The transition to intelligent manufacturing provides a fulcrum for the revolution of product lifecycle like design, manufacturing and maintenance, so does it for process planning. Specifically, digital twin manufacturing cell (DTMC) is regarded as a new means of and also a basic unit for implementing intelligent manufacturing. Incorporating process planning in DTMC could improve the integrity of DTMC and enhance the feasibility of process planning. Consequently, this paper proposes a deep learning-enabled framework for intelligent process planning towards DTMC. Firstly, a process knowledge reuse network (PKR-Net) that takes deep residual networks as base architecture is embedding into the framework, which could understand design intents expressed in a drawing or a 3D computer-aided design (CAD) model via its views and automatically retrieve relevant knowledge for the quick generation of theorical processes. Then, an evaluation twin is constructed to transform the theorical processes into practical operations and produce an optimal process plan. Finally, a test bed of the framework is constructed and the experimental results demonstrate the feasibility and effectiveness of the approach.

Deep learning-enabled intelligent process planning for digital twin manufacturing cell

As the core link of intelligent manufacturing, the process planning of aviation parts still faces the challenges such as relying on manual experiences for process decision-making and lack of linkag...

A twin data and knowledge-driven intelligent process planning framework of aviation parts

Manufacturing industries are constantly pressured by market demands to produce low-cost, high-quality products. Market demands tend to be complex and full of variations, especially in fast-paced in...

Intelligent machining: a review of trends, achievements and current progress

Intelligent process planning for smart manufacturing systems: a state-of-the-art review

Industry 4.0 paves the way to smart factory through integrating Internet of Things, cyber-physical systems, cloud manufacturing, and big data analytics for various industrial fields. Especially, the machinery industry significantly benefits from these automation techniques in machine-tool quality inspection, cutting-tool remaining-useful-life, surface roughness prediction, etc. However, these innovation technologies are still not executable in the aerospace-component production due to their complex machining conditions, which result in long metrology delays and severe component deformations. To overcome these two challenges of the aerospace industry, this letter proposes to apply the automatic virtual metrology (AVM) system to successfully convert sampling inspections with metrology delay into real-time and online total inspection and to utilize the so-called deformation fusion (DF) scheme for dealing with the component-deformation problem. The application of the flange operation of aerospace engine-case manufacturing is adopted to illustrate the performance of the AVM-plus-DF approach.

Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing

An Intelligent Process Planning Method Based on Feature-based History Machining Data for Aircraft Structural Parts

In order to explore the dynamic variation rule of temperature distribution on surface of optical element, the temperaturerise effect of atmospheric plasma processing was analyzed by using simulation model, and the corresponding thermal stress mathematical model was established for fused silica. On this basis, the experiment of plasma processing was carried out, the dynamic variation rule of temperature distribution on surface of four materials of aluminum, aluminum alloy, SiC and fused silica during the atmospheric plasma processing was analyzed, and the verification and comparison with the simulation results were carried out. The results show that the arc discharge atmospheric plasma polishing technology can effectively remove the damage layer of fused silica optical element by physical polishing, and the simulation model can provide data support for the atmospheric plasma processing technology.

Research on temperature and thermal stress in atmospheric plasma machining based on arc method

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An Intelligent Process Planning Method Based on Feature-based History Machining Data for Aircraft Structural Parts

Research on temperature and thermal stress in atmospheric plasma machining based on arc method