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For axial piston units, product optimisation plays an essential role in the development phase, where an efficient and price-attractive product design and manufacturing process is the key to the success of a product. Moreover, the market needs are on lighter, more compact, efficient, and reliable products designed to the limit. Therefore, reliability is the focus of this work, where the research includes method analysis of the material stress calculation applied to the cylinder block implemented in an axial piston pump. A simplified calculation model is presented and evaluated concerning load while remaining accessible for users without deep technical knowledge or access to expensive Finite Element (FE) simulation tools. The analytical calculation method delivers a stress distribution intended for different purposes like design evaluation or as load spectrum for lifetime calculation. Additionally, the developed calculation method is generalised, enabling the methodology to be used on any standard axial piston pump. The methodology utilises the stress determination model, which includes the load as a sum of forces caused by the external and internal influencing factors. To show the method’s success, a comparison demonstrates strong positive agreement between the calculated and simulated stress results obtained by Finite Element Analysis.

Industrial Digitalization and Automation (DA), promoted through new production paradigms such as Industry 4.0 currently dominates the strategy agendas of industrial companies. However, practitioners report various problems while implementing DA into their organizations work-routines. We find underlying problems in missing operational monitoring systems to assess and control the DA-implementation status. We therefore propose a holistic monitoring system using 143 developed DA-implementation key performance indicators in nine dimensions. Moreover, we integrate the monitoring system into a continuous improvement approach following the PDCA-circle. First applications show increased objectivity during initial KPI-status assessments, higher acceptance of defined KPI-target-states and increased alignment during definition of future DA-projects.

Industrial enterprises increasingly identify their employee's digital competences as a main barrier towards modern manufacturing paradigms - such as Industry 4.0. Considering shorter technology cycles, they find a key enabler in timely capturing existing digital competences to forecast future competence requirements relating to identified trends. To support digital competence planning in practise, we developed a digital competence maturity model (DigiCoM), consisting of 49 attributes in four dimensions to measure maturity on individual employee level. The model considers digital competences required for direct value creation activities as well as competences in supporting areas. We tested the model in several use-cases, whereby two use-cases in SMEs are presented in detail. Result evaluation shows that the proposed approach makes digital competences visible and measurable and builds the basis for the derivation of competence developments. The DigiCoM therefore allows interconnecting strategic human resource planning with operational employee competences on an individual level.

Increased utilization of digital elements and computer assistance in industrial enterprises leads to the acquisition of data encompassing all company levels and areas. Subsequently, manufacturing companies face large amounts of collected data from different sources and in various formats. Today this data is beginning to be viewed as a resource, comparable to material or machines. Thus, the ability to effectively collect and utilize internal and external data, is becoming an increasingly important factor in determining their economic success.

The utilization of Digitalization and Automation (DA) is currently one of the determining factors of increasing prosperity, productivity, and efficiency. Although widely promoted and incentivized by policy- and decision makers, industrial enterprises seem reluctant to actively push DA-development in their organizations. From a scientific point of view, we find missing operationalization resulting in a lack of analysis and understanding of basic DA-elements in real production environments as the main barrier towards Industry 4.0- and DA-implementation. Thus, in this paper we introduce a novel approach to transfer abstract Industry 4.0-concepts into industrial environments through the utilization of basic concepts of industrial digitalization and automation. Based on this framework we develop a model to assess the degree of digitalization and automation of value creation factors and organizational factors (DAVO). Knowledge about the company’s DAVO-degree enables their decision makers to derive operational Industry 4.0 target-states, define strategies thus decide targeted investments into organizational and technological developments. We applied the developed and operationalized measurement-metric and model in an empirical study in the Austrian industry assessing the DAVO-degree of 200 industrial companies. Besides insights into their maturity status, we find clear evidence in our study for the need of operationalized Industry 4.0-concepts. Furthermore, the DAVO-approach seems to encourage practitioners to reflect their organization’s DA-status with more accuracy thus enables them to derive more targeted and sustainable strategic steps.

This paper covers a reliability analysis as a qualitative method, especially focused on axial piston units. The method is based on Fault Tree Analysis (FTA) and results in risk and reliability assessment at the components level. Especially, the development of the reliability assessment as a methodical tool is the core of the paper. Moreover, the FTA is combined with the industrial standard method known as Design Failure Mode Effects Analysis (DFMEA) which is typically used in the development phase of the design. The evaluation and the usability of the FTA methodology is analyzed in connection with field data. Thus, the deviation of the theoretical valuation from the field data was utilized as a success indicator of the method. The analysis of the fault spreading covers the assessment of component faults and links failure states with unit effects. The analysis of the axial piston unit as a system is made on idealized/theoretical design and functional behavior only. Hence, the failure rating and the effect is subsequently applied to determine the fault risk in form of the Risk Priority Number (RPN). The failure modes and effects are based on engineering experience of past decades, supported by existing DFMEAs of axial piston units. Thus, the assessment of the risk priority number is based on previous data, yielding the given severity, occurrence and detection quantification. This approach opens new opportunities of design assessment and the results show a good agreement to the damage accumulation seen in real field data. Furthermore, the connection between theoretical design assessment and field data do support the failure ranking improvement of the DFMEA.

The consideration of the drivetrains behavior under real operating conditions is becoming increasingly important. While in the past decades it was good enough to have a rough knowledge about the operation conditions in field, a more accurate and detailed picture of the duty cycles is required today. Caused by specific and complex operation conditions the load deviation between different applications is significant. In many cases like e.g. lifetime calculation, a precise knowledge of field conditions leads to the more accurate calculated or simulated results. This paper covers the duty cycles investigation of a forwarder application in field including the measurement concept and implementation. Especially, the analysis of the load conditions, clustering algorithm and lifetime calculation is included. The load spectrum generation is consequently done based on comprehensive load cycle determination. The Aim of this work is to verify the calculation accuracy of the load spectrum by using the clustered and raw data. The outcome of the investigation gives an indicator for the load degree of the drivetrain. Here the developed method creates a new and fast possibility of data analysis and is applicable to most drivetrain applications. Furthermore, the collected data is used as reference for future investigation of the forwarder evolution.

The need for improvement of lifetime prediction of hydrostatic axial piston units is driven by partly outdated knowledge dating back to the 1970s and is also motivated by a significant evolution of hydrostatic drivetrains. However, the evolution of the load life is unknown and must be investigated. An additional aspect for the method improvement is the potential decrease of qualification effort caused by required evaluation background. The goal is an increase in the prediction accuracy of the lifetime by using of qualitative and quantitative calculation methods. This paper covers the analysis phase of the lifetime calculation improvement. Hence, the currently used calculation method is investigated and the improvement potential builds the research approach. The improvement potential covers calculation model of load-dependent treatment, where the characteristics of sub-components build a system assessment. The aim includes the representative load cycle acquisition for typical applications in the off-road market.

Manufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing - also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions ""Products"", ""Customers"", ""Operations"" and ""Technology"" have been created to assess the basic enablers. Additionally, the dimensions ""Strategy"", ""Leadership"", Governance, ""Culture"" and ""People"" allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments.

The occurrence of similar code, or ‘code clones’, can make program code difficult to read, modify and maintain. This paper describes industrial case studies of clone detection and elimination, and were were performed in collaboration with engineers from Ericsson AB using the refactoring and clone detection tool Wrangler for Erlang.\ud \ud We use the studies to illustrate the complex set of decisions that have to be taken when performing clone elimination in practice; we also discuss how the studies have informed the design of the tool. However, the conclusions we draw are largely language-independent, and set out the pragmatics of clone detection and elimination in real-world projects as well as design principles for clone detection decision-support tools.\ud \ud Context. The context of this work is the fact that a software tool is designed to be used; the success of such a tool therefore depends on its suitability and usability in practice.\ud \ud The work proceeds by observing the use of a tool in particular case studies in detail, through a “partici- pant observer” approach, and drawing qualitative conclusions from these studies, rather than collecting and analysing quantitative data from a larger set of applications. Our conclusions help not only programmers but also the designers of software tools.\ud \ud Inquiry. Data collected in this way make two kinds of contribution. First, they provide the basis for deriving a set of questions that typically need to be answered by engineers in the process of removing clones from an application, and a set of heuristics that can be used to help answer these questions. Secondly, they provide feedback on existing features of software tools, as well as suggesting new features to be added to the tools.\ud \ud Approach. The work was undertaken by the tool designers and engineers from Ericsson AB, working to- gether on clone elimination for code from the company.\ud Knowledge. The work led to a number of conclusions, at different levels of generality.\ud \ud At the top level, there is overwhelming evidence that the process of clone elimination cannot be entirely automated, and needs to include the input of engineers familiar with the domain in question.\ud \ud Furthermore, there is strong evidence that the automated tools are sensitive to a set of parameters, which will differ for different applications and programming styles, and that individual clones can be over- and under-identified: again, involving those with knowledge of the code and the domain is key to successful application.\ud \ud Grounding. The work is grounded in “participant observation” by the tool builders, who made detailed logs of the processes undertaken by the group.\ud \ud Importance. The work gives guidelines that assist an engineer in using clone detection and elimination in practice, as well as helping a tool developer to shape their tool building. Although the work was in the context of a particular tool and programming language, the authors would argue that the high-level knowledge gained applies equally well to other notions of clone, as well as other tools and programming languages.

Swapping N,P for C,N: Iridium complexes of bidentate pyridine-based C,N ligands with an N-heterocylic carbene (NHC) unit proved to be efficient and highly enantioselective hydrogenation catalysts. As a result of the lower acidity of iridium hydride intermediates produced from NHC-based complexes, these catalysts are much better suited than analogous N,P-ligand complexes for the hydrogenation of acid-sensitive substrates.

Unus pro omnibus: The development of compounds inducing neurite outgrowth might constitute a valuable approach for the non-invasive medical treatment of neurodegenerative diseases. With the aid of a bifunctional building block, the total syntheses of a group of pyridone polyenes originally produced by entomopathogenic fungi was achieved (see picture). All of these natural products displayed neuritogenic activity in the PC-12 cell line.

Der Beitrag befasst sich mit der europarechtlichen Konformitat der Berechnung des Ruckkaufswertes von Lebensversicherungen nach §169 Abs. 3 VVG. Im Mittelpunkt steht dabei die Beeintrachtigung von Versicherern anderer Mitgliedstaaten der Europaischen Union bei der Gestaltung und dem Vertrieb ihrer Produkte in Deutschland im Wege der Dienstleistungs- und der Niederlassungsfreiheit. Dabei ist auf die Probleme garantierter Ruckkaufswerte und zwingender Verteilung der Abschlusskosten einzugehen. Schlussendlich erfolgt eine Auseinandersetzung mit dem europarechtlich gepragten Grundsatz des Vorrangs von Informationen vor der Produktregulierung vor dem Hintergrund des europaischen Versicherungsbinnenmarktes.