Manufacturing process modelling using process specification language

Qiao, Lihong; Kao, Shuting; Yizhu, Zhang

doi:10.1007/s00170-010-3115-3

Cited by 36 publications

(8 citation statements)

References 10 publications

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“…The accuracy of the classifier and prediction was evaluated through the corresponding evaluation indexes. The diagnostic accuracy is shown in Equation (14).…”

Section: Case Studymentioning

confidence: 99%

“…The MP is one of the leading factors affecting the PMA lifecycle [13]. As it becomes increasingly complicated and diversified, fault diagnoses and optimization according to relevant information and data have become indispensable measures to improve the reliability of PMAs and have attracted much research attention [14,15]. The batch production of PMAs comprises a large number of parts; the MP covers multiple stages such as machining, heating, welding, coating, cleaning, assembling, adjustments, encapsulating, and baking.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems

Tan

Chen

et al. 2022

Energies

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A permanent magnet actuator (PMA) is a critical device for transforming, transmitting, and protecting electrical energy in renewable energy systems. The reliability of a PMA exerts a direct effect on the operational safety, stability, and reliability of renewable energy systems. An effective fault diagnosis and adjustments for manufacturing processes (MPs) are vital for improving the reliability of a PMA. However, the state-of-the-art fault diagnosis methods are mainly used for single process parameters, extensive sample data, and automated manufacturing systems under real-time monitoring and are not applicable to a PMA with low levels of automation and high human factor-induced uncertainties. This study proposes a novel fault diagnosis approach based on a surrogate model and machine learning for multiple manufacturing processes of a PMA with insufficient training data due to human factor uncertainties. First, a surrogate model that correlated the MP parameters with the output characteristics (OCs) was constructed by a finite element simulation. Second, the quality performance of the OCs under different fault combinations with the mean or variance of the shift of the MP parameters as typical patterns was calculated by the Monte Carlo method. Finally, using the above computations as the training data, a fault diagnosis model capable of identifying the fault pattern of the manufacturing process parameters according to the OCs was constructed based on machine learning. This approach compensated for the inadequacies of traditional fault diagnosis methods with complex analytical models or numerous processing data. The effectiveness and potential applications of the proposed approach were verified through a case study of a rotary PMA in smart grids.

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“…The accuracy of the classifier and prediction was evaluated through the corresponding evaluation indexes. The diagnostic accuracy is shown in Equation (14).…”

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems

Tan

Chen

et al. 2022

Energies

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“…It provides new technical support for industrial manufacturing [12]- [15]. Qiao et al [16] proposed a 3D process model with geometric evolution and multi-perspective of process information, which can comprehensively describe and express process information. This study promoted the generation and optimization of digital process information.…”

Section: Related Work a 3d Process Planning 1) Mbd Process Modelsmentioning

confidence: 99%

The Modeling and Using Strategy for the Digital Twin in Process Planning

et al. 2020

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Process planning is the core of guiding process production and one of the most critical parts to realize intelligent manufacturing of products. In order to realize the monitoring, simulation, prediction and control of the physical space in the intelligent manufacturing mode, the digital twin technology is used to guide the process planning. This research describes a modeling method of digital twin process model (DTPM) for manufacturing process and the contents of DT data, discusses the acquisition method of real-time data and the management method of simulation data. Aiming at the data fusion between physical space and virtual space, a hierarchical model and mapping strategy for multi-source heterogeneous data in machining process are proposed to generate the DT data. Finally, the guidance and the visualization function in process planning of DTPM is analyzed, and the effectiveness of this method is verified by choosing the design process of a key component in a marine diesel engine.

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“…In PPDRC ontology, workflows are a type of complex occurrence with a common objective shared by one or more agents, which coordinate in order to achieve this objective (Tamma et al 2005), and which are coherent with the use of PSL for activity flow modeling as proposed by Bock and Gruninger (2005). Contrary to the approach found in PSL, in which objects are defined by exclusion with respect to the other three first level entities of the ontology (activity, activity occurrence and timepoint) (Qiao, Kao, and Zhang 2011), in the PPDRC ontology, an object is a tangible or intangible entity that does exist (Solano, Romero and Rosado 2010). Among these objects are social objects, or shared descriptions which allow mutual understanding between the members of a community (Ferrario and Oltramari 2004), as is the case with the activity dealt with previously.…”

Section: Fundamental Concepts Of the Domainmentioning

confidence: 99%

Knowledge representation for product and processes development planning in collaborative environments

Solano

Rosado

Romero

2013

International Journal of Computer Integrated Manufacturing

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Efficiency in the management of integrated product and processes development is a basic requisite to guarantee competitiveness and success for manufacturing companies. This means that operational management of activities, and human and material resources is extremely important, especially in virtual OKP (One-of-aKind Production) systems, and must cover related aspects of their capabilities and social character as well as assignment criteria. In this context, and to facilitate collaborative resources management, an ontology focused on resources and capabilities is proposed in this work. This ontology supports the necessary knowledge for generic and collaborative process planning, providing a shared common semantic for all the members of the virtual company. This work differs from other proposed ontologies in the area of process planning in that the resources considered are all those elements that participate in the execution of the different activity types involved in this wide and complex process. The ontology directly covers the shared, social nature of the resources, the agentive behavior of many of them and a characterization of their capabilities, thus providing specific solutions to the needs of the Collaborative Integrated Development of Products, Processes and Resources (CIDP 2 R) process.

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Manufacturing process modelling using process specification language

Cited by 36 publications

References 10 publications

A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems

A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems

The Modeling and Using Strategy for the Digital Twin in Process Planning

Knowledge representation for product and processes development planning in collaborative environments

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