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Feature-Based Design in Integrated Manufacturing
Published in Cornelius Leondes, The Design of Manufacturing Systems, 2019
Automatic feature recognition systems recognize the features after a part is modeled using a CAD system. Typically, these automatic feature recognition systems use geometric and/or topological information to infer the presence of a particular type of feature. The approach of extracting manufacturing features seems very logical given the fact that these features can be mapped onto a limited number of manufacturing processes. For example, the possible manufacturing processes that can be employed for making a feature “hole” are drilling, boring, or reaming. While a number of robust methodologies have been devised to recognize primitive features (noninteracting), devising algorithms/methodologies to recognize interacting features is still an open-ended research problem that needs deeper investigation. To date, there exists no general automatic feature recognition methodology that would recognize all types of features interactions. One of the drawbacks of automatic feature recognition systems is that they tend to be fairly complex and computationally intensive.
Direct digital prototyping and manufacturing
Published in Fuewen Frank Liou, Rapid Prototyping and Engineering Applications, 2019
The feature-based approach has immediate success in some areas as features can provide an effective bridge between CAD and CAM. However, limitations do occur. The critical step in transforming a CAD drawing into manufacturing information is called feature recognition. There is a lot of literature reported in the area of feature recognition [Chang90, Subrahmanyam95]. Most of the feature recognition algorithms use some kind of pattern matching to determine the machining sequence for some previously existing entity, to divide the cavity into laminae, or to decompose the part into primitives of arbitrary shapes, etc. The obvious limitations of these methods are that they apply to a domain of specific features. However, there are challenges in automated feature recognition, especially when dealing with intersecting features.
Methodologies of Feature Representations
Published in Awais Ahmad Khan, Emad Abouel Nasr, Abdulrahman Al-Ahmari, Syed Hammad Mian, Integrated Process & Fixture Planning, 2018
Awais Ahmad Khan, Emad Abouel Nasr, Abdulrahman Al-Ahmari, Syed Hammad Mian
Feature recognition can be defined as the identification and grouping of feature entities from a geometric model. In fact, it extracts features and their parameters from the solid models. Generally, the identified entities (i.e., the recognized features) are extracted from the model and engineering information such as tolerances and non-geometric attributes are then attached to the feature entities [31]. The flow diagram of the feature recognition can be seen in Figure 2.6. There are a number of factors that make feature recognition an indispensable part of the CAD/CAM systems [32]. Efficient utilization of CAD data in downstream applications.Nonexistence of industry standard for feature definition and storage.Solid models without feature information cannot be easily edited without using feature recognition systems.There are various types of features such as design feature or manufacturing depending on the application requirement. Therefore, feature recognition systems are required to identify the features for different applications.In CAE such finite element analysis (FEA), there is a need to recognize and inhibit the unwanted features and speed the analysis process.
Development and performance evaluation of a web-based feature extraction and recognition system for sheet metal bending process planning operations
Published in International Journal of Computer Integrated Manufacturing, 2021
Eriyeti Murena, Khumbulani Mpofu, Alfred T Ncube, Olasumbo Makinde, John A Trimble, Xi Vincent Wang
Due to the increase in product variety and mass customisation, Computer-Aided Process Planning(CAPP) (Harik et al. 2008) played an essential role in manufacturing over the past decade. It works has a link between product design and product manufacturing as it generates the procedures to manufacture a product. Manufacturing industries require vastly sophisticated CAPP technologies and methods that merge with the manufacturing systems. To reduce production cycle time and human errors, continuous integration of CAD and Computer-Aided Manufacturing (CAM) are very crucial (VENU and KOMMA 2017). The integration of CAPP with CAD and CAM, the automation of CAPP, data exchange and collaboration, reconfigurable systems offers flexibility to today’s manufacturing industries. Feature Recognition (FR) is a vital key to the automation of CAPP because the information obtained from FR and extraction system is used as input in all the three other stages of CAPP, namely, tool selection, manufacturing sequence and the remote planning, which is the output of the process plan. Feature recognition is the conversion of geometrical data into a feature model. The feature model is what the machine is required to produce.
User-assisted integrated method for controlling level of detail of large-scale B-rep assembly models
Published in International Journal of Computer Integrated Manufacturing, 2018
Soonjo Kwon, Byung Chul Kim, Duhwan Mun, Soonhung Han
Feature recognition-based methods find and remove features with engineering significance, based on the topological information of the model. They can be divided into local feature recognition and arbitrary feature recognition methods (Thakur, Banerjee and Gupta 2009). The local feature recognition methods were developed mainly for reducing the analysis time in the engineering analysis field. Zhu and Menq (2002) recognised the fillet and round features on the B-rep model based on the homomorphic equivalence of the trace face chain after defining the trace face. Joshi and Dutta (2003) defined the rules for each feature in order to recognise the hole, fillet, boss and some complex local features in the B-rep model, and recognised the feature according to the rule. The arbitrary feature recognition methods have been developed variously according to the type of shape to be recognised. Venkataraman and Sohoni (2002) proposed a face deletion algorithm that finds boundary loops in a B-rep model and removes the related faces to reconstruct a feature volume. Sun, Gao and Zhao (2010) proposed a loop decomposition method based on co-defined edges and co-surface loops, and suppressed regions by filling the detected loop regions. Koo and Lee (2002) proposed a way to simplify part and assembly models by exploring the convex inner loop in a B-rep model. Seo et al. (2005) extended the existing wrap-around operation (Koo and Lee 2002) by considering fillet, round, hole and concave faces.
Manufacturability analysis for additive manufacturing using a novel feature recognition technique
Published in Computer-Aided Design and Applications, 2018
Yang Shi, Yicha Zhang, Steven Baek, Wout De Backer, Ramy Harik
Automatic feature recognition has been an active research area for decades. Many different techniques have been proposed. The basic problem that feature recognition technology tries to solve is identifying high-level information from the low-level geometric entities [11], such as a collection of faces, edges, vertices and the connectivity relationships in a CAD model and interpreting such high-level hints as a set of features.