Search

Given today’s demand shift for product variants triggered by different market segments, manufacturing companies are facing high degrees of complexity, when utilising their global production network...

Low information exchange in global production networks results in long response time to disruption and negative performance impact. Digitalization enables a more intensive information exchange. This paper analyses the performance of order management, quality problem resolution and engineering change management in production networks with respect to different disruptions and information flows. Cause-effect relationships are revealed based on a multi-method simulation model and statistical experiments. Using surrogate modelling and robust optimization, a target picture for information exchange is determined. The benefits of the approach are demonstrated using a case study for the production of metal-plastic parts for the automotive supplier industry.

Additive manufacturing (AM), and particularly metal laser powder bed fusion (L-PBF) processes are rapidly being industrialized. Still, the current L-PBF process lacks both process quality and reproducibility. For that reason, robust process monitoring needs to be developed to reduce the process variation and ensure quality. Accordingly, to deal with this issue, this work proposes a new approach to predict the quality of L-PBF products. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability to predict the different products’ density classes. The approach was applied on cubical specimens produced with different process parameters. Support vector machines (SVMs) were used as classification tools, and the first results are promising with a prediction accuracy higher than 90%.”

During turning of quenched and tempered AISI4140 surface layer states can be generated, which degrade the lifetime of manufactured parts. Such states may be brittle rehardened layers or tensile residual stresses. A soft sensor concept is presented in this work, in order to identify relevant surface modifications during machining. A crucial part of this concept is the measurement of magnetic characteristics by means of the 3MA-testing (Micromagnetic Multiparameter Microstructure and Stress Analysis). Those measurements correlate with the microstructure of the material, only take a few seconds and can be processed on the machine. This enables a continuous workpiece quality control during machining. However specific problems come with the distant measurement of thin surface layers, which are analyzed here. Furthermore the scope of this work is the in-process-measurement of the tool wear, which is an important input parameter of the thermomechanical surface load. The availability of the current tool wear is to be used for the adaption of the process parameters in order to avoid detrimental surface states. This enables new approaches for a workpiece focused process control, which is of high importance considering the goals of Industry 4.0.

Kurzfassung Produktionsanlaufe an verschiedenen Standorten globaler Produktionsnetzwerke scheitern haufig an der verspateten und unkoordinierten Reaktion der Unternehmen auf eintretende Storungen. ...

Kurzfassung Die Nachverfolgbarkeit (engl. traceability) von Produkten entlang ihres Lebenszyklus ist eine Grundvoraussetzung für zahlreiche Anwendungsfelder: der Ermöglichung von Kreislaufwirtschaftsprozessen, dem Schutz vor Produktpiraterie und der zielgerichteten Qualitätssicherung. Jedoch existieren über die gesetzlich vorgeschriebenen Standards hinaus wenig umfassende Lösungen, um Produkte unternehmensübergreifend nachzuverfolgen. Dieser Beitrag zeigt ein Konzept zur strukturierten Konzeption, Implementierung und Bewertung solcher Traceability-Systeme.

Für produzierende Unternehmen nehmen Smart Services weiter an Bedeutung zu. Daher ist eine einheitliche Methode wichtig, um diese Services im Umfeld von Produktionsnetzwerken zu entwerfen sowie Geschäftsmodelle für netzwerkbasierte Smart Services aufstellen zu können. Es wird ein Vorgehen vorgestellt, um Smart Services im Kontext von Produktionsnetzwerken zu modellieren. Der Ansatz soll helfen, Geschäftsmodelle für diese Services aufzustellen. Smart services are becoming increasingly important for manufacturing companies. Therefore, a uniform method is required for designing these services in the environment of production networks as well as to establish business models for network-based smart services. A method for modelling smart services in the context of production networks is presented. This approach helps to establish business models for these services.

Das Forschungsprojekt „Prozessintegrierte Softsensorik zur Oberflächenkonditionierung beim Außenlängsdrehen von 42CrMo4“ widmet sich der Entstehung und der In-process-Erfassung von industriell relevanten Randschichtzuständen. Im Speziellen werden sogenannte White Layer und Eigenspannungszustände untersucht. Durch die modulare Verknüpfung von zerstörungsfreier Prüftechnik, Simulationsergebnissen und Prozesswissen mittels Datenfusion wird ein Softsensor erforscht. Dieser soll im Rahmen einer adaptiven Regelung des Drehprozesses eingesetzt werden und eine gezielte Einstellung von vorteilhaften Randschichtzuständen erlauben. The research project „Process-integrated soft sensor technology for surface conditioning during external longitudinal turning of 42CrMo4“ is dedicated to the formation and in-process-detection of surface layers with industrial relevance. In particular, so-called white layers and residual stresses are investigated. A soft sensor is being researched through the modular combination of non-destructive testing technology and process knowledge by means of data fusion. This is to be used in the context of an adaptive control of the turning process in order to adjust beneficial surface states.

Die additive Fertigung schafft neue Gestaltungsfreiheiten. Im Rahmen des Prototypenbaus und der Kleinserienproduktion kann das Verfahren des selektiven Laserschmelzens genutzt werden. Die Verwendung in der Serienproduktion ist bisher aufgrund unzureichender Bauteilqualität, langen Anlaufzeiten sowie mangelnder Automatisierung nicht im wirtschaftlichen Rahmen möglich. Das Projekt „ReAddi“ möchte eine erste prototypische Serienfertigung entwickeln, mit der additiv gefertigte Bauteile für die Automobilindustrie wirtschaftlich produziert werden können. Additive manufacturing (AM) offers new freedom of design. The selective laser-powderbed fusion (L-PBF) process can be used for prototyping and small series production. So far, it has not been economical to use it on a production scale due to insufficient component quality, long start-up times and a lack of automation. The project ReAddi aims to develop a first prototype series production to cost-effectively manufacture 3D-printed components for the automotive industry.

Kurzfassung Die zunehmende Globalisierung führte in den letzten Dekaden zu global verteilten Produktionsnetzwerken. Um solche Netzwerke und die darin befindlichen Fabriken auf strategischer Ebene auszurichten, haben sich idealtypische Modelle bewährt, die als Leitlinien einer fundierten Fabrik- und Netzwerkorganisation zu verstehen sind. Dieser Beitrag beschäftigt sich mit solchen idealtypischen Modellen und zeigt exemplarisch auf, wie sie sich für das Management globaler Produktionsnetzwerke einsetzen lassen.

Zusammenfassung Das selektive Laserstrahlschmelzen (LBM) steht aktuell an der Schwelle zum Einsatz für Kleinserien. Ein wesentlicher Nachteil des Verfahrens ist aktuell noch die geringe Reproduzierbarkeit der Prozessqualität. Einige aktuelle Forschungsarbeiten konzentrieren sich deshalb auf die Integration optischer Messtechnik zur Prozessüberwachung. Neben den optischen Verfahren zeigen erste Untersuchungen, dass auch akustische Sensoren zur Prozessüberwachung ein vielversprechender Ansatz sind. Eine große Herausforderung bei den akustischen Daten stellt die Datenverarbeitung dar, da das akustische Rohsignal nur schwer zu interpretieren ist. In dieser Arbeit wird ein neues Konzept für ein akustisches Prozessüberwachungssystem vorgestellt und in eine Versuchsumgebung integriert. Zum Aufzeichnen akustischer Signale werden in einem Design of Experiments Prozessparameter gezielt variiert und Testkörper verschiedener Bauteilqualität aufgebaut. Für einen ersten Nachweis der Eignung des Messsystems zur Überwachung des Prozesses wird ein künstliches neuronales Netz trainiert, um die verwendeten Prozessparameter (drei Laserleistungen) zu bewerten. Damit kann gezeigt werden, dass diese Messtechnik das Potential hat, den Prozess zu überwachen.

Zusammenfassung Das Abwälzverhalten von Mikroverzahnungen erzeugt akustische Emissionen, welche im Rahmen von zunehmenden Qualitätsanforderungen zu minimieren sind. Auf Basis von In-Line-Topographiemessungen mittels Fokusvariation wird in diesem Artikel ein Ansatz geschildert, welcher auf die Prognose und Optimierung des Geräuschverhaltens der Verzahnungen abzielt. Durch bestehende Ansätze der flächigen Zahncharakterisierung lassen sich die optisch aufgenommen Messdatensätze flächig auswerten. Um eine valide Aussage über die Messergebnisse geben zu können, werden erste Ergebnisse einer experimentellen Messunsicherheitsermittlung der Fokusvariation präsentiert.

Discontinuous fiber-reinforced Sheet Moulding Compound (SMC) in combination with continuous carbon fiber patches provide a high design freedom in combination with good weight-specific properties. However, the application of these materials requires a strategic and exact positioning of the patches, necessitating the consideration of unavoidable manufacturing defects during the design phase. Therefore, a workflow is proposed to evaluate the robustness of multi-objective patch optimization results using two robustness measures, the degree of robustness and robustness index. An efficient calculation of the robustness measures is achieved by replacing computational expensive simulation models with a Kriging surrogate model. Typical manufacturing deviations occurring during the patch positioning and moulding process are determined from experiments using active thermography. Finally, the proposed workflow is applied to the multi-objective optimization of two patches on a demonstrator under four-point-bending load. The resulting robustness measures can be used as a decision criterion for the selection of the best Pareto optimal solution. Furthermore, they can be used for the determination of the maximum occurring objective variation as well as the largest permissible manufacturing tolerances.

Kurzfassung Vielen kleinen und mittleren Unternehmen bedarf es eines effizienten Managements des Produktionsanlaufs, um ihre globale Wettbewerbsfähigkeit zu erhöhen. Dieser Beitrag stellt einen Ansatz vor, mithilfe einer Standortanalyse und eines Planungs- und Steuerungsmodells den Produktionsanlauf an verschiedenen Standorten effizient durchzuführen. Hierzu werden gegebene Rahmenbedingungen eines Produktionsanlaufs an global verteilten Standorten analysiert und mit Hilfe einer Methodik einem Referenzprozess zugeordnet, um eine Anpassung an wechselnde globale Einflussfaktoren, wie z. B. Faktorkos-ten oder Marktentwicklungen, zu gewährleisten.

Kurzfassung Die Technologie der Datensammlung und -analyse hat sich in den letzten Jahren vor allem durch die revolutionäre Entwicklung von Indus-trie 4.0 in der Produktion stark verändert. Viele verschiedene Mess­systeme und Sensoren nehmen Daten zu jedem Zeitpunkt der Produktion auf und speichern diese in den jeweiligen proprietären Datenspeichersystemen. Durch Middleware werden diese Daten vernetzt und können mittels einer Datenanalyse für Produktallokationen nutzbar gemacht werden. Im Folgenden wird eine Methodik vorgestellt, mit der Data-Analytics-Verfahren für Produktallokationen in globalen Produktionsnetzwerken angewendet werden können.

Zusammenfassung Die aktive Thermografie wird zur Detektion von Schäden in kohlenstofffaserverstärkten Kunststoffen (CFK) verwendet. Aufgrund des bildgebenden und tomografischen Messverfahrens wird eine zerstörungsfreie Prüfung von CFK-Schäden ermöglicht, wie sie z. B. die Serienproduktion von Fahrzeugen fordert. Das indirekte Messverfahren misst die IR-Strahlen infolge einer aktiven Anregung des Prüfobjektes. Anregungsformen, welche im industriellen Umfeld Anwendung finden, sind die sinusförmige Lock-in und die pulsförmige Puls-Phasen Thermografie. In beiden Verfahren wird die erfasste Wärmestrahlung mittels Fast Fourier Transformation in ein Phasenbild überführt. Die quantitative Auswertung erfolgt anhand der gemessenen Phasenwerte und des Phasenbildes. Während die Lock-in Thermografie den Vorteil der Rauschunterdrückung und Detektion von tieferliegenden Schäden bietet, kann durch die Puls-Phasen Thermografie eine zeiteffizientere und somit wirtschaftlichere Messung erfolgen. In diesem Beitrag wird eine automatisierte Auswertung von Bildaufnahmen mittels Lock-in und Puls-Phasen Thermografie entwickelt und ein quantitativer Vergleich beider Verfahren hinsichtlich messtechnischer und wirtschaftlicher Kriterien der Schadensdetektion von CFK-Strukturen durchgeführt.

Schalenformige Bauteile zeichnen sich durch ein sehr groses Verhaltnis von Breite oder Lange zur Wanddicke aus. Durch die geringe Wandstarke kommt der Einleitung von Lasten in derartige Strukturen eine besondere Bedeutung zu. Hierfur werden haufig spezielle Lasteinleitungselemente (Inserts) in die Struktur eingebracht, die als Anbindungspunkte dienen. Die Kombination von metallischem Lasteinleitungselement und CFK-Struktur wird anhand drei verschiedener Teilprojekte untersucht. Im Projekt „Multilayer-Inserts – Intrinsische Hybridverbunde zur Krafteinleitung in dunnwandige Hochleistungs-CFK-Strukturen“ wurde ein Lasteinleitungselement fur automatisiert gefertigte Faserverbundstrukturen entwickelt. Das Projekt „Grundlagenuntersuchungen intrinsisch gefertigter FVK/Metall-Verbunde – vom eingebetteten Insert zur lasttragenden Hybridstruktur“ untersucht die faserschonende, intrinsische Herstellung von FVK/Metall-Verbunden im RTM-Prozess anhand verschiedener Hybridisierungsansatze. Im Projekt „Einfluss, Detektion und Vorhersage von Defekten in grosserientauglichen Hybridverbunden fur Metall/CFK-Leichtbautragstrukturen“ wurde ein neuartiges Anbindungskonzept fur Metall-CFK-Hybridstrukturen mit thermoplastischer Zwischenkomponente entwickelt. Im Rahmen dieses Kapitels werden die Ergebnisse der Teilprojekte detailliert vorgestellt und erortert.

The intrinsic production of metal-CFRP (carbon fiber-reinforced polymers) hybrid structures allows a load-path-optimal design of connecting components. However, the intrinsic production of complex hybrid 3D parts is prone to defects such as delaminations or fiber misalignments. Fiber misalignments of the woven fabric especially occur in the region of the metal insert. Pulsed phase thermography (PPT) is widely used for non-destructive testing of fiber-reinforced polymers, especially for delaminations. Laser light section (LLS) is suitable for generating a 3D cloud of points (CoP) of the component to be measured. The usage of two laser light section sensors allows in-line measurements of complex 3D geometries by preventing shades. An in-line quality assurance of the finished hybrid component is performed by data fusion of LLS and PPT. The resulting 3D thermographic data can be used to locate and quantify defects. In this contribution, a method for detecting individual fiber bundles in woven fabrics and quantifying the 3D fiber alignment based on the fused LLS and PPT data is presented. This could allow for the simultaneous detection of delaminations and fiber misalignments using the same measurement technology.

The Laser Powder Bed Fusion (L-PBF) process is being adopted in different industrial fields. However, L-PBF currently lacks process reproducibility and quality. Hence, quality monitoring techniques need to be adopted in order to reduce the process variability and to ensure a high-quality process. Accordingly, this work proposes a quality monitoring approach based on machine learning which links the optical signal of a layer to the density of the final part. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability in predicting the different density classes of different products. Afterwards, the approach is compared to a new deep learning framework that allows predicting a part density from the corresponding raw optical signals. This comparison allows assessing the relevance of the identified statistical features. The proposed monitoring approach is applied on cubical specimens produced with different process parameters, and the results are then discussed and analyzed.

The ongoing individualization of products forces companies to focus more on flexibility and adaptability. To achieve this, cooperating companies need to intensify their cooperation into collaboration. New types of collaboration are enabled by a wave of digitalization reducing organizational efforts and risks of collaboration. Especially smart services can be a medium to incentivize more interaction. This paper contributes a method to connect the loose ends from the study of collaboration on a strategic level to the digitalization trend on an operational level, representing all key aspects of a collaboration like relevant stakeholders, case of application and system interrelation.

In this work an experimental study of the turning of AISI4140 is presented. The scope is the understanding of the workpiece microstructure and hardness-depth-profiles which result from different cutting conditions and thus thermomechanical surface loads. The regarded input parameters are the cutting velocity (vc = 100, 300 m/min), feed rate (f = 0.1, 0.3 mm), cutting depth (ap = 0.3, 1.2 mm) and the heat treatment of the workpiece (tempering temperatures 300, 450 and 600°C). The experimental data is interpreted in terms of machining mechanisms and material phenomena, e.g. the generation of white layers, which influence the surface hardness. Hereby the process forces are analyzed as well. The gained knowledge is the prerequisite of a workpiece focused process control.

Due to excess of resource consumption, circular economy (CE) aims to return products to the production life cycle in an economically and ecologically reasonable way. Yet, few approaches focus on the strategic and network level of CE. First, existing approaches regarding the configuration of CE networks and the development of appropriate business models are reviewed. Second, an approach on how to integrate both aspects is presented. By integrating these aspects, implementation strategies for CE in global production networks shall be developed facilitating strategic decisions. The approach is planned to be demonstrated in a German company supplying the automotive sector.

Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives.

Collaboration in global production networks becomes more important in times of increased interconnectivity and complexity. However, due to various resistors the collaboration potential has not been realized, yet. At the same time digitalization has become a key enabler in today’s world of high complexity leading to new, disruptive solutions. Part of digitalization are smart services, triggering incentives by including business models. This and further characteristics of smart services have the potential of overcoming the resistors of collaboration. In this paper an approach is proposed for developing collaborative relationships - from strategy and collaboration scenario modelling to a service-oriented implementation.

Quality problems of screwing processes in assembly systems, which are an important issue for operation excellence, needs to be quickly analyzed and solved. A network can be very beneficial for root cause analysis due to different data from various factories. Nevertheless, it is difficult to obtain reliable and consistent data. In this context, this paper aims to develop a method for data-driven oriented quality analytics of screwing processes considering a global production network. Firstly, the overview of data structure is introduced. Further, the data transformation is modelled for edge- and cloud-based analytics across the global production network. Lastly, the rules for analyzing are identified. A joint case study based on Learning Factory Global Production (LF) in Germany and I4.0 Innovation Centre and Artificial Intelligence Innovation Factory (IC&AIIF) in China is used to validate the proposed approach, which is also a new teaching method for quality analysis in the framework of learning factory.

Kurzfassung Der Serieneinsatz der additiven Fertigung ist maßgeblich durch die hohen Kosten und der geringen Produktivität der Verfahren limitiert. Der hier vorgestellte Ansatz zeigt, wie die Wirtschaftlichkeit des Laser-Strahlschmelzens (LBM) durch die Kombination mit etablierten Fertigungsverfahren erhöht werden kann. Ziel ist es, nur solche Funktionsträger additiv zu fertigen, die einen höheren Kundennutzen bringen. Dazu werden Konstruktionsrichtlinien definiert, Prozessketten erarbeitet und eine Qualitätssicherung mittels Ultraschallüberwachung realisiert.

Pulse-phase thermography (PPT) is widely used to nondestructively inspect internal defects in fiber reinforced polymers. However, the challenges using PPT for complex shapes is poorly documented in literature. Only small changes in the object distance have been considered. Complex parts can have significant variations in object distance and thus, in detected radiation. In this contribution, the effect of a non-orthogonal camera angle with respect to a flat sample, leading to varying object distances and an inhomogeneous sound background area in phasegrams, is investigated. Samples with artificial round and square defects of different sizes are positioned under varying angles with respect to the camera, representing geometric properties of complex parts. The construction of the thermographic system and the experimental setup to systematically vary the angle between camera and specimen is presented. We investigated the change of the signal-to-noise ratio (SNR) of artificial delaminations in PPT measurements under varying object distances. The SNR in a distance of 136 mm out of the focal plane is sufficiently high for image feature extraction. Phasegrams are exported to a colored representation, leading to a higher contrast in distinct color channels. An algorithm which extracts and merges defect information from three different color channels is developed. Challenging lighting conditions lead to a noisy background having artifacts. The developed filter performs better in defect detection and size quantification than a global or local threshold in grayscale phasegrams under those conditions.

New technologies can yield high market potential, but also challenge engineering capabilities. For example, additive manufacturing enables unlimited freedom of design and economical production of small batch sizes. However, there are huge challenges: A large variety of new additive technologies, limited choice of materials and mostly high production cost as result of long production time. Since today’s production requires an economical implementation, focus needs to be on hybrid production, which combines the advantages of additive and conventional manufacturing technologies. This requires an integrated optimization of the product design, the manufacturing technology chain and the operative equipment. The following paper presents an approach for this integrated planning approach with the aim of economically feasible hybrid production. In general, the interdependencies between product and manufacturing technology need to be used for optimization in early stages of the product life cycle. To achieve a high customer value, the product requirements have to be analyzed in detail to find an optimal product function, but also to identify degrees of design freedom, which do not influence product function and, thus, can be adapted to optimize production. Moreover, possible changes in the capabilities of manufacturing technologies and, subsequently, operative equipment and machines can be anticipated to further enhance the production. After identifying optimal combinations of product design and manufacturing technology chains, the selection and optimal configuration of the operative equipment is necessary and needs to be validate based on the final product design. The integration of product design, manufacturing technology optimization and operative process planning enables companies to identify and realize high economic potential early in their value creation process and thus can contribute to improving competitiveness.

Today, companies of all industries are part of global production networks. They have a variety of performance relationships with suppliers and customers. Digitalization offers the potential to exchange more information between the partners of global production networks. This may improve operational performance. Especially within the three business processes order management, quality problem solving and engineering change management, a targeted increase in transparency promises a better handling of disruptions and an increase in robustness. This paper presents a simulation-based methodology for modeling production and business processes as well as information exchange in global production networks. Following the principles of Design of Experiment (DoE), screening test plans first carve out the impact of disruptions and information exchange on the performance of the production network. This is followed by the determination of the disruption-robust information exchange using Taguchi-experiments. Starting from the actual state of information exchange, digitalization activities to increase transparency are finally determined. The activities consist of the implementation of digitalization technologies and the stronger linkage of information systems. The paper ends with an application of the methodology to a global production network for plastic-metal components in the automotive supplier industry.

In the current VUCA-world (Volatility, Uncertainty, Complexity & Ambiguity) collaboration is a potent method to increase the efficiency of sluggish, historically grown global value added networks and, thus, face the resulting new challenges. Although the different benefits of collaboration, within as well as between different companies, are already well known, the collaboration level is not reaching its potential. At the same time the production principles are disrupted by digitalization. The influences of the digital era can be noticed by the increasing servitization of manufacturing. Therefore, the state of the art needs to be updated in order to illustrate the new conditions. Especially services will have a big impact on the way how companies interact with each other. In this article an approach is presented to classify different Smart Services regarding different properties and the resulting requirements for realization. This framework is a premise for the goal to reevaluate collaboration scenarios based on Smart Services and, therefore, a premise for a higher level of collaboration in global value added networks.

Sheet Molding Compounds (SMC), a type of fiber-reinforced polymers, are popular lightweight material due to their handling and processability. The fiber mass content (FMC) as well as the fiber orientation distribution determine mechanical properties of manufactured components. However, local deviations from the intended FMC occur during the production of the semi-finished material as well as during the molding itself. In this contribution, SMC was investigated regarding its properties in the Terahertz (THz) spectrum to non-destructively quantify the local FMC. SMC with different FMC between 38 and 54 wt% was industrially produced and dry glass fibers were added to further increase the glass fiber content. Reference measurements of the FMC were conducted via thermogravimetric analysis and showed a mean deviation from the intended FMC of 2.64 wt%. The peak arrival time, the transmitted energy and the refractive index of the THz signal were determined and assessed regarding their correlation with the FMC. A multivariate regression analysis including backward elimination led to a low bias statistical model between the refractive index and the FMC with a cross-validated root mean square error (RMSE) of 1.33 wt%.

Steigende Anforderungen an die Produktqualität stellen Unternehmen vor die Herausforderung, günstige Produkte nahe der technologischen Fertigungsgrenzen zu produzieren. Die Paarung von Montagekomponenten mit angepassten Produktionsstrategien bietet in diesem Umfeld mögliche Lösungsansätze. Neue Informations- und Kommunikationstechnologien im Kontext von Industrie 4.0 eröffnen hierfür neuartige Möglichkeiten. Companies are faced with increasing product quality requirements to manufacture high quality products close to technological limits in a cost-effective way. The pairing of assembly components provides an approach to cope with this challenge by adapted production strategies. New information and communication technologies offer novel opportunities within „Industrie 4.0“.

Industrie 4.0 ist ein Enabler für wandlungsfähige Montagesysteme. Am Beispiel der skalierbaren Automatisierung eines Montagesystems für Elektromotoren wird der effektive Einsatz von Industrie 4.0-Konzepten und -Komponenten aufgezeigt. Exemplarisch werden die Applikation einer dezentralen Steuerung sowie ein integriertes Robotiksystem vorgestellt. Erst die Kombination der Industrie 4.0-Elemente erlaubt ein plug&produce-fähiges Montagesystem, das auf veränderte Anforderungen reagieren kann. Industrie 4.0 is an enabler for changeable assembly systems. A scalable automation assembly system for electric motors exemplifies the effective use of Industrie 4.0 concepts and components. An exemplary application of a decentralized control and an integrated robotic system is presented. The combination of Industrie 4.0 elements allows a plug & produce assembly system that can adapt to varying conditions.

Durch die vielfältigen Anwendungsgebiete der faserverstärkten Kunststoffe entstehen individuelle Anforderungen an die Qualität der gefertigten Bauteile. Diese kann durch unterschiedliche Prüfverfahren unter Laborbedingungen bereits zuverlässig charakterisiert werden. Die Herausforderung für die Industrie besteht darin, diese Mess- und Prüftechniken in die Serienfertigung inline zu integrieren, um sowohl die Ausschussraten als auch die Wertschöpfung in der Fertigung optimieren zu können. The diversity of applications using Fibre Reinforced Plastics demands the detection of individual characteristics on manufactured parts by different methods of quality control. These methods offer a high reliability under laboratory conditions, but need to be adapted for the industrial use. Therefore, this article deals with the inline integration of these methods in serial production in order to reduce scrap and to maximise the added value of the process chain itself.

Currently, the laser powder bed fusion (L-PBF) process cannot offer a reproducible and predefined quality of the processed parts. Recent research on process monitoring focuses strongly on integrated optical measurement technology. Besides optical sensors, acoustic sensors also seem promising. Previous studies have shown the potential of analyzing structure-borne and air-borne acoustic emissions in laser welding. Only a few works evaluate the potential that lies in the usage during the L-PBF process. This work shows how the approach to structure-borne acoustic process monitoring can be elaborated by correlating acoustic signals to statistical values indicating part quality. Density measurements according to Archimedes’ principle are used to label the layer-based acoustic data and to measure the quality. The data set is then treated as a classification problem while investigating the applicability of existing artificial neural network algorithms to match acoustic data with density measurements. Furthermore, this work investigates the transferability of the approach to more complex specimens.

X-Ray Computed Tomography (CT) has been successfully introduced into industrial applications in the last few years. Recent industrial needs aim at increasing the accessibility of CT technology to more users than only measurement experts. Suitable concepts for user support have been developed in the past. However, these user support concepts do not cover the entire measurement process and only a small variety of measurement tasks. To overcome these shortcomings and to support the user in an optimal manner, the existing concepts need to be integrated together with knowledge deduced from different measurements into a single software tool. In this paper, different types of knowledge based systems are compared and assessed in their ability to integrate knowledge of the measurement process, the form and material of the workpiece as well as quality parameters of the CT projections. The assessment is used to derive the structure of a suitable knowledge based system that addresses industrial needs regarding flexibility and usability. The results may be used in the future to develop a software tool for user support, which can be used in an industrial environment, enabling untrained users to utilize CT-based systems with a low measurement uncertainty.