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To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.

The authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.

The authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.

This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.

The purpose of this paper is to investigate the application of key performance indicators (KPIs) on shop floor level in German small- and medium-sized enterprises (SMEs). The paper focuses on the examination of perception differences between shop floor employees and managers with regard to collection, calculation and consolidation of KPIs as well as visualization and motivational aspects.

To examine the hypothesis on differing perceptions regarding KPIs, 27 qualitative interviews with shop floor employees and production managers within 6 SMEs from the German machinery and equipment industry were conducted on basis of a semi-structured guideline.

The findings show that shop floor employees self-assess a lack of relevant knowledge when it comes to understanding KPIs. Moreover, the results show that shop floor employees perceive the visualization of shop floor KPIs as insufficient and non-motivational. This goes along with the finding that managers are aware of the lacking benefit of KPIs resulting from the rather negative perception of shop floor employees. The interviewed managers recognize a strong potential for improvement of their KPI systems.

The interview results confirm the need to design a performance management system on the shop floor that considers and aligns both management and operations, is directed to the shop floor level, considers explicitly the perspective of employees and integrates motivational elements.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

This paper aims to provide an approach of modeling haptic impressions of surfaces over a wide range of applications by using multiple sensor sources.

A multisensory measurement experiment was conducted using various leather and artificial leather surfaces. After processing of measurement data and feature extraction, different learning algorithms were applied to the measurement data and a corresponding set of data from a sensory study. The study contained evaluations of the same surfaces regarding descriptors of haptic quality (e.g. roughness) by human subjects and was conducted in a former research project.

The research revealed that it is possible to model and project haptic impressions by using multiple sensor sources in combination with data fusion. The presented method possesses the potential for an industrial application.

This paper provides a new approach to predict haptic impressions of surfaces by using multiple sensor sources.

The aim of this chapter is to argue that charisma is a collective representation, and that charismatic authority is a social status that derives more from the “recognition” of the followers than from the “magnetism” of the leaders. I contend further that a close reading of Max Weber shows that he, too, saw charisma in this light.

I develop my argument by a close reading of many of the most relevant texts on the subject. This includes not only the renowned texts on this subject by Max Weber, but also many books and articles that interpret or criticize Weber’s views.

I pay exceptionally close attention to key arguments and texts, several of which have been overlooked in the past.

Writers for whom charisma is personal magnetism tend to assume that charismatic rule is natural and that the full realization of democratic norms is unlikely. Authority, in this view, emanates from rulers unbound by popular constraint. I argue that, in fact, authority draws both its mandate and its energy from the public, and that rulers depend on the loyalty of their subjects, which is never assured. So charismatic claimants are dependent on popular choice, not vice versa.

I advocate a “culturalist” interpretation of Weber, which runs counter to the dominant “personalist” account. Conventional interpreters, under the sway of theology or mass psychology, misread Weber as a romantic, for whom charisma is primal and undemocratic rule is destiny. This essay offers a counter-reading.

Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues.

This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values.

As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort.

The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

This chapter examines jury nullification, through which American juries refuse to convict criminal defendants in the face of overwhelming evidence of guilt to express disapproval of specific criminal laws or of their application to particular defendants, through the political theory of Carl Schmitt. It distinguishes liberal components of American jurisprudence, especially the rule of law, from democratic sovereignty, and shows how the two are in deep tension with one another. In light of this tension it argues that jury nullification amounts to democratic sovereignty applied counter to the liberal state in a way that paradoxically upholds individual liberty.