Due to the recent advances in digitisation of the manufacturing industry and the generation of manufacturing data, there is increasing interest to integrate machinelearning on the shop floor to improve efficiency and...
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ISBN:
(纸本)9783031176289;9783031176296
Due to the recent advances in digitisation of the manufacturing industry and the generation of manufacturing data, there is increasing interest to integrate machinelearning on the shop floor to improve efficiency and quality control. Ultrasonic welding is an emerging joining process used in various manufacturing industries, and is an energy efficient, cost-effective method of joining similar or dissimilar materials. However, the quality of the joint achievable is heavily dependent on process input parameters. In this study, a Gaussian Process Regression (GPR) model is developed to map the relationship between process parameters and joint performance for ultrasonically welded aluminium joints, with a view to improving quality control in a manufacturing setting. Initially, a 33 full factorial design of experiments is conducted to investigate the influential parameters, then a GPR model is trained on the experimental data. In-process sensor data is also used to infer process performance. To assess the prediction performance of the model, ten unseen parameter combinations are predicted and compared to their respective experimental result. The model demonstrates a high level of accuracy producing a Pearson's correlation coefficient of 0.982 between the predicted and actual results for all data. The mean relative predictive error for unseen data is 7.35%.
The rapid development of 'Internet' has brought people into the era of 'interconnection of all things', changed people's living habits, and also brought changes to the production mode of enterprise...
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A still in-progress technology of the Industrial Internet of things (IIoT) involves the use of machine-to-machine (M2M) for communication. It can serve the whole purpose of automation in industries by integrating it w...
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Background:As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits tha...
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Out-of-distribution (OOD) detection is important for machinelearning models deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for improved OOD detection. However, these approache...
Out-of-distribution (OOD) detection is important for machinelearning models deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for improved OOD detection. However, these approaches make a strong distributional assumption that the auxiliary outlier data is completely separable from the in-distribution (ID) data. In this paper, we propose a novel framework that leverages wild mixture data-that naturally consists of both ID and OOD samples. Such wild data is abundant and arises freely upon deploying a machinelearning classifier in their natural habitats. Our key idea is to formulate a constrained optimization problem and to show how to tractably solve it. Our learning objective maximizes the OOD detection rate, subject to constraints on the classification error of ID data and on the OOD error rate of ID examples. We extensively evaluate our approach on common OOD detection tasks and demonstrate superior performance. Code is available at https: //***/jkatzsam/woods_ood.
Diabetes is brought about by undesirable ways of life, terrible eating routine, and work pressure, and it can prompt an assortment of lethal medical issues, including coronary episodes, fits, kidney disappointment, lo...
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The accuracy and aesthetics of information in the printing barcode area are of great significance in practical life. Flat printing defect detection is quite common, and there is currently almost no involvement in non ...
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Spectrum sensing has important research implications for alleviating the conflict between static spectrum allocation strategies and dynamic spectrum demand. This paper provides a brief summary and comparison of some t...
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In the magnetic composite fluid (MCF) polishing process, appropriate polishing parameters are the basis of achieving high-quality polishing without damage. Appropriate polishing parameters are mainly based on an accur...
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In the magnetic composite fluid (MCF) polishing process, appropriate polishing parameters are the basis of achieving high-quality polishing without damage. Appropriate polishing parameters are mainly based on an accurate polishing model and an excellent polishing parameters optimization algorithm. However, due to the complicated principle of MCF polishing and various influencing elements, traditional modeling methods have the limitations of low accuracy, poor application, and difficulty in correcting. Therefore, it is challenging to obtain the optimal polishing quality by optimizing the polishing parameters based on the traditional model. This study proposed an online modeling approach considering data cleaning based on machinelearning modeling, and the particle swarm optimization (PSO) algorithm was used to optimize polishing parameters. Then, copper polishing experiments were carried out to validate the modeling and optimization methods. The results demonstrate that the proposed machinelearning online modeling method can establish an accurate MCF polishing model, and the nano-scale fine polishing of copper can be achieved by the optimized polishing parameters of PSO, and the surface roughness of the copper sample was reduced by 85% to 0.031 mu m.
Torsional oscillations can cause severe damage to downhole tools and may result in expensive fishing and sidetracking operations. The drilling industry is aware of this problem and still looking for suitable solutions...
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