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A shift towards new powertrains and alternative energy sources will increase the complexity of future aircraft configurations and demands for comprehensive Life Cycle Assessment. Due to missing data management structures, gathering of reliable life cycle data is time- and cost-intensive and hampers close examination of heterogenic scenarios. Blockchain and its characteristics, i.e. the distributed architecture and immutability of records, are promising for linking multiple stakeholders and tracking data. Therefore, this paper introduces a blockchain-based data management concept for facilitating comprehensive and detailed Life Cycle Assessment and discusses the conceptual approach with a focus on aircraft maintenance, repair and overhaul (MRO).

Due to the increased complexity of products and manufacturing systems as well as increasing competition, the importance of product quality has grown for manufacturing companies. Accordingly, the demand for the analysis of intermediate product features and intermediate product propagation to support process-integrated quality management is growing. In this paper, a framework for the data-driven analysis of product property propagation to support process integrated quality management is presented. Here, particularly distinctive product properties that are evolving throughout production processes are introduced as product phenotypes. The framework uses a combination of machine learning algorithms to derive product specific inspection strategies. The developed framework is applied to a case study from electronic production.

High product quality and low rejection rates are decisive for the competitiveness of manufacturing systems. Hence, manufacturing shifts towards digitalization for predicting and controlling product quality. In order to implement data-driven approaches in manufacturing successfully, sufficient model performances are necessary. Especially metal casting comes with several hurdles for data-driven approaches, like sparse and imbalanced data, a relatively high human interaction and frequent mounting events. This often leads to hardly generalizable and weak models. Within this paper, transfer learning is investigated for the quality prediction of aluminum gravity die casting to try to overcome these hurdles and create robust, accurate and data-efficient models.

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed.

Life cycle engineering (LCE) builds up on the comprehension of domain-specific engineering activities and their interlinkage along the product life cycle. Its goal is to guide engineering processes through knowledge regarding hotspots and trade-offs in terms of environmental, social and economic impacts. However, LCE implementations show shortcomings due to a discontinuous integration in key business processes as well as separated tool environments between core engineering disciplines and LCE methods and tools. Addressing the shortcomings, an interdisciplinary LCE framework is elaborated on the basis of a visual analytics (VA) process. This domain connects human interaction, data analysis and visualization. The framework is applied to the LCE case of shop-floor life cycle assessment. The realization of seamless toolchains from domain-specific tools is in the focus. Further challenges include the prioritization and bridging of relevant interfaces as well as their qualification for automation and near real-time processing.

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts.

Ensuring constant high process and product quality is one of the decisive competitive factors in today’s industrial production. Industry 4.0 by means of the analysis of production data is seen as a viable approach to achieve zero defects production. Due to their sensitive behavior, energy data, which is often acquired independently of the machine control, can provide an adequate data source for quality-related analysis tasks. An analysis based on energy data reduces the effort for accessing machine controls and enables scalability. This paper shows the concept and implementation of a data-driven process and quality monitoring tool based on energy data for an electro-pneumatic handling system.

Digital transformation is regarded as a key enabler for quality management in the modern casting industry. By uncovering parameters that affect the casting part quality, a continuous process improvement could be enabled. In particular, a focus is laid on the explicability of the identified dependencies due to the acceptance of data-driven decisions by workers as well as to initiate tailored improvement measures. For this purpose, in this paper two visual analytics approaches are discussed for uncovering quality-affecting parameters with the claim for a high degree of explicability – on the one hand a method-feature-heatmap that visualizes a multiple backward feature elimination and on the other hand a graph-based visualization of class association rules. An automotive industry use case demonstrates the approaches in the context of gravity die casting of aluminium knuckles.

Manufacturing companies are exposed to increased complexity and competition. To stay competitive, companies need to minimize the total cost of quality while ensuring high transparency about process&ndash, product relationships within the manufacturing system. In this context, the development of technologies such as advanced analytics and cyber physical production systems offer a promising approach. This paper discusses and defines essential elements of virtual quality gates in the context of manufacturing systems. To support the planning and implementation of virtual quality gates, a morphological box is developed which can be used to identify and derive an individual approach for a virtual quality gate based on the specific characteristics and requirements of the respective manufacturing system. Moreover, the framework is exemplified by three case studies from various industries and resulting potential are discussed.

Advanced manufacturing of automotive lightweight structures implies the introduction of new process steps into traditional process chains. Due to the combination of materials and their functional integration, those process steps show increased complexity. As a result, manufacturing faces new challenges regarding a constant and high product quality. A widely discussed approach to encounter these new challenges is the analysis of manufacturing process data by applying data mining methods. Benefits of the underlying digitalization approach are found in extensive transparency, product quality assurance, decision support or even in an automated manufacturing control.