Abstract
In modern manufacturing industry featured with automation and flexibility, the intelligent tool management for Computer Numeric Control (CNC) machine plays an essential role in manufacturing automation. The automatic tool recognition in terms of geometric shapes, materials and usage functions could facilitate the seamless integration with downstream process planning and scheduling processes. In this paper, a intelligent tool recognition system is proposed with a novel hybrid framework of multi-channel deep learning network with non-iterative and fast feedforward neural network to meet high efficiency and accuracy requirement in intelligent manufacturing. The combination of the fine-tuning Convolutional Neural Networks (CNNs) with the random parameter assignment mechanism of Extreme Learning Machines (ELMs) reach a balance in accurate feature extraction and fast recognition. In the proposed hybrid framework, features extracted from efficient CNNs are aggregated into robust ELM auto-encoders (ELM-AEs) to generate the compact but rich feature information, which are then feed to the subsequent single layer ELM network for tool recognition. The performance of proposed framework is verified on several standardized 3D shape retrieval and classification dataset, as well as on a self-constructed multi-view 3D data represented tool library database. Numerical experiments reveal a promising application perspective of proposed intelligent recognition system on manufacturing automation.
Highlights
As the central coordinator of machinery manufacturing industry, the manufacturing execution system takes charge of manufacturing plan execution, manufacturing information feedback, and massive manufacturing information distribution [1]–[3]
To process the 3D data, a multi-view Convolutional Neural Networks (CNNs) (MVCNN) [7] is utilized to extract efficient object features. Those features are assembled into an Extreme Learning Machines (ELMs)-AE [8] for obtaining robust feature aggregation, a fast ELM classifier is employed on top of the network to perform recognition, to make the whole network an ELM-embedded deep learning framework
In this paper, a hybrid deep learning framework is proposed to build up an intelligent machine tool management system
Summary
As the central coordinator of machinery manufacturing industry, the manufacturing execution system takes charge of manufacturing plan execution, manufacturing information feedback, and massive manufacturing information distribution [1]–[3]. To process the 3D data, a multi-view CNN (MVCNN) [7] is utilized to extract efficient object features Those features are assembled into an ELM-AE [8] for obtaining robust feature aggregation, a fast ELM classifier is employed on top of the network to perform recognition, to make the whole network an ELM-embedded deep learning framework. Numerical experiment results show that our proposed method balances between recognition accuracy and VOLUME 8, 2020 computational efficiency, the key contributions of this work are summarized as below: 1) A tool library database is constructed according to the actual 3D structures of machine tools, as a fundamental component to validate the feasibility of our proposed tool recognition system applying in intelligent machinery manufacturing industry.
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