FG Automatisierungstechnik
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Institute
Albeit augmented reality (AR) technologies first have been discovered in the third quarter of the 20th century, their widespread use began just two decades ago. Existing paper trails show that AR has a wide range of industrial application: it simplifies human-machine communication, improves human-machine interfaces (HMI) for quick information exchange in training (including feedback to study the workflow), correction of errors, machine maintenance, assembly assistance etc. However, broader industrial acceptance of AR, prior to all by small and medium-sized enterprises (SMEs), recently faced considerable problems and the expansion of AR solutions does not match the high potential it has demonstrated. That results in a limited practical use, mainly for demonstration and advertising purposes. This short review is to present the state of the art of the industry, challenges that SMEs face in adopting AR technologies, and several practical examples of a (commercial) adoption of AR. Some prospects for further development of AR and its ongoing integration into industry are briefly discussed in the summary.
The introduction of both mobile robots (MR) and AI-embedded mobile robots (AIMR) into the industry is very slow compared to other types of industrial robots (IR) and automation systems. Many scientific articles and studies are focused on the programming and design of MR. At the same time, integration issues, topical problems and related obstacles are almost entirely absent from the scientific literature. The authors of this paper acknowledge that the complete analysis of this area is a very challenging task. Therefore, for the purposes of this study, we focus on the local problem of analysing the introduction of MR and AIMR in small and medium-sized enterprises (SMEs). The authors offer the analysis of the current challenges and trends in the introduction of mobile robots into SMEs. They also propose solutions to these problems based on their own as well as external experience in the design, programming and implementation of mobile robots.
Evaluation of Marker-Based AR-Tracking with Vuforia in the Context of Rail Vehicle Maintenance
(2023)
Maintenance and repair jobs present a multitude of challenges. Digital assistance systems (DAS) can be used to support workers in tackling these challenges. One way of achieving this is by using a tablet with AR-technology to display necessary information seemingly in the real world. In order to place information at the correct positions, the DAS needs to know exactly where it is currently located. Multiple approaches are available to achieve this. One of those approaches is to use a marker which is placed somewhere in the real world as an anchor point for the system. In the context of rail vehicle maintenance, the workspace is inherently large with rail wagons having lengths of more than 10 m. This means that a marker that is placed somewhere on the wagon will not always be in the field of view of the DAS’s camera, resulting in a possible reduction of the precision with which virtual objects can be displayed. This paper examines the viability of the marker-based approach under realistic circumstances. It was found that with a distance of multiple meters between marker and a spot that is to be highlighted, the precision will decrease significantly.
Durch die Menschzentrierung in der Industrie 5.0 [1] sollten bestehende Ansätze hinterfragt und geprüft werden [2]. Gerade der, durch die Digitalisierung hervorgerufene, Technostress sollte neben dem Fachkräftemangel einen zunehmenden Betrachtungsfaktor im Produktionsumfeld erhalten. Da trotz bisheriger Maßnahmen eine stetige Zunahme von Stress zu verzeichnen ist [3]. Auffällig sind dabei die Arbeitsunfähigkeits-Fehltage, welche im letzten Jahr um weitere 6 % bzw. um 18,2 Fehltagegestiegen sind. Womit durchschnittlich jede Erwerbsperson 3,17 Tage unter der Diagnose „psychische Störung“ krankgeschrieben wurde [4]. Um diesen Trend entgegenzuwirken, betrachtet der Beitrag die Möglichkeiten der Software-Ergonomie, womit die Gestaltung von HMIs (Mensch-Maschinen-Interfaces) den Menschen gezielter berücksichtigt um etwaige Belastungen zu reduzieren [5]. Im Rahmen der komplementären Industrie 4.0 und der einhergehenden Informationsflut, bestimmen immer komplexer werdende Schnittstellen und Bedienoberflächen einen Großteil des Arbeitsalltags. Diese ermöglichen den Anwenderinnen und Anwendern neben einem schnellen Überblick über Maschinendaten, Prozessabläufe und Handlungsempfehlungen die Möglichkeit zur einfachen Bedienung der technischen Anlagen. Der hier vorgestellte Ansatz betrachtet am Beispiel einer Bestandsanlage, wo sich Potenziale in der Stressreduzierung im Bereich der visuellen Benutzerschnittstellen befinden und welche Auswirkungen eine Veränderung der Leitführung ermöglicht. Hierfür wurde eine mehrphasige Studie durchgeführt dessen Ergebnisse im Bereich des Eye-Trackings sowie anhand von Vitalwerten im vorliegenden Beitrag diskutiert werden.
Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR’s stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble.
The Augmented reality (AR) technologies have been first discovered in the third quarter of the twentieth century. However, the wider development of them has taken place only in the last two decades. By now, the research has shown that AR can be used in various areas of human activity. In industry, AR simplifies humanmachine communication and improves human-machine interfaces (HMI) for fast and feedback-provided retrieval of training/guidance information for operation pattern study, error correction, machine maintenance, assembly assistance, etc. In spite of that, the broad practical implementation of AR in industry, including small and medium-sized enterprises (SMEs), has faced considerable problems. As a result, the following controversy emerged: the comprehensive study of AR is combined with a rather narrow practical use primarily for advertising and demonstration tasks. This chapter attempts not only to overview the current state of AR in the industry, but also demonstrate the current challenges the AR is facing, as well as to analyse their respective causes and suggest solution ideas. It is also intended to assess the prospects for further development of AR and its continued integration into the industry. For this purpose, several examples of AR projects, their development, practical use and upgrading (performed by the authors of this study as well) are presented.
The Smart Production Vision
(2022)
In this chapter, the Smart Production vision is discussed. The Smart Production approach is developed and described, and Smart Production is positioned in relation to Industry 4.0. Smart Production operationalize the journey towards Industry 4.0 and beyond. First, the need for a new approach to manufacturing is discussed, and from the perspectives of Industry 4.0, the Smart Production concept is derived. Then the framework is explored and finally, the approach is outlined. The Smart Production vision is an approach to make an integrated production system smarter by continuous digitizing, automating, and organizing towards supporting the company specific missions.
Qualification in Small and Medium-Sized Enterprises as the Key Driver for a Digitalized Economy
(2022)
Transformation processes towards a smart production reshape the way we work. Digitalization and the application of new technologies lead to additional interfaces between man and machine and thus, result in a tremendous change of employment, work environment and workforce qualification. This in turn means, to take full advantage of digitalization employees at all levels and sectors have to be evolved in the transformation process and need to be skilled for the upcoming challenges. To meet this need for qualification and to sensitize especially small and medium-sized enterprises (SMEs) to the matter of digitalization, the Competence Center Cottbus was founded.
Through a four-step approach, the Center addresses SMEs at all digitalization levels, from beginners to innovators. The central instrument for the Centers’ work is the self-developed LTA-FIT qualification concept. This concept is designed to impart the required knowledge and competences in accordance with the distinct qualification level of SMEs and their employees.
Intelligent Assistance Systems are a key technology for solving the problem of today’s quickly changing product configurations and the corresponding production requirements focusing on human needs. A recent study among industrial German companies identifies higher productivity, process control, quality, and cost-effectiveness as the main potentials of assistance technologies by reducing the worker’s cognitive load. Choosing the right combination of assistance technologies such as augmented reality, virtual reality, machine learning, and their functionalities like learning capabilities, situation awareness, and assembly activity recognition remain essential for their success. This chapter presents an assistance systems overview, focusing on assistance technologies, for application at production sites. Furthermore, an example of a gesture recognition-based assistance system is given, showing the benefits arising from today’s advanced assistance technologies.
This chapter will introduce the second part of the book. This part contains a collection of chapters aimed at supporting the SMEs in the transformation toward the Smart Production vision. In this part, different approaches are presented, which can assist SMEs in the formulation of a smart production vision and in the identification and prioritization of relevant initiatives, guiding the outline of a project roadmap. Furthermore, the part will introduce different regional innovation platforms in Denmark and Germany which support the SME transformations. Finally, it will be discussed how subscription-based methods could be used by SMEs to cut upfront investments and reduce requirements for digital competencies.
This book explains and exemplifies how SMEs can embrace the Smart Production approach and technologies in order to gain a beneficiary outcome. The book describes the Smart Production vision for SMEs, as well as the method to get there. The concept behind the book is based on the long-term experience of the authors in researching and tackling problems of SMEs in the manufacturing sector. The book provides applied methods and obtained solutions in different branches and different sizes of SMEs, encompassing a broad survey of our markets and societies. The perspective is systemic/holistic and integrated including human, organizational, technological, and digital perspectives.
Borck et al. evaluate the challenges and opportunities of Industrial Internet of Things (IIoT) and smart sensors in human-centered manufacturing. Particularly in small and medium-sized manufacturing with fewer machines and smart tools, it is significantly more difficult to automate processes and get the required information from the shop floor. Therefore, they give proven recommendations for the use of sensors based on a set of frequently occurring tasks in assembly, maintenance and logistics to achieve the support of smart data models in the context of Industry 4.0. “IIoT and smart sensors in human-centered manufacturing” concludes with concrete sample scenarios and describe the challenges and one solution using smart sensors and data models.
Schmitt et al. provide a brief overview of the key technology for Industry 4.0, the Industrial Internet of Things (IIoT). For this purpose, the paper first examines how this technology has developed, what its essential components are and what challenges still need to be overcome. The work particularly presents the specific requirements, challenges and opportunities for SMEs. “Industrial Internet of Things (IIoT)” concludes with an example about the integration of IIoT in a model factory.
Digitalization and automation represent both a major challenge and a long-term opportunity for SMEs to secure their business success. They often lack specialist knowledge and a sufficient financial scope and therefore need external support in meeting these challenges. This is of particular relevance and political interest as Brandenburgs economy is determined by SMEs. Knowledge and technology transfer between scientific institutions and companies is seen as a possible solution. However, different goals, approaches and expectations of researchers and companies inhibits or complicates a successful cooperation. Therefore, transfer intermediaries are needed as mediators.The book chapter uses the example of IMI Brandenburg to show how knowledge and technology transfer can be implemented by intermediaries and what key findings were obtained in the process. Furthermore, possible success factors for both the digitization of Brandenburg's SMEs and successful knowledge and technology transfer are discussed.The chapter addresses decision-makers in SMEs as well as research institutions and transfer intermediaries and is intended to raise awareness of the opportunities, challenges and solutions in the cooperation between science and industry.
Abstract. As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal humanmachine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user’s input.However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task.
Learning factories constitute a promising approach for the acquisition of specific competencies, especially in terms of a digital transformation of the economy. Respectively, a variety of such factories differing in technology, learning concept, and potential audience have evolved. A precise and recent overview of those does not exist. However, such an overview is required for the implementation of concrete political measures, a future-oriented development of the individual learning factories, and an adequate selection by the audience. For this purpose, the authors investigate the current state of the art of European learning factories in the context of digitization. Thus, the terminology and definition of learning factories are provided. Moreover, using a structured literature review, the factories and their operation mode are outlined. Subsequently, the authors evaluate whether the different factories can build the required competencies among the audience and thus, support a successful digital transformation. Additionally, expert interviews with learning factory operators are performed to obtain profound information on the performance of learning factories. The findings help to assess the pertinency of European learning factories and provide a trace for their future development.
Intelligente Assistenzsysteme unterstützen die Mitarbeiter in der Produktion und erhöhen die Effizienz durch das Einblenden von situationsbasierten Aufgabeninformationen. Im Projekt Advanced Robot Assistance Solution (ARAS) im Rahmen des Kuka Innovation Awards 2021 wurde eine Assistenztechnologie entwickelt, um automatisiert Roboterprogramme für Montageabläufe zu generieren. Durch innovative Mensch-Maschine-Schnittstellen werden Montageschritte per maschinellem Lernen erkannt und in ausführbare Programme für Industrieroboter übersetzt. Dadurch können roboterbasierte Montageprozesse innerhalb von Minuten auf neue Produkte angepasst werden, ohne dass die Mitarbeiter über Kenntnisse des Programmierens oder der Robotik verfügen müssen. Ein Mitarbeiter muss den Montageprozess nur einmal vormachen. Das ARAS-System ermöglicht die kosten- und zeiteffiziente Integration und Adaption von Industrierobotern in der Montage für große und mittelständische Unternehmen.
In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70%, thus furthering the necessary development needed to have a system ready to be used on a production environment.
The advent of the industrial digital transformation and the related technologies of the Industry 4.0 agenda has uncovered new concepts and terminology in the manufacturing domain. Clear definitions represent a solid foundation for supporting the manufacturing research community in addressing this field consistently. This paper addresses this need focusing on the “smart factory”. Starting from a review of the extant literature and integrating it with the outcome of a Delphi study, we propose a new definition of a “smart factory” and discuss its key characteristics. These are related to interconnectivity capabilities and adaptability to the surrounding environment in order to generate and appropriate value. Eventually, such characteristics are exemplified in an empirical context. The aim of this paper is to provide the research community with an updated definition of a smart factory taking both industrial and societal values into account. Furthermore, it may represent a reference for practitioners engaged in the digital transformation of their factories.
In the industry, connecting machines and tools - also known as the industrial Internet of things (IIoT) - is an essential part of the digital transformation of a company. The aim is to increase the efficiency and predictability of complex processes. In manual and semi-automatic processes, imaging sensors can help to monitor conditions, gives automated feedbacks to a central system, and e.g. provide current information for a digital twin. However, when imaging sensors are integrated into established IIoT platforms, they quickly reach their system limits due to the multidimensionality and high update and data rates. This paper presents a software platform that enables decoupled automated image processing through the abstraction and contextualization of the sensor technology and its data as well as a plugin architecture. Analogous to edge computing, partial processing can already be performed close to the sensor node to condensate data and reduce network loads and latencies. Thereby, all these approaches increase the longevity, flexibility and scalability of multi-sensor systems and associated processing algorithms. Based on the generic structure of the sensor network, the user is provided with an intuitive user interface that is based on IIoT platforms and enables the integration of their processing pipelines even for non-experts, despite the high complexity of the data.
The fast and safe motion of goods and people is one of the foundations of the modern world. Jet aircraft is the fastest transport at the moment along with high-speed trains. Accordingly, both production and maintenance of aircrafts are an important task of our modern industrial environment as well. Modern aircraft engines require appropriate care and understanding of design and manufacturing. This is even more important, as the production of aerospace engines remains a manual process in many cases with limited data sources. Thus, quality control will need to take into account verification of manufacturing and assembly steps through specific checks and controls whilst implementing additional data sources. Automation of tasks still is at a low level. In this article a review of the challenges with regard to controls, automation and process and technical understanding for aerospace engine production and repair is provided. As this requires the collaboration of many teams and partners, an improvement and step change towards deeper understanding and process efficiency is required. As many operations remain manual, innovations for how humans interact with the technology and collaborate with an industrial environment are needed. The project in this article demonstrates the creation and usage of the proposed solutions for collaboration, troubleshooting and error correction.
This minitrack will address scientific and technical research on how traditional production systems are transformed into next generation smart production systems. It comprises a mix of presentations from leading industries (automotive and aircraft) as well as from well-known research institutions (Fraunhofer society, Germany) and universities (Brandenburg University of Technology Cottbus-Senftenberg, University of Bergamo, Aalborg University).
The minitrack will foster discussions concerning the strategies companies are adopting for managing such transformation, the need for new competences for sustaining it, the implementation of new digital technologies and their impact on the existing production systems.
Even though digitization is a topic of high interest and promises remarkable benefits for the German manufacturing industry, it is still in its development phase. Thus, necessities arising from it are not predictable, yet. A great obstacle that will occur is the appropriate qualification of employees at all levels, sectors and firm sizes. The integration of new technologies will have radical impact on the role and job profile of all staff members as they alter the requirements across the whole value chain and lead to new processes. Latest studies showed that an insufficient qualification of employees is one of the greatest challenges for a successful digitization. Thus, to regain a globally leading position in the manufacturing industry German companies, especially small and medium sized enterprises (SME), need to qualify their employees appropriately. The LTA-FIT model is an approach that aims to face these challenges.
The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best.
The Digital Twin is seen as the step towards digitalization in many areas, including the area of manufacturing which is the focus of this paper. However, the frequently application-oriented development of concepts and initial implementations make it difficult to adapt them to other applications. Especially when production deviates from the usual highly automated processes and requires high flexibility, e.g. to allow batch size one. In such a case, the Digital Twin must support modular shopfloors with flexible areas in which different production times and various tools and machines are used. It should also be possible to react promptly to problems in production. Design errors that only become apparent during production must be reported directly. In order to deal with this problem, the paper presents a concept for an abstract representation of a Digital Shadow (DS). The DS combines the disciplines of production planning and control, product lifecycle and layout planning which creates new links and enables further application scenarios. Based on this Digital Shadow, different applications can be realized that provide user-related solutions. For this purpose, a tree structure is used that links information about the product and the production environment which in this case is variable.
Digital Business Model Innovation in SMEs - Case Studies with DIH Support from Brandenburg (Germany)
(2020)
A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era
(2019)
The digital transformation of the production sector is setting the scene for a major industrial change. The need for supporting companies in this transformation is currently covered by several maturity models, generally operationalized through standardized questionnaires, which provide, as an outcome, an assessment of the current maturity stage and a set of general improvement recommendations according to it. However, to provide companies with a more tangible support, there is a need for more individual approach. In order to deal with this need, this paper proposes, following a design science research framework, a novel approach based on Problem-Based Learning for structuring the assessment procedure as a dialectic process. This approach aims at facilitating the contextualization of the assessed company and, consequently, the identification of context-specific improvement recommendations. The proposed approach, supported by a maturity model used for framing information collected during the assessment process, is tested in three industrial cases. Although these have been assessed at the same maturity stage, different improvement recommendations have been proposed according to contextual factors such as strategic goals, core processes and key performance indicators.
Mensch-Roboter-Kollaboration im Spannungsfeld von Rekonfigurierbarkeit, Sicherheit und Effizienz
(2019)
Digitalization is widely accepted as a principal technological strategy and is expected to vastly impact and transform industry, predominantly by a substantial improvement of the entire value chain. However, its implementation process remains slow, in particular for small and medium-sized enterprises (SMEs), as they tend to have misconceptions about the complexity and expense of digitalization. The purpose of this research is to analyze factors that affect Brandenburg-State, DEU SMEs in their decision making process for an effective implementation of digital technologies in their daily business routine. The results of this study are based on a survey that was conducted in cooperation with fifty SMEs, which currently face problems in the areas of enterprise resource planning (ERP), CAD/CAM, automation, logistics and production/factory planning. Ten of these SMEs have decided to invest in digitalization measures and started the implementation process. Three of them were chosen for the case study to evaluate potential benefits, trade-offs and barriers hampering the implementation of digitalization technologies. These three companies are considered representative items, since their needs and measures investigated were identical to most of the companies of this survey. It was found that the overwhelming majority of the companies regard ERP implementation as their highest priority when investing in the most cost-effective technology.
Industry 4.0 is still in its development phase and it promises to bring remarkable benefits to the manufacturing industry around the world when employing the Smart Factory application in large organizations and their supply chains. However, there is a risk of a miss-match when trying to introduce Industry 4.0 to Small and Medium Enterprises (SME) as the concept is mainly being developed around large manufacturing companies. The purpose of this research is to analyze the readiness level and feasibility of implementing Industry 4.0 technologies for SME’s in the federal state of Brandenburg (Germany). The work is based on the survey of 20 SME’s assessing their current problems emphasizing on automation, Enterprise Resource Planning (ERP), CAD/CAM, factory layout planning and logistics. Five SME’s from different domains out of the 20 surveyed are taken as case studies to evaluate the potential benefits, trade-offs and barriers from an implementation of these integrated technologies. The findings revealed that the companies are still coping with the issues relating to planning, logistics and automation. It was also found that all the concepts of i4.0 may not be necessary or even beneficial to an enterprise in the current scenario and new strategies need to be developed for its realization in SME’s.
As Convolutional Neural Network based models become reliable and efficient, two questions arise in relation to their applications for industrial purposes. The usefulness of these models in industrial environments and their implementation in these settings. This paper describes the autonomous generation of Region based CNN models trained on images from rendered CAD models and examines their applicability and performance for part handling application. The development of the automated synthetic data generation is detailed and two CNN models are trained with the aim to detect a car component and differentiate it against another similar looking part. The performance of these models is tested on real images and it was found that the proposed approach can be easily adopted for detecting a range of parts in arbitrary backgrounds. Moreover, the use of syntheic images for training CNNs automates the process of generating a detector.
This work describes three technical improvements to the handling assistant, a collaborative robot for handling and commissioning, concerning the depth of information the robot can extract from the teaching process. The teaching process involves an unskilled worker setting up the robots movements and actions through hand guiding to instruct the robot to perform a given task. The possibilities of improvements in picking, placement and path demonstrated are identified and solutions for the same are presented. The task of picking of the part is improved through the use of null space motion of the robot and exploiting part symmetry. The task of placement of parts is improved through the use of data collected during the demonstration. Finally the path of the robot is optimized using regression with a cost function targeted minimizing the time required for path movement. The developed methods are implemented and validated on a test case using a handling assistant which has a KUKA iiwa robot and collaborative gripper.
Due to the extensive changes along with Industry 4.0 many established barriers drop. One of those barriers which increasingly disappears separates the workspace between human beings and robots and thus prevents a versatile collaborating environment. This change has led to the development of a wide field of research focusing on the prevention of human-robot impacts and/or the minimization of related risks. This paper is focused on safety regulations introduced as a consequence of those developments over the last ten years and particularly on an approach for demonstrating the unintended gap arising between human and robot due to those regulations. Modularity and integrability as two of the key characteristics in the concept of Reconfigurable Manufacturing Systems (RMS), serve as the comparative parameters.
The transformation of the manufacturing sector towards Industry 4.0 is setting the scene for a major industrial change. Currently, the need for assisting companies in this transformation is covered by a number of maturity models that assess their digital maturity and provide indications accordingly. However, in order to provide operational recommendations to diverse companies, there is a need for making the assessment company-specific. To cope with this challenge, this paper provides an illustration of a new digital maturity assessment approach - 360 Digital Maturity Assessment - which is based on the Problem Based Learning (PBL) model.
Entwicklung eines modularisierten mobilen Manipulatorsystems für die flexible automatisierte Montage
(2017)
Die steigende Produktvielfalt und die Anforderung nach höherer Flexibilität in der Produktion sowie nach Steigerung der Produktivität erfordern im industriellen Umfeld eine Erhöhung des Automatisierungsgrades. Vor diesem Hintergrund steigert sich der Robotereinsatz in der Industrie seit Jahren. Jedoch liegt der Automatisierungsgrad im Montagebereich immer noch auf einem niedrigen Niveau, weil entweder der hohe Zeitbedarf zur Entwicklung eines Robotersystems und/oder der hohe Aufwand zur Umsetzung ins Zielsystem hinderlich auf die Erhöhung des Automatisierungsgrads wirken. In diesem Paper wird eine neue Methode zur Entwicklung eines Robotersystems für Montageaufgabe vorgestellt. Diese Methode zielt auf Erstellung einer Montageaufgabe durch Anwendung der Erweiterten Realität (Augmented Reality, Abkürzung AR). Eine virtuelle Montageaufgabe mit virtuellen Bauteilen wird händisch ausgeführt, dadurch wird diese Montageaufgabe visualisiert dargestellt und automatisch im Zielsystem erstellt.
In this paper the use of a mobile lightweight robot is evaluated to perform an assembly task while simultaneously moving. The motion of the mobile platform results into a variable end effector position in space. The paper assesses
the existing method of placing a screw in an assembly where the location of placement is variable due to end effector movement. Experiments have been conducted to evaluate the task performance by monitoring the applied force on the
end effector, the position data and the task time. The results show that with impedance configuration, a moveable compliant robot is a possible solution for use in assembly operation.
In this paper a flexible robot system for assembly operations in a semi structured environment is proposed. The system uses a moveable robot equipped with an arbitrarily mounted 3D camera and a 2D camera on the robot gripper for accurate object detection. The system is developed based on the Robot Operating System (ROS) and uses the state of the art trajectory planner MoveIt for collision free robot motion planning. The object recognition for the system is performed using a combination of the two camera's and robot end effector and the deviation of objects in the robot workspace is found to be in the range of 2.2 mm along X and Y axes. Furthermore, an implementation procedure of the proposed system for assembly task is explained.