Semantic approach to the automatic recognition of machining features

Zhang, Yingzhong; Luo, Xiangfeng; Zhang, Baiyun; Zhang, Shaohua

doi:10.1007/s00170-016-9056-8

Cited by 35 publications

(11 citation statements)

References 26 publications

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“…There are a variety of rule-based approaches, e.g. graphbased method (Joshi and Chang 1988;Huang and Yip-Hoi 2002;Lockett and Guenov 2005;Kao 1993;Li et al 2010;Xu et al 2015;Campana and Mele 2018), hint-based method (Vandenbrande and Requicha 1993;Han and Requicha 1998;Han et al 2001b;Han and Requicha 1997), cell-based approach (Woo 2003), ontology-based method (Wang and Yu 2014;Zhang et al 2017), STEP-based approach (Mokhtar et al 2009;Venu and Komma 2017;Venu et al 2018;Al-wswasi and Ivanov 2019;Han et al 2001a;Ong et al 2003;Kannan and Shunmugam 2009a, b;Dipper et al 2011;Mokhtar and Xu 2011), planning approach (Marchetta and Forradellas 2010), tree-based method (Li et al 2002;Sung et al 2001), hybrid approach (Gao and Shah 1998;Rahmani and Arezoo 2007;Rameshbabu and Shunmugam 2009;Hayasi and Asiabanpour 2009), and others (Zhang et al 2014;Harik et al 2017). A typical rule-based feature recognition approach is the graph-based method (Joshi and Chang 1988), in which a feature recognition problem is formulated as a graph matching problem.…”

Section: Related Workmentioning

confidence: 99%

A novel learning-based feature recognition method using multiple sectional view representation

Shi

Qin

et al. 2020

J Intell Manuf

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In computer-aided design (CAD) and process planning (CAPP), feature recognition is an essential task which identifies the feature type of a 3D model for computer-aided manufacturing (CAM). In general, traditional rule-based feature recognition methods are computationally expensive, and dependent on surface or feature types. In addition, it is quite challenging to design proper rules to recognise intersecting features. Recently, a learning-based method, named FeatureNet, has been proposed for both single and multi-feature recognition. This is a general purpose algorithm which is capable of dealing with any type of features and surfaces. However, thousands of annotated training samples for each feature are required for training to achieve a high single feature recognition accuracy, which makes this technique difficult to use in practice. In addition, experimental results suggest that multi-feature recognition part in this approach works very well on intersecting features with small overlapping areas, but may fail when recognising highly intersecting features. To address the above issues, a deep learning framework based on multiple sectional view (MSV) representation named MsvNet is proposed for feature recognition. In the MsvNet, MSVs of a 3D model are collected as the input of the deep network, and the information achieved from different views are combined via the neural network for recognition. In addition to MSV representation, some advanced learning strategies (e.g. transfer learning, data augmentation) are also employed to minimise the number of training samples and training time. For multi-feature recognition, a novel view-based feature segmentation and recognition algorithm is presented. Experimental results demonstrate that the proposed approach can achieve the state-of-the-art single feature performance on the FeatureNet dataset with only a very small number of training samples (e.g. 8-32 samples for each feature), and outperforms the state-of-the-art learning-based multi-feature recognition method in terms of recognition performances.

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Section: Related Workmentioning

confidence: 99%

A novel learning-based feature recognition method using multiple sectional view representation

Shi

Qin

et al. 2020

J Intell Manuf

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“…MPS deals with the automatic allocation of manufacturing resources to achieve optimized matching between part features, material characteristics, and process capabilities based on input information including part geometry (geometric features and part dimensions) and constraints (quality, mechanical, and economical) [17]. For subtractive manufacturing, the process can be selected via a geometric analysis of features and then by matching these features with the appropriate machining processes [18]. For additive manufacturing, process selection can be done based on material choice, part size, and build quality [6].…”

Section: Related Workmentioning

confidence: 99%

“…Some ontology-based approaches have been developed for specific processes, such as additive manufacturing [35], and therefore suffer from genericity considerations. Some studies have developed semantic approaches to the automatic recognition of machining features [18].…”

Section: Semantic Web Technologies For Mps According Tomentioning

confidence: 99%

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Mabkhot

Al-Samhan

Hidri

2019

Advances in Materials Science and Engineering

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In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

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“…Wang et al [32] designed an ontology that includes the classes and properties corresponding to STEP schema, and then their system read STEP file of a CAD model and perform reasoner on the model to implement Geometric feature recognition. Zhang et al [37] demonstrated the ontology-based concept model for representing the machining faces and features, annotated geometric faces using domain ontology, and recognized the faces based on the annotation. While the methods tried introducing ontology in Geometric feature recognition, the methods focused on introducing the ontology-based versions of the other methods by exclusively considering geometric context, rather than considering non-geometric context with an ontology.…”

Section: Geometric Feature Recognitionmentioning

confidence: 99%

Development of an Agile Concept for MBSE for Future Digital Products through the Entire Life Cycle Management Called Munich Agile MBSE Concept (MAGIC)

Salehi¹

2019

CAD&A

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The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition. However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e. semantic) keywords, has been elusive. In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework. Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model. This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks. The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models. This paper concludes with the future challenges of the approach.

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Semantic approach to the automatic recognition of machining features

Cited by 35 publications

References 26 publications

A novel learning-based feature recognition method using multiple sectional view representation

A novel learning-based feature recognition method using multiple sectional view representation

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Development of an Agile Concept for MBSE for Future Digital Products through the Entire Life Cycle Management Called Munich Agile MBSE Concept (MAGIC)

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