670 Industrielle Fertigung
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Automatic content creation system for augmented reality maintenance applications for legacy machines
(2024)
Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
In increasingly complex production environments, tremendous efforts are being made to optimize the efficiency of a production system. An important efficiency factor is industrial maintenance, both influencing the cost and securing the technical availability of machines and components. Maintenance managers are required to deliver the necessary availability of the production system while minimizing the resources needed to do so. To make this possible, a method to evaluate the dependency between the technical availability of an entire production system and maintenance resources is necessary. This paper presents a systematic literature review of such methods is presented. In order to assess the methods proposed in the literature, first, requirements are developed, including a necessary focus on maintenance strategies within these methods. Including maintenance strategies is necessary since they provide the foundation for both the availability of a component and the maintenance resources needed. In total, 13 requirements are developed, and 21 different methods are evaluated. Only one of the proposed methods addresses all requirements, with others lacking possible combinations of maintenance strategies and the resulting influences on the production system.
Cotton contamination by honeydew is considered one of the significant problems for quality in textiles as it causes stickiness during manufacturing. Therefore, millions of dollars in losses are attributed to honeydew contamination each year. This work presents the use of UV hyperspectral imaging (225–300 nm) to characterize honeydew contamination on raw cotton samples. As reference samples, cotton samples were soaked in solutions containing sugar and proteins at different concentrations to mimic honeydew. Multivariate techniques such as a principal component analysis (PCA) and partial least squares regression (PLS-R) were used to predict and classify the amount of honeydew at each pixel of a hyperspectral image of raw cotton samples. The results show that the PCA model was able to differentiate cotton samples based on their sugar concentrations. The first two principal components (PCs) explain nearly 91.0% of the total variance. A PLS-R model was built, showing a performance with a coefficient of determination for the validation (R2cv) = 0.91 and root mean square error of cross-validation (RMSECV) = 0.036 g. This PLS-R model was able to predict the honeydew content in grams on raw cotton samples for each pixel. In conclusion, UV hyperspectral imaging, in combination with multivariate data analysis, shows high potential for quality control in textiles.
Machine failures’ consequences – a classification model considering ultra-efficiency criteria
(2023)
To strive for a sustainable production, maintenance has to evaluate possible machine failure consequences not just economically but also holistically. Approaches such as the ultra-efficiency factory consider energy, material, human/staff, emission, and organization as optimization dimensions. These ultra-efficiency dimensions can be considered for analyzing not only the respective machine failure but also the effects on the entire production system holistically. This paper presents an easy to use method, based on a questionnaire, for assessing the failure consequences of a machine malfunction in a production system considering the ultra-efficiency dimensions. The method was validated in a battery production.
Using predictive maintenance, more efficient processes can be implemented, leading to fewer maintenance costs and increased availability. The development of a predictive maintenance solution currently requires high efforts in time and capacity as well as often interdisciplinary cooperation. This paper presents a standardized model to describe a predictive maintenance use case. The description model is used to collect, present, and document the required information for the implementation of predictive maintenance use cases by and for different stakeholders. Based on this model, predictive maintenance solutions can be introduced more efficiently. The method is validated across departments in the automotive sector.
The increasing complexity and need for availability of automated guided vehicles (AGVs) pose challenges to companies, leading to a focus on new maintenance strategies. In this paper, a smart maintenance architecture based on a digital twin is presented to optimize the technical and economic effectiveness of AGV maintenance activities. To realize this, a literature review was conducted to identify the necessary requirements for Smart Maintenance and Digital Twins. The identified requirements were combined into modules and then integrated into an architecture. The architecture was evaluated on a real AGV on the battery as one of the critical components.
Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes.
Protective welding clothing must meet various requirements. Among other things, it must be flame-resistant, protect against splashes of metal or sparks and also ensure protection against radiant heat and UV light caused by exposure to the welding arc. The protection against molten metal splashes is directly related to the fabric weight per unit area of the protective welding clothing and the level of protection is normally determined by the number of molten metal droplets that fall on the fabric. The higher the weight per unit area, the greater the protection against welding spatter. However, increasing the fabric weight per unit area also leads to psychologically uncomfortable wearing and thus increasing the physical strain on the wearer. The required basis weight per unit area of protective welding clothing can be reduced by applying nanoparticles as a protective layer while preserving other indispensable properties.
Protective welding clothing must meet various requirements. Among other things, it must be flame-resistant, protect against splashes of metal or sparks and also ensure protection against radiant heat and UV light caused by exposure to the welding arc. The protection against molten metal splashes is directly related to the fabric weight per unit area of the protective welding clothing and the level of protection is normally determined by the number of molten metal droplets that fall on the fabric. The higher the weight per unit area, the greater the protection against welding spatter. However, increasing the fabric weight per unit area also leads to psychologically uncomfortable wearing and thus increasing the physical strain on the wearer. The required basis weight per unit area of protective welding clothing can be reduced by applying nanoparticles as a protective layer while preserving other indispensable properties.
Die Anforderungen an Textilien unterscheiden sich je nach Anwendungsbereich stark, wobei es häufig nicht bei nur einer benötigten Funktionalität bleibt. Im Bereich der Funktions- oder Schutzkleidung bzw. PSA ist es z.B. nötig, die Träger der Kleidung vor UV-Strahlung zu schützen. Gleichzeitig bieten hier selbstreinigende Effekte gewisse Vorteile. Zudem kann eine antimikrobielle Wirkung im Bereich der Funktionskleidung die Bildung unangenehmer Gerüche vermindern, sowie im Bereich der PSA – besonders im Gesundheitswesen – zur Unterbrechung von Infektionsketten beitragen. Eine Möglichkeit, diese 3 gewünschten Funktionen in nur einem Ausrüstungsschritt zu erzielen, ist die Immobilisierung von Titandioxid (TiO2). Dieses wird aber aufgrund einer REACH-Listung kritisch für die Anwendung im textilen Sektor gesehen. Nachteilig ist zudem, dass es seine Wirkung nur unter UV-Einstrahlung entfaltet und damit nicht für den Innenbereich geeignet ist. Alternativ können Photokatalysatoren wie dotierte Zinkoxide (ZnO) verwendet werden, die auch durch Einstrahlung im Bereich des sichtbaren Lichts eine katalytische Aktivität aufweisen, die zur Abtötung von Mikroorganismen und zum Abbau organischer Verschmutzungen führen kann.
Schweißerschutzkleidung muss unterschiedlichen Anforderungen genügen. Sie muss u.a. flammfest sein, den Schweißer vor Metallspritzern schützen, die beim Schweißen entstehen, und auch einen Schutz vor UV-Licht sicherstellen, das im Schweißbogen entsteht. Besonders der Schutz vor Metallspritzern wird durch das Flächengewicht der Textilien bestimmt. Der entsprechende Schutzfaktor wird durch Tropfen flüssigen Eisens bestimmt, die auf ein Gewebe fallen. Dabei gilt: je höher das Flächengewicht, desto höher der Schutz vor Schweißspritzern. Jedoch gilt auch: je höher das Flächengewicht, desto schlechter ist der Tragekomfort und desto wärmender ist die Kleidung und damit die körperliche Belastung des Trägers. Durch die Applikation von Nanopartikeln ist es möglich, das benötigte Flächengewicht der Kleidung zu reduzieren.
Der Verschleiß von Werkzeugen bei der Zerspanung mit geometrisch definierter Schneide ist wesentliches Kriterium für die Qualität der bearbeiteten Werkstücke, die Zuverlässigkeit der Bearbeitungsprozesse sowie der Wirtschaftlichkeit. Die Wirtschaftlichkeit der Bearbeitung wird vor allem durch die Anzahl der mit einem Werkzeug zuverlässig bearbeitbaren Werkstücke beeinflusst. Die Standzeit / der Standweg der Werkzeuge sowie die einsetzbaren Technologieparameter sind von unterschiedlichen Faktoren abhängig. Dabei sind neben dem Werkzeug und deren Eingriffsbedingungen (z. B. axiale und radiale Zustellung) auch die Einflüsse seitens der Maschine (z. B. Steifigkeit, Eigenfrequenzen, Drehmoment), des Werkstückes (z. B. Werkstoff, Genauigkeiten) und des Bearbeitungsprozesses mit den dabei auftretenden Kräften, Drehmomenten, Drehzahlen und Vorschüben abhängig. Trotz verschiedener Bemühungen der vergangenen beiden Jahrzehnte zur Bearbeitung ohne Kühlschmierstoff oder mit Minimalmengenschmierung werden heute immer noch zahlreiche Bearbeitungsprozesse unter Einsatz von Kühlschmierstoff durchgeführt. Dadurch lassen sich aufgrund der geringeren thermischen Belastung von Werkzeug und Werkstück teilweise deutlich höhere Schnittbedingungen und/oder Standzeiten erzielen.
Sichtprüfungen von Produktoberflächen werden überwiegend von Mitarbeitern ausgeführt, wobei Automatisierungsansätze mit Kamera- und Bildverarbeitungssystemen großes Potenzial zeigen. Auch Cobots werden in Qualitätssicherungsprozesse einbezogen.Im Folgenden werden die Integrationsmöglichkeiten von Cobots in die Sichtprüfung diskutiert und ein Entscheidungsmodell dargestellt, mit dem Sichtprüfungsprozesse auf ihre Cobot-Tauglichkeit überprüft werden können. Das Entscheidungsmodell ist für die direkte Integration in bereits existierende Cobot-Eignungsuntersuchungsverfahren konzipiert und dient als erste strategische Entscheidungshilfe.
Conventional production systems are evolving through cyber-physical systems and application-oriented approaches of AI, more and more into "smart" production systems, which are characterized among other things by a high level of communication and integration of the individual components. The exchange of information between the systems is usually only oriented towards the data content, where semantics is usually only implicitly considered. The adaptability required by external and internal influences requires the integration of new or the redesign of existing components. Through an open application-oriented ontology the information and communication exchange are extended by explicit semantic information. This enables a better integration of new and an easier reconfiguration of existing components. The developed ontology, the derived application and use of the semantic information will be evaluated by means of a practical use case.
Maintenance is an increasingly complex and knowledge-intensive field. In order to address these challenges, assistance systems based on augmented, mixed, or virtual reality can be applied. Therefore, the objective of this paper is to present a framework that can be used to identify, select, and implement an assistance system based on reality technology in the maintenance environment. The development of the framework is based on a systematic literature review and subject matter expert interviews. The framework provides the best technological and economic solution in several steps. The validation of the framework is carried out through a case study.
This paper presents the concept of the system architecture of a flexible cyber-physical factory control system. The system allows the automation of process structures using cyber-physical fractal nodes. These nodes have a functional and independent form and can be clustered to larger structures. This makes it possible to equip the factory with a flexible, freely scalable, modular system. The description of this system architecture and the associated rules and conditions is outlined in the concept.
Increasing complexity in manufacturing processes poses new challenges for industrial maintenance. In addition, advanced machine monitoring and lifetime forecasting options expand the tools and maintenance strategies available. Today, maintenance strategy selection is performed sequentially usually based on prioritised machines and components. These selections are optimized locally for each machine isolated, not considering the context of other machines within the value-adding network. To overcome these challenges, this paper presents an approach for an integrated maintenance strategy selection in one-step by an integrated model considering possible machine failures and the context of other machines within the value-adding network in parallel.
Railway operators are being challenged by increasing complexity and safeguarding the availability of passenger rolling stock, bringing maintenance and especially emerging technologies into the focus. This paper presents a model for selection and implementation of Industry 4.0 technologies in rolling stock maintenance. The model consists of different stages and considers the main components of rolling stock, the related appropriate maintenance strategies and Industry 4.0 technologies considering the maturity level of the railway operators. Relevant criteria and main prerequisites of the technologies were identified. The model proposes relevant activities and was validated by industry experts.
The technologies of digital transformation, such as the Internet-of-Things (IoT), artificial intelligence or predictive maintenance enable significant efficiency gains in industry and are becoming increasingly important as a competitive factor. However, their successful implementation and creative, future application requires the broad acceptance and knowledge of non-IT-related groups, such as production management students, engineers or skilled workers, which is still lacking today. This paper presents a low-threshold training concept bringing IoT-technologies and applications into manufacturing related higher education and employee training. The concept addresses the relevant topics starting from IoT-basics to predictive maintenance using mobile low-cost hardware and infrastructure.
The maintenance of railway infrastructure remains a challenge. Data acquisition technologies have evolved because of Industry 4.0, expanding the capabilities of predictive maintenance. Despite the advances, the potential of these emerging technologies has not been fully realised. This paper presents a technology selection framework in support of railway infrastructure predictive maintenance, which is based on qualitative methods. It consists of three stages, including the mapping of the infrastructure characteristics with the identified technologies, the evaluation of the most appropriate technologies, and the sourcing thereof. This presents the collective decision support output of the framework.