Automated quality assurance as an intelligent cloud service using machine learning

Schreiber, Martin; Klöber-Koch, J.; Bömelburg-Zacharias, J.; Braunreuther, Stefan; Reinhart, Gunther

doi:10.1016/j.procir.2020.01.034

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…In a similar context, Imran et al (2017) employed genetic algorithm techniques for the cellular manufacturing systems. Similarly, massive research work has been exercised in past few decades to improve process and cost reduction in the manufacturing industries (D'Addona & Antonelli, 2019;Knoll et al, 2019;Kreutz et al, 2019;Schreiber et al, 2019;. Because each algorithm has advantages and disadvantages, the model's performance is heavily reliant on the data used to construct the model.…”

Section: In Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

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For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

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Section: In Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

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“…For improving the practical utility of manufactured products, it is necessary to accurately quantify the surface roughness, and surface roughness prediction is gradually becoming a hot spot of theoretical and applied research in CNC machining [ 8 ]. The machining industry demands an increased surface quality while reducing manufacturing costs [ 9 , 10 ], so quantifying surface roughness is critical to parameter selection and has attracted many researchers and engineers to work on it [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning

Zeng

2023

Sensors

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Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.

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Milling surface roughness prediction method based on spatiotemporal ensemble learning

Zeng

2023

Int J Adv Manuf Technol

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Automated quality assurance as an intelligent cloud service using machine learning

Cited by 10 publications

References 18 publications

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning

Milling surface roughness prediction method based on spatiotemporal ensemble learning

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