A STEP-based machining data model for autonomous process generation of intelligent CNC controller

Zhu, Wendan; Hu, Tianliang; Luo, Weilin; Yang, Yan; Zhang, Chengrui

doi:10.1007/s00170-017-1554-9

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…Naranje and Kumar 18 described a knowledge-based system (KBS) developed for process planning of axisymmetric deep drawn sheet metal parts, whose technical knowledge is acquired from different sources of knowledge acquisition. Zhu et al 19 proposed a STEP-based machining data model for autonomous process generation of intelligent computer numerically controlled (CNC) controller, which can provide necessary workpiece information for intelligent process planning functions based on knowledge base. Huang et al 20,21 established a process knowledge database with existing manufacturing model by data mining; the similarity assessment of machining features was carried out for process reuse, while the geometrical similarity is kind of strict.…”

Section: Literature Reviewmentioning

confidence: 99%

A reliable process planning approach based on fuzzy comprehensive evaluation method incorporating historical machining data

Mou

Gao

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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The quality of process planning could directly affect product quality, machining efficiency and cost. In small batch production such as machining aircraft structural parts, human experience is dominant in the process planning of those parts with great variability. Inferior planning of the machining process directly leads to low efficiency and quality, which has serious impact on the lead time of aircraft structural parts. To address these problems, different from the existing process knowledge reuse method by estimating the geometric similarity, a more reliable process planning method based on fuzzy comprehensive evaluation via historical machining data is proposed in this article. As long as machining resources are determined, a feature-based historical machining data model can be built, and the similarities between new machining features and the features in the database are estimated accordingly. Machining strategy, which contains tool path strategy and machining parameters, can then be identified according to the evaluation results of the similar features based on entropy weight method. A prototype system is developed and successfully applied to the typical aircraft structural parts.

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Section: Literature Reviewmentioning

confidence: 99%

A reliable process planning approach based on fuzzy comprehensive evaluation method incorporating historical machining data

Mou

Gao

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Process planning serves as an intermediary phase that bridges the gap between the design of a product and its subsequent production [1]. This endeavour aims to devise a practical and efficient process sequence solution for the part while allocating manufacturing resources to ensure costeffective and proficient production [2].…”

Section: Introductionmentioning

confidence: 99%

“…This endeavour aims to devise a practical and efficient process sequence solution for the part while allocating manufacturing resources to ensure costeffective and proficient production [2]. In the conventional approach to process planning, the process designer engages in a manual design process, drawing upon their current knowledge and experience [3]. Researchers have extensively investigated different retrieval techniques and intelligent optimisation algorithms in process planning since the 1960s [4].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

Zhang,

Wu,

Chen

et al. 2024

Preprint

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With the wide application of computer-aided technologies such as CAD and CAM in the manufacturing industry, more and more process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, more effective methods are needed to mine a large amount of multi-source information and the explicit and implicit relationships between knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning problem as a sequential recommendation of process knowledge. Secondly, this paper designs in detail the state representation that combines process sequences and potential relationships between processes. This paper also creates a composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure proposed in this paper has more accurate recommendation results than other methods. Finally, this paper takes flange as an example to verify the feasibility and effectiveness of the proposed method.

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“…The standards for the Exchange of Product model data (STEP / ISO 10303) and Standard for the Exchange of Product model data for Numerical Control (STEP-NC / ISO 14649) allow encoding the complete machining information, as well as the exchange of data as the length of the design-machining chain [23], [24]. The STEP-NC data model provides much more comprehensive information and overcomes the G-M code's disadvantages, but still limited for autonomous process planning of the intelligent CNC controller [25].…”

mentioning

confidence: 99%

Correction of Errors during the Manufacture by Computer Numerical Control (CNC) of Blades for an Axial Hydrokinetic Turbine

Graciano-Uribe

Arbeláez

Hincapie

et al. 2022

IJIE

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The design and manufacture of new systems for providing electric power to non-interconnected areas are challenges for engineering. There are several alternatives, including water or wind-power generation systems, where hydrokinetic turbines are highlighted. This work establishes the methodology, identification, and correction of errors generated during the manufacture by machining, using CAD/CAPP/CAM techniques, for an axial hydrokinetic turbine. During the manufacturing process, the generation of an error on the edges of the blades was identified, which was attributed to problems in the design of the model since the degrees of freedom of the manufacturing system used were not considered. For the manufacture of complex surfaces in the design of models, the most extreme points of the surfaces in contact must match the tangent edges to ensure that machining tools can reach them with the trajectories generated from the CAM.

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A STEP-based machining data model for autonomous process generation of intelligent CNC controller

Cited by 19 publications

References 27 publications

A reliable process planning approach based on fuzzy comprehensive evaluation method incorporating historical machining data

A reliable process planning approach based on fuzzy comprehensive evaluation method incorporating historical machining data

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

Correction of Errors during the Manufacture by Computer Numerical Control (CNC) of Blades for an Axial Hydrokinetic Turbine

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