Development of a smart machining system using self-optimizing control

Park, Hae‐Sim; Tran, Ngoc-Hien

doi:10.1007/s00170-014-6076-0

Cited by 19 publications

(5 citation statements)

References 32 publications

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“…Monitoring the machining process is becoming increasingly important for maintaining consistent quality in machined parts [16]. To acquire the actual status of the manufacturing resources and the workpiece, the sensor-based monitoring systems have caused a lot of attention.…”

Section: ) the Real-time Acquisition Data Technologymentioning

confidence: 99%

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

et al. 2019

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Process evaluation is widely accepted as an effective strategy to improve product quality and shorten its development cycle. However, there has been very little research on how to evaluate the process plan with the dynamic change of the machining condition and uncertain available manufacturing resources. This paper proposes a novel process evaluation method based on digital twin technology. Three core technologies embodied in the proposed method are illustrated in details: 1) real-time mapping mechanism between the collected data in machining and the process design information; 2) construction of the digital twin-based machining process evaluation (DT-MPPE) framework; and 3) process evaluation driven by digital twin data. To elaborate on how to apply the proposed method to the reality, we present a detailed implementation process of the proposed DT-MPPE method for the key parts of the marine diesel engine. Meanwhile, the future work to completely fulfill digital twin-based smart process planning for complex products is discussed. INDEX TERMS Digital twin, process planning, process evaluation, mapping mechanism, digital twin data.

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Section: ) the Real-time Acquisition Data Technologymentioning

confidence: 99%

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

et al. 2019

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“…The evolution of control techniques toward the intelligent machine for future is shown in Fig. 4 (Park, et al, 2014). The first innovation focuses on developing the hardware for machine tools that brings the high speed, high precision, and high productivity.…”

Section: Ict Infrastructurementioning

confidence: 99%

Development of a cloud based smart manufacturing system

Park¹,

Tran

2015

JAMDSM

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Smart manufacturing considered as a new trend of modern manufacturing helps to satisfy objectives associated with the productivity, quality, cost and competiveness. The smart manufacturing system is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of smart manufacturing is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. In this paper, a cloud based smart manufacturing system for machining transmission cases is considered as research object in which the advanced information and communication technology such as cognitive agent, swarm intelligence, and cloud computing are used to integrate, organize and allocate the machining resources.

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“…Companies operating in the industry are in a never-ending race for innovation in a bid to keep ahead of the competition and to generate positive financial results (Wuest et al, 2014). Even with the positive innovations that have been brought about by the fourth industrial revolution (Industry 4.0) (Baur et al, 2020;Han et al, 2019aHan et al, , 2019bHuang et al, 2019;Kohler & Weisz, 2016;Martínez-Arellano et al, 2019;Voisin et al, 2018), in the specific context of machining, companies are still plagued with challenges in the form of unexpected Computer Numerical Control (CNC) machine downtime, cutting tool breakage and nonconforming products, which ultimately, increase variances in the production process and burden the companies' financial health (Chadha et al, 2019;Park & Tran, 2014;Zhou & Xue, 2018). Monitoring and prognostic systems developed to monitor machine health (including that of critical components such as cutting tools or bearings) or to predict the remaining useful life are in effect assets that could help solve the aforementioned problems faced by the industry and ensure better financial performance (Laloix et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 shows a non-exhaustive list of factors influencing the machining process, and thus the workpiece quality. These elements are based on the work of Benardos and Vosniakos (2003), Park and Tran (2014) and Ouafi and Barka (2014).…”

Section: Introductionmentioning

confidence: 99%

“…Instead, they propose an intelligent monitoring system to monitor the quality based on an ML approach. Park and Tran (2014) support the idea of a quality monitoring and quality prediction system, but in their work, they propose a different vision of quality prediction: they define the workpiece quality as a prediction based on the cutting tool wear and surface roughness. This is supported by Bakker et al (2015), who assume that the quality of the workpiece and of the process is derived from the cutting tool condition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the quality of a machined workpiece with a variational autoencoder approach

et al. 2021

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In this article, it is shown that a machine learning approach based only on data from sensors (vibration and current consumption) can be used to predict the geometric dimensioning and tolerancing quality measurement values of machined workpieces in an industrial context. First, a methodology based on a variational autoencoder approach is used, and then a metric based on the concept of Euclidean distance and the 2D latent space produced by the variational autoencoder is proposed. The proposed variational autoencoder regression model is shown capable of predicting the quality measurement values, with a mean square error of 5.2573 × 10 −4 mm. The proposed measurement system also displays a confidence interval of ± 0.05 mm. Moreover, the resulting 2D latent space is capable of distributing and structuring data based on the quality level and of providing a quick visual support. Compared to the t-SNE method, this latent space displays a better structure. Furthermore, the proposed Euclidean distance metric is correlated to the quality level in both the predicted and observed subsets. This work is also based on an industrial dataset, thus increasing its potential for technological transfer; that in turn allows a better monitoring of the machining process, as well as the prediction of the workpiece quality.

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Development of a smart machining system using self-optimizing control

Cited by 19 publications

References 32 publications

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

Development of a cloud based smart manufacturing system

Predicting the quality of a machined workpiece with a variational autoencoder approach

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