Electron-vibration interaction in transport through atomic gold wires

We calculate the effect of electron-vibration coupling on conduction through atomic gold wires, which was measured in the experiments of Agra\"it et al. [Phys. Rev. Lett. 88, 216803 (2002)]. The vibrational modes, the coupling constants, and the inelastic transport are all calculated using a tight-binding parametrization and the non-equilibrium Green function formalism. The electron-vibration coupling gives rise to small drops in the conductance at voltages corresponding to energies of some of the vibrational modes. We study systematically how the position and height of these steps vary as a linear wire is stretched and more atoms are added to it, and find a good agreement with the experiments. We also consider two different types of geometries, which are found to yield qualitatively similar results. In contrast to previous calculations, we find that typically there are several close-lying drops due to different longitudinal modes. In the experiments, only a single drop is usually visible, but its width is too large to be accounted for by temperature. Therefore, to explain the experimental results, we find it necessary to introduce a finite broadening to the vibrational modes, which makes the separate drops merge into a single, wide one. In addition, we predict how the signatures of vibrational modes in the conductance curves differ between linear and zigzag-type wires.Comment: 19 pages, 12 figure

Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions

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

Machine learning based activity recognition to identify wasteful activities in production

Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results

Conformal scattering for a nonlinear wave equation on a curved background

The purpose of this paper is to establish a geometric scattering result for a conformally invariant nonlinear wave equation on an asymptotically simple spacetime. The scattering operator is obtained via trace operators at null infinities. The proof is achieved in three steps. A priori linear estimates are obtained via an adaptation of the Morawetz vector field in the Schwarzschild spacetime and a method used by H\"ormander for the Goursat problem. A well-posedness result for the characteristic Cauchy problem on a light cone at infinity is then obtained. This requires a control of the nonlinearity uniform in time which comes from an estimates of the Sobolev constant and a decay assumption on the nonlinearity of the equation. Finally, the trace operators on conformal infinities are built and used to define the conformal scattering operator

A data-driven approach for quality analytics of screwing processes in a global learning factory

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

Experimental identification of a surface integrity model for turning of AISI4140

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

Showcasing synergies between agriculture, biodiversity and ecosystem services to help farmers capitalising on native biodiversity (SHOWCASE)

The slow adoption by the agricultural sector of practices to promote biodiversity are thought to originate from three interrelated issues. First, we know little about which incentives effectively motivate farmers to integrate biodiversity into daily farm management. Second, few studies so far have produced evidence that biodiversity-based approaches produce benefits in terms of key variables for farmers (yield, profit). Third, there is a large communication gap between the scientists investigating biodiversity-based farming practices and the farmers who have to implement them. To overcome these barriers, SHOWCASE will review and test the effectiveness of a range of economic and societal incentives to implement biodiversity management in farming operations and examine farmer and public acceptance. Focus will be on three promising approaches: (i) result-based incentives, (ii) involvement in citizen science biodiversity monitoring and (iii) biodiversity-based business models. SHOWCASE will co-produce together with stakeholders solid interdisciplinary evidence for the agro-ecological and socio-economic benefits of biodiversity management in 10 contrasting farming systems across Europe. SHOWCASE will also design communication strategies that are tailor-made to farmers and other key stakeholders operating in different socio-economic and environmental conditions.SHOWCASE will develop a multi-actor network of 10 Experimental Biodiversity Areas in contrasting European farming systems that will be used for in-situ research on biodiversity incentives and evidence for benefits as well as knowledge exchange. This network will be used to identify and test biodiversity indicators and targets relevant to all stakeholders and use them in a learning-by-doing approach to improve benefits of biodiversity management on farms, both within the network and beyond