FG Automatisierungstechnik
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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.
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.
A Multilevel Reconfiguration Concept to Enable Versatile Production in Distributed Manufacturing
(2011)
The manufacturing industry is confronting challenges due to high diversity of product variants,reduced product life cycles,short innovation cycles, faster time to market as well as strictenvironmental regulations. These challenges have persuaded manufacturers to exploit conceptsrelated to open innovation, distributed manufacturing, modular and scalable production systemdesign and eco-efficient production. This paper aims at providing a short review of state of the art inreconfiguration of distributed production systems and focuses on new strategies to resolvecomplexities that arise subsequently. In this regard, a reconfiguration concept based on newstrategic objectives has been proposed to enable customized production. The approach will beimplemented and validated in the collaborative projects.
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.
A variant-flexible assembly cell for hydraulic valve sections using a sensitive lightweight robot
(2016)
A vision for an information management tool for plant engineering - functionality and user interface
(2004)
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 automotive industry is distinguished by regionalization and mass customization of products. As a consequence, the diversity of products will increase and the lot sizes will decrease. Thus more product types have to be handled along the process chain and common production paradigms will fail. Hence, Rapid Manufacturing (RM) will be used for manufacturing small individual lot sizes. Nevertheless, new solution for joining and assembling these components are needed. The state of the art production control solutions exhibit several disadvantages while responding mass customization. A comprehensive and real time intelligent production control and monitoring system can overcome these limitations which will be the totality of intelligent production control and will be based on intelligent soft computing algorithms and smart intelligent peripheral devices. The concept is based on three interlinked main modules: a technology data catalogue (TDC) to access, share, process and structure relevant engineering data, an automated scheduling processor (ASP) based on graph theory to get an optimized and adaptable work plan using feature technology and a central programmable automation controller (PAC) for real-time sensor/actor communication. The processes will be controlled through PAC and influenced by humans through scalable human machine interfaces. The PAC and its supported software’s widespread functionality for real time applications and compatibility and flexibility with the conventional programming languages will enable in developing customized palette of functions and tools for scalable level of control solutions. It will have a widespread accessibility as it will influence the execution of robot programs after pre-processing the data coming from various peripheral devices and calculating new robot paths by segmentation of detected curves and paths corresponding to the process planning information via one interface. The human machine communication will be based on the ontological approach as a natural language interaction system for filtering and translating into machine command. The entire concept will be demonstrated in a laboratory set-up with distinct machining, assembly and joining processes and will be experimentally validated in European research and development projects.
Die in diesem Artikel beschriebene, praktische, Verwendung einer Augmented Reality Umgebung findet statt für die Fehlerbeseitigung und Fehlerkorrektur bei der Arbeit in einer Laboranlage, die verschiedene Fehler simuliert kann. Innerhalb der Anlage sind alle wesentlichen Komponenten mit Sensorik ausgestattet, so dass jederzeit Informationen über
den Zustand des Systems in Echtzeit bereitliegen. Sobald sich einen Fehler innerhalb der Anlage befindet, kann das Steuerungssystem durch die vorliegenden Sensordaten dieses
Problem identifizieren. Die verschiedenen Teile der Anlage sind mit Positions-Markern zur Identifizierung eines
Anlagenbereiches ausgerüstet. Wird die Anlage durch einen Fehler gestoppt ist, muss der Mitarbeiter die Anlage mit Hilfe des Kamerasystems eines mobilen Endgerätes (mit dem
Tablet) erfassen. Die erfassten Daten aus diesem Scan werden mit Hilfe der Software verarbeitet. Durch die visuelle Rückmeldung (z.B., auf dem Tablet) erhält der Anwender die Information über den aufgetretenen Fehler innerhalb der Anlage und zur Behebung des Zustandes. Auf
Grundlager der erkannten Marker können positionsgenau Objekte eingeblendet werden die dem Anwender den Ort des Fehlers anzeigen und Hinweise zur Bedienerführung zur
Aufnahme des ordnungsgemäßen Betriebs der Anlage geben. Der Anlagenführer wird in die Lage versetzt, die Fehlerbeseitigung und Fehlerkorrektur schnell und erfolgreich zu realisieren. Ein Einsatz des Systems für Schulungsprozesse ist vorgesehen, da sich dies
positiv auf die Verringerung von Stillstandszeiten auswirkt. So demonstriert das beschriebene, realisierte Verfahren wie die praktische Verwendung der
AR zur Reduzierung des zeitlichen Aufwandes für die Fehlerbeseitigung und die Fehlerkorrektur der Maschinenumgebung bei der Mensch-Maschinen-Integration in Industrie 4.0 Umgebungen beiträgt und den gesamten Integrationsprozess zwischen Mensch und Maschine vertiefet.
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.
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.
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.
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.
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.
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.
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.
Das Konzept eines Engineering Portals zur benutzerzentrierten Unterstützung der Anlagenplanung
(2006)
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 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.
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.