Jiduo Zhang

The Design of Minimal Deep Learning Systems for Online Tool Condition Monitoring in Stacked Drilling

Jiduo Zhang, Dongze He, Robert Heinemann, Otto Jan Bakker

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In-process unified prediction for process incidence and tool wear based on a deep learning approach

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker

When drilling carbon fibre reinforced polymer (CFRP)/Al stacks, adaptive drilling can allow for the optimisation of cutting parameters for each stack layer, which not only increases cutting efficiency but also improves borehole quality. This work proposes a deep learning approach to both …

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EBPC: a deep learning cloud computing framework for hybrid stack drilling monitoring

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker

Abstract Single-shot drilling of stacks composed of Carbon Fibre Reinforcement Polymers (CFRP) and aluminium (AL) is a common operation in aircraft assembly, where adaptive drilling that allows real-time adjustment of cutting parameters is crucial to improve assembly strength. Although deep learning approaches …

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Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker

In drilling Carbon-Fibre-Reinforced Polymers (CFRP)/Al stacks, adaptive drilling facilitates the optimisation of cutting parameters for each constituent stack layer and tool wear, thus enhancing cutting efficiency and borehole quality. This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both …

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Minimum sufficient signal condition of identifying process incidence in stacked drilling through deep learning

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker, Xiaoyu Xiao et autres

The determination of minimum sufficient condition of machining signals in representing events is vital to not only the procedure of signal acquisition, transfer, and storage, but also the design, training and deployment of deep learning and its integrated system. This paper proposed …

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Intelligent machining of CFRP composites via data-driven prediction and optimization: Advances, challenges and future prospects

Jia Ge, Jiduo Zhang, Midie Xu, Ming Wu et autres

The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However, …

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Process incidence monitoring in material identification during drilling stacked structures using support vector machine

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker

Abstract Drilling of stacks comprising carbon fibre-reinforced polymers (CFRP) and aluminium in a single shot is a typical operation in the assembly of aircraft. This paper proposes a novel approach to identify incidences in CFRP/Al stack drilling with 94 % classification accuracy …

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Cross-Material Adversarial Neural Network Tool Wear Monitoring for Drilling Aerospace Stacks

Yixian Ding, Leilei Pan, Yueming Liu, Jiduo Zhang

In the aerospace sector, multi-material stacks are widely used. However, it is harder for monitoring tool wear in drilling the stacks, due to the non-uniformed properties of materials leading data distribution shifts for the models. To address the inconsistent feature distribution, this …

cn, gb (code pays fourni par la source)

Minimum Sufficient Signal Condition of Identifying Process Incidence in Stacked Drilling Through Deep Learning

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker, Siqi Li et autres

In-process tool incidence identification based on temporal pyramid pooling and convolutional neural network

Jiduo Zhang, Robert Heinemann, Otto Jan Bakker, Menghui Zhu

Adaptive drilling allows for the change of cutting parameters when drilling multi-material stacks, for example carbon fibre reinforced polymer (CFRP) combined with Al which are commonly found in the aerospace industry. This work proposes a deep learning approach to identify process incidences …

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Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability

Huibin Sun, Junlin Pan, Jiduo Zhang, Dali Cao

cn (code pays fourni par la source)

In-process tool condition forecasting based on a deep learning method

Huibin Sun, Jiduo Zhang, Rong Mo, Xianzhi Zhang

cn, gb (code pays fourni par la source)