Statistics Decomposition and Monitoring in Original Variable Space

The traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.

Childcare and Children’s Development: Features of Effective Programs

Governments around the world are increasingly investing resources for young children, and universal provision of early childhood education and care (ECEC) has become widespread. Children’s development is affected by the investments they receive both within and outside the household. A simple theoretical framework predicts that the provision of public childcare will improve children’s development if it offers more stimulation than the care it replaces. Generally, carefully designed studies show that the provision of early childcare is beneficial, especially for children from disadvantaged backgrounds. This is in line with expectations that the alternative care experienced by children from less affluent, less educated, and immigrant backgrounds is likely to be of lower quality. Interestingly, however, studies show that the children who would benefit the most are least likely to receive care, providing a challenge for policy makers. Some programs, such as the $5-per-day childcare in Quebec, have negative effects and therefore may be of poor quality. However, comparing results across programs that vary in several dimensions makes it difficult to separate out the ingredients that are most important for success. Studies that focus on identifying the factors in ECEC that lead to the greatest benefit indicate that some standard measures such as staff qualifications are weakly linked to children’s outcomes, whereas larger staff–child ratios and researcher-measured process quality are beneficial. Spending more time in high-quality childcare from around age 3 has proved to be beneficial, whereas the effect of an increase in childcare for younger children is particularly sensitive to each program’s features and context.

An Investigation on Internal Material Loads and Modifications in Precision Turning of Steel 42CrMo4

The functional properties of a workpiece are determined by a modification of the surface and subsurface materials. In this work, the correlation between thermo-mechanical material loads and the modification of the residual stresses is presented. While the resulting residual stresses were measured by X-ray diffraction after machining experiments, the material loads were determined using a process simulation. The experimental data (measured process forces and results from previous experiments) are used to validate the simulation, which is then applied to calculate the internal thermo-mechanical loads of the maximal temperature and the equivalent von-Mises-stresses per volume element during the machining experiments. In conclusion, a higher depth impact of mechanical loads compared to a lower depth impact of thermal loads in precision machining is observed. For the sake of novelty, the thermo-mechanical loads were plotted and interpreted in a three-dimensional fashion. Finally, cross sections of this mutual representation at certain constant material loads—thermal and mechanical—result in a process signature, which can prospectively improve the prediction of functional workpiece properties.

Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

Increasing the accuracy of signal extraction by correcting the approximating function under conditions of a priori uncertainty

The paper discusses the issues of practical implementation of increasing the accuracy of signal extraction, which is achieved by eliminating the «flip» of the approximating function when dividing the measured process into intervals under conditions of a priori uncertainty about the signal function, which significantly increases the error of allocating a useful signal. The probability of a «flip» of the approximating function depends significantly on the variance of the additive noise and the sample length. The use of the proposed methods and their software implementation makes it possible to increase the accuracy of the useful signal extraction up to 30 percent in the absence of a priori information about the function of the measured process for complex signals and at least 20% for simpler ones. The use of the proposed methods will significantly increase the processing efficiency in the conditions of a priori uncertainty about the function of the measured process (useful signal) and the statistical characteristics of the additive noise components.

Measuring and modelling of process forces during tapping using single tooth analogy process

For machining internal threads, tapping is a commonly used process. However, due to the complex geometry of the tapping tool, each tooth has a unique geometry resulting in individual forces. Since the forces act synchronously during the process, they partly compensate each other. However, since resulting forces in tapping can cause undesired deflection of the tool which can lead to threads that are not true to gauge or tool breakage, the knowledge of the forces is crucial. To predict the occurring forces on each tooth, different modelling approaches can be used. An approach based on the chip load-cutting force relationship is the mechanistic modelling. Therefore, a suitable force model is of central importance. An empirical force model can be established using an analogy process. Within this work a single tooth analogy process is presented to measure the forces of each tooth separately. By means of a geometrical analysis of the real tool, the chip sizes, such as the cross-section area of the undeformed chip are calculated. Merging the measured process forces from the analogy process and the actual chip sizes, an empirical force model is set up using multivariate regression. The model is validated by implementing it in an existing framework and comparing the results to experimental data.

Slow Practice as Ethical Aesthetics: The Ecocritical Strategy of Patience

How can cultural works from the distant past –such as the Middle Ages—teach us ethical modes of behavior for today? One form of ecopoetics emerges through slow practice, making the reader collaborate in the measured process of co-creating the emotional impact of an imaginative text. Drawing on rich debates about slow cinema, this essay suggests how Chaucer’s The Clerk’s Tale—from his grand fourteenth-century poem, The Canterbury Tales—evokes a slow eco-aesthetics with ethical impact. The relative slowness of walking shapes how individuals respond to their environment. In turn, a deceleration of perception affects how travel comes to be written about, as seen in the tale of Patient Griselda. Introduced by Giovanni Boccaccio and adapted by such writers as Francesco Petrarch, Geoffrey Chaucer, and Christine de Pizan, she acts dynamically through her apparent silence and notorious patience. The environmental humanities offer paradigms for us to consider the strategies of slowness and patience. This essay shows how medieval pilgrimage literature evokes a slow aesthetic which is at the same time an ecocritical strategy. Slowness results in an enduring impact and heightened sensitivity to the ecological damage for which we all are culpable. Slower somatically inculcates key aspects of environmental awareness. Pilgrimage texts from the Middle Ages teach us slow ethical aesthetics, suggesting that the medieval moment—finally and a long time coming— is now.

What is respiration - response to glucose addition, presence of plant roots and differences across biomes

<p>Respiration is likely the most often measured process in soil ecology. It is used as a general measurement of soil activity, and physiologically related to microbial maintenance requirements, growth, and soil organic matter production via biochemical efficiency and CUE.</p><p>Genomic tools are increasingly used in soil ecology for measurement of community composition, and functional analysis of communities, and when combined with stable isotopes, can be used to infer activities, either of the whole community or of individual taxa. However, relating genomic or gene-expressed functions to whole ecosystem processes, such as respiration, remains a conceptual and practical problem.</p><p>We analyzed the biochemical processes related to respiration and determine how, during a short soil incubation experiment in the presence of glucose, these processes change. Furthermore, we will show how gene and transcript abundances of respiratory processes vary across more than 4000 soil and rhizosphere samples in forests and grasslands and other biomes.  </p><p>Results illustrate the treasure trove of biochemical information available to us in the form of metagenomes and metatranscriptomes.</p>

Extensometer for Determining Strains on a Tensile and Torsion Simultaneous Load

The paper presents an extensometer designed to measure two mechanical strains at the same time—one from tensile load and the other from torsion load. Strain transducers provide different electric signals, which, after calibration, lead to the simultaneous measurement of linear (ε) and angular (γ) strains. Each of these two signals depends on the measured process and is not influenced by the other strain process. This extensometer is designed to be easily mounted on the sample with only two mounting points and can be used to measure the combined cyclical fatigue of tensile and torsional loadings. This extensometer has two bars—one rigid, reported at the resulting stress points, and one elastic and deformable. The elastic deformable bar has two beams with different orientations. When the sample is deformed, both beams are loaded by two bending moments (perpendicular to each other and both perpendicular on the longitudinal axis of the bars).