry, the University of Siegen did a ZIM research project (Central Innovation Program for SMEs) for new roller technology in cooperation with a local manufacturer. On the one hand, the project aimed to renew traditional production and make it more economical, and on the other, to explore the possibilities for product improvements through innovative production techniques and automation.
By integrating the latest knowledge in the field of production technology, product improvements such as sensor integration, redesigns and efficiency improvements to increase sustainability are to be made possible and implemented. Thanks to its research expertise in the field of additive manufacturing, collaborative robot systems and experience in modeling manual assembly processes, the FAMS chair is particularly well suited to introducing innovative technologies into industry.
One core idea of Industry 4.0 is the representation of production as a digital shadow. But why does this often involve an oversimplification of human behavior? “Human motions vary so greatly that modeling them using current methods is a lot of work,” says Professor Martin Manns, Chair of Manufacturing Automation and Assembly at the University of Siegen.
The ITEA3 research project, End-to-end Digital Integration based on Modular Simulation of Natural Human Motions (MOSIM), strives to change this by combining processes from the gaming industry with those used in production research. A modular system of human motion modules (called motion model units) that can be put together to form 3D simulations will be developed. The goal is not only to allow the simulation of optimal target motions but also of the most common actual motions with little effort. These simulations can be used in automotive assembly, in the construction industry and in pedestrian simulations.
The MOSIM project includes four national consortia from Germany, Sweden, Finland and Austria with a total of 22 participating partners. The consortia are headed by Daimler AG. Academics partners in the German consortium include the University of Siegen and the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI – German Research Center for Artificial Intelligence) in Saarbrücken.
In a production system, production processes demanding high human skills, such as forming processes, require a readjustment of the process parameters of all production steps as a new product evolves. The deficiencies can be mainly attributed to the lack of efficient ways for trusted data sharing and reconfiguration resilience. FLEX4RES has received funding from Horizon Europe (Project number: 101091903) for proving an open platform that supports reconfiguration of production networks to support resilient manufacturing value chains. It will utilize platform-based manufacturing that builds on the state-of-the-art Gaia-X and IDS technologies for data-sharing in the horizontal supply chain and the Asset Administration Shell (AAS) to implement intra-factory reconfiguration practices.
FLEX4RES considers the Digital Twin of the value-adding network a key enabling technology to achieve reconfiguration processes in highly flexible production systems and networks. The key element of technology linkage is represented by the Self-Descriptions with linked, standardized information models, especially in terms of resilience.
The developed platform and specialized hardware aim to improve the existing industry-established lean management approaches related to Reconfiguration Management through the digitalization of the production, characterized as Industry4.0, by allowing for information sharing between value chain stakeholders.
The University of Siegen contributes mainly to multi-stage progressive forming process reconfiguration use case scenario. This involves developing a reconfigurable smart tool system with embodied sensors in collaboration with other partners from the project. The smart tool system comprises a fault detection system and AI-based strategy for effectively adjusting smart tools at different operation stages. Accordingly, it assists a human worker in detecting, identifying, and removing faults at the earliest stage. This includes the physical display devices, their setup, the devices used to detect human feedback, the communicated content, and the interaction concept between the worker and the AI system. This project is crucial for achieving highly flexible production systems and networks where reconfiguration of processes is critical.
Im Produktportfolio, welches zur Herstellung i. d. R. von OEMs an Zulieferer weitergegeben wird, befinden sich sehr oft Karosseriebauteile, die mit der vorherrschenden Fertigungstechnik bei zuliefernden Mittelständlern nicht immer kostendeckend gefertigt werden können. Ursache ist eine hohe Anzahl an Varianten bei geringen Losgrößen. Statt solche Teile als Teilumfang großer Aufträge teuer und unprofitabel mitzuproduzieren, könnte eine varianten- und kapazitätsflexiblere Produktion geometrisch individualisierter Bauteile für mittelständische Zulieferer attraktiv und gewinnbringend machen.
Hierzu hat ein Konsortium mit 8 Partnern im Rahmen des Förderprogramm KoPa 35c des Bundesministeriums für Wirtschaft und Energie (BWMi) erfolgreich das Projekt SkaLaB beantragt (Förderkennzeichen: 13IK025B). Die Universität Siegen ist mit den Lehrstühlen FAMS, UTS und WiNeMe am Projekt SkaLaB beteiligt. Projektstart ist der 01.01.2023.
Ziel ist die Entwicklung und Erprobung hochflexibler, in Serie herstellbarer Herstellungscenter für in allen Dimensionen skalierbare Karosserieblechbauteile (Geometrie, Halbzeug, Werkstoff, Fertigungsmenge). Die Herstellcenter sollen erstmals ermöglichen, die Prozessreihenfolge in der Serienproduktion bauteilindividuell verändern zu können. Damit sollen die Herstellkosten für neue, geometrisch unterschiedliche Karosserievarianten gesenkt werden.
Bislang verwendet die Folgeverbundfertigung von Blechbauteilen fest verknüpfte Umformstufen zur Herstellung eines Bauteils. Mehrere Bauteile werden später im Zusammenbau zu Baugruppen gefügt. Stand heute werden praktisch keine Fertigungsfolgen in der Blechteileherstellung, die Fügen und Umformen koppeln bzw. mischen, genutzt. Ebenso erfolgt die Aufteilung von Umformschritten fest verknüpft unter einem Stößel, so dass Optimierungen an einer Stelle auch immer Auswirkungen auf alle anderen Stufen haben.
Durch ein technisches Auflösen der Einzelschritte und Fertigungsfolgen kann eine deutliche Erhöhung der Flexibilität erreicht werden. So kann z. B. eine variantenbildende Unterscheidung durch Schweißen eines bereits gebogenen Teils in der Prozesskette nach hinten verlagert werden, um Losgrößeneffekte zu erzielen. Notwendig ist dann jedoch eine intelligente Verknüpfung durch einen Prozessgenerator. Dieser wahrt zum einen die Produktivität und stellt zum anderen die geforderten Teileeigenschaften sicher. alt: Trennen → Umformen 1 → Umformen 2 → … → Spannen → Fügen neu: Trennen → Umformen 1→ Spannen → Fügen → Umformen 5 → Umformen 4 → Trennen Fügen → Umformen 2 → Fügen
Human-robot collaboration (HRC) combines the capabilities of humans and robots in order to create a more inclusive and human-centered future in the production industry. The capabilities of HRC have been investigated extensively for manufacturing processes, mainly in laboratory environments. However, they are not widely applied in production systems. Performance and safety can be primary challenge that limits collaboration efficiency, particularly for motions at higher speeds. The collaborative handling of an object is one of the current challenges facing HRC’s real collaboration capabilities. In this context, how robots and humans should behave to handle an object and anticipate motion for mutual care is still unclear. One approach is to create robot motions that avoid risks to human body parts caused by high speeds of the robot tool center point (TCP), which might result from reactions to human motions. Coupling motion models for such approaches is difficult because, unlike industrial robots, human motion is not easily modeled using geometric or analytical formulations such as rigid body dynamics. Instead, data-driven models for human motion generation are employed in various research and industrial applications such as sports, health, film, ergonomics, and production.
HiSMoT has received funding from DFG (Project number: 500490184) in collaboration with Ruhr University of Bochum to investigate real-time capable approaches to coupling data-driven human motion models with robot motion models in HRC applications.
In this project the role of the Chair for Production automation and Assembly (FAMS) is to investigate in what way human and robot motion models can be coupled and implemented using motion capture-driven approaches. In this context, latent space control approaches are investigated for the capability to follow the high mutual support and anticipation principle.
Corrosion is an electrochemical process with the environment, leading to the gradual deterioration of metallic components. To prevent corrosion, various methods and procedures are employed, such as hot-dip galvanizing or painting, which are collectively known as corrosion protection. Hot-dip galvanizing is a batch process in which pre-manufactured iron and steel parts are dipped into a molten zinc bath.
Particularly for components with demanding and complex geometries, such as scaffold tubes with galvanized inner surfaces, manual post-processing is often required. However, the manual rework process is highly labor-intensive, leading to a high degree of monotony and fatigue for workers. Furthermore, a minimum layer thickness must be maintained, which cannot be compromised during the manual finishing process. This necessitates the use of highly skilled professionals with significant experience in the precise post-processing of hot-dip galvanized components. As a result, there is a significant shortage of skilled labor in the industry.
The goal of this research and development project, in collaboration with Jabertools & Robotics, is to develop a robot-based, self-learning, and automated finishing process for hot-dip galvanized components. The aim is to fully automate the detection and finishing of defects on the surface of hot-dip galvanized parts.
Additive manufacturing systems and Industry 4.0 are recent major innovations that have great potential to revolutionize production. The conventional manufacture of complex parts from individual components followed by joining them to assemblies could decrease significantly if 3D printing solutions with integrated functions become mainstream. At the same time, there will be new challenges, such as in upstream and downstream processes. In particular, this will affect medium-sized companies that specialize in managing current manufacturing processes and then have to prepare for this new situation early on. However, it is precisely small and medium-sized companies that shy away from the initial investments required to establish knowhow because they do not trust the relevance and usability of new technologies in their environment.
SmaPS is a research infrastructure project that allows five chairs at the University of Siegen to utilize new technologies (3D printing, rigidity measurements of tools, movements of workers by means of motion capture). Companies, and in particular small and medium-sized companies, have the opportunity to collaborate with the university at the SmaPS Center that will be created as part of this project and simultaneously avoid large investments of their own that are often not feasible. SmaPS can be used as an extended experimentation bench allowing experiments to be conducted between the phases of technology development and technical usability and free from all production pressure.
The FAMS Chair is involved in two planned investment in the project:
Sensors for the capture of human motions during production The FAMS Chair has acquired three inertial sensor-based systems for the capture
of human motions. These include two xsens MVN Link systems in which the sensors are connected to cables and which operate at 240 Hz as well as a more robust xsens Awinda system that has cable-free sensors and operates at 60 Hz. The xsens systems are complemented by two pairs of Manus VR gloves used to track finger motions as well as two Pupil Invisible glasses to track eye movements. These systems also allow workers’ motions to be captured precisely in the production environment.
Benefit: Digital work planning based on real motion sequences, optimized workstation efficiency and ergonomics
Additive manufacturing system for laser metal deposition The FAMS Chair is currently in the process of acquiring an additive manufacturing
system for metals in a collaboration with the Chair of Production Development (Prof. Reinicke). The system in question is a laser beam melting system (LBM method) that can be used to deposit metal on existing parts, to join parts by means of deposition and to laser cut parts.
Benefit: Deposition of complicated and delicate geometries, sensor integration in components and reconditioning of used tools
Technological innovations must inevitably be introduced to the market by means of suitable production. New manufacturing techniques such as additive manufacturing or process automation enable the cost-effective implementation of previously uneconomical ideas.
To support local industry, the University of Siegen did a ZIM research project (Central Innovation Program for SMEs) for new roller technology in cooperation with a local manufacturer. On the one hand, the project aimed to renew traditional production and make it more economical, and on the other, to explore the possibilities for product improvements through innovative production techniques and automation.
By integrating the latest knowledge in the field of production technology, product improvements such as sensor integration, redesigns and efficiency improvements to increase sustainability are to be made possible and implemented. Thanks to its research expertise in the field of additive manufacturing, collaborative robot systems and experience in modeling manual assembly processes, the FAMS chair is particularly well suited to introducing innovative technologies into industry.
One core idea of Industry 4.0 is the representation of production as a digital shadow. But why does this often involve an oversimplification of human behavior? “Human motions vary so greatly that modeling them using current methods is a lot of work,” says Professor Martin Manns, Chair of Manufacturing Automation and Assembly at the University of Siegen.
The ITEA3 research project, End-to-end Digital Integration based on Modular Simulation of Natural Human Motions (MOSIM), strives to change this by combining processes from the gaming industry with those used in production research. A modular system of human motion modules (called motion model units) that can be put together to form 3D simulations will be developed. The goal is not only to allow the simulation of optimal target motions but also of the most common actual motions with little effort. These simulations can be used in automotive assembly, in the construction industry and in pedestrian simulations.
The MOSIM project includes four national consortia from Germany, Sweden, Finland and Austria with a total of 22 participating partners. The consortia are headed by Daimler AG. Academics partners in the German consortium include the University of Siegen and the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI – German Research Center for Artificial Intelligence) in Saarbrücken.