Abstract
In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3-D model. In this research area, recognizing and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this article presents a novel deep learning approach named SsdNet to tackle the machining feature localization and recognition problem. The typical SSD is designed for 2-D image objection detection rather than 3-D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilized to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localization performance (90.62% F-score), and recognition efficiency (243.85 ms per model).
Highlights
In the realm of manufacturing, every product starts with a computer-aided design (CAD) model
Based on the framework presented in the previous section, this section first makes a comparison between the proposed approach and other learning-based approaches in terms of intersecting machining feature localisation and recognition
This paper proposed a novel method for intersecting feature localisation and recognition via single shot multibox detector (SSD)
Summary
In the realm of manufacturing, every product starts with a (or a set of) computer-aided design (CAD) model (or models). One of the essential steps towards such advance, is the ability of a machine to “understand” a given CAD model, that is, recognise any machining features of the model. Most methods were implemented based on manually designed rules. In these rule-based approaches, recognising a wide variety of highly intersecting features remains a somewhat challenging task [1]–[4] as in-depth knowledge about different features and feature combinations is required. Machine learning techniques have been widely utilised in the area of smart manufacturing
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