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"Process control."
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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control
Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs.
Journal Article
Additive manufacturing of ultrafine-grained high-strength titanium alloys
Additive manufacturing, often known as three-dimensional (3D) printing, is a process in which a part is built layer-by-layer and is a promising approach for creating components close to their final (net) shape. This process is challenging the dominance of conventional manufacturing processes for products with high complexity and low material waste
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. Titanium alloys made by additive manufacturing have been used in applications in various industries. However, the intrinsic high cooling rates and high thermal gradient of the fusion-based metal additive manufacturing process often leads to a very fine microstructure and a tendency towards almost exclusively columnar grains, particularly in titanium-based alloys
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. (Columnar grains in additively manufactured titanium components can result in anisotropic mechanical properties and are therefore undesirable
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.) Attempts to optimize the processing parameters of additive manufacturing have shown that it is difficult to alter the conditions to promote equiaxed growth of titanium grains
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. In contrast with other common engineering alloys such as aluminium, there is no commercial grain refiner for titanium that is able to effectively refine the microstructure. To address this challenge, here we report on the development of titanium–copper alloys that have a high constitutional supercooling capacity as a result of partitioning of the alloying element during solidification, which can override the negative effect of a high thermal gradient in the laser-melted region during additive manufacturing. Without any special process control or additional treatment, our as-printed titanium–copper alloy specimens have a fully equiaxed fine-grained microstructure. They also display promising mechanical properties, such as high yield strength and uniform elongation, compared to conventional alloys under similar processing conditions, owing to the formation of an ultrafine eutectoid microstructure that appears as a result of exploiting the high cooling rates and multiple thermal cycles of the manufacturing process. We anticipate that this approach will be applicable to other eutectoid-forming alloy systems, and that it will have applications in the aerospace and biomedical industries.
Titanium–copper alloys with fully equiaxed grains and a fine microstructure are realized via an additive manufacturing process that exploits high cooling rates and multiple thermal cycles.
Journal Article
Control chart pattern recognition using the convolutional neural network
Unnatural control chart patterns (CCPs) usually correspond to the specific factors in a manufacturing process, so the control charts have become important means of the statistical process control. Therefore, an accurate and automatic control chart pattern recognition (CCPR) is of great significance for manufacturing enterprises. In order to improve the CCPR accuracy, experts have designed various complex features, which undoubtedly increases the workload and difficulty of the quality control. To solve these problems, a CCPR method based on a one-dimensional convolutional neural network (1D-CNN) is proposed. The proposed method does not require to extract complex features manually; instead, it uses a 1D-CNN to obtain the optimal feature set from the raw data of the CCPs through the feature learning and completes the CCPR. The dataset for training and validation, containing six typical CCPs, is generated by the Monte-Carlo simulation. Then, the influence of the network structural parameters and activation functions on the recognition performance is analyzed and discussed, and some suggestions for parameter selection are given. Finally, the performance of the proposed method is compared with that of the traditional multi-layer perceptron method using the same dataset. The comparison results show that the proposed 1D-CNN method has obvious advantages in the CCPR tasks. Compared with the related literature, the features extracted by the 1D-CNN are of higher quality. Furthermore, the 1D-CNN trained with simulation dataset still perform well in recognizing the real dataset from the production environment.
Journal Article
Strategies to Improve COVID-19 Vaccination in a Pregnant, Marginalized Population: Correspondence/Respond
The quality improvement (QI) initiative the authors describe used lean six sigma techniques to increase pregnant women's COVID-19 immunization rates at a western New York State federally certified health center. Furthermore, the intricacy of factors affecting vaccination rates may not be adequately captured by data analysis techniques, such as statistical process control charts and weekly run charts. Sustaining the gains made by this QI initiative will require ongoing assessment and monitoring of vaccination rates in addition to input from patients and community health workers.
Journal Article
Wafer-scale single-crystal hexagonal boron nitride monolayers on Cu (111)
Ultrathin two-dimensional (2D) semiconducting layered materials offer great potential for extending Moore’s law of the number of transistors in an integrated circuit
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. One key challenge with 2D semiconductors is to avoid the formation of charge scattering and trap sites from adjacent dielectrics. An insulating van der Waals layer of hexagonal boron nitride (hBN) provides an excellent interface dielectric, efficiently reducing charge scattering
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,
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. Recent studies have shown the growth of single-crystal hBN films on molten gold surfaces
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or bulk copper foils
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. However, the use of molten gold is not favoured by industry, owing to its high cost, cross-contamination and potential issues of process control and scalability. Copper foils might be suitable for roll-to-roll processes, but are unlikely to be compatible with advanced microelectronic fabrication on wafers. Thus, a reliable way of growing single-crystal hBN films directly on wafers would contribute to the broad adoption of 2D layered materials in industry. Previous attempts to grow hBN monolayers on Cu (111) metals have failed to achieve mono-orientation, resulting in unwanted grain boundaries when the layers merge into films
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,
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. Growing single-crystal hBN on such high-symmetry surface planes as Cu (111)
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,
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is widely believed to be impossible, even in theory. Nonetheless, here we report the successful epitaxial growth of single-crystal hBN monolayers on a Cu (111) thin film across a two-inch
c
-plane sapphire wafer. This surprising result is corroborated by our first-principles calculations, suggesting that the epitaxial growth is enhanced by lateral docking of hBN to Cu (111) steps, ensuring the mono-orientation of hBN monolayers. The obtained single-crystal hBN, incorporated as an interface layer between molybdenum disulfide and hafnium dioxide in a bottom-gate configuration, enhanced the electrical performance of transistors. This reliable approach to producing wafer-scale single-crystal hBN paves the way to future 2D electronics.
The epitaxial growth of single-crystal hexagonal boron nitride monolayers on a copper (111) thin film across a sapphire wafer suggests a route to the broad adoption of two-dimensional layered semiconductor materials in industry.
Journal Article