670 Industrielle Fertigung
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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.