Computer-Aided Operation Planning for an Actual Machine Tool Based on Updatable Machining Database and Database-Oriented Planning Algorithm

Igari, Shinji; Tanaka, Fumiki; Onosato, Masahiko

doi:10.20965/ijat.2012.p0717

Cited by 10 publications

(1 citation statement)

References 4 publications

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“…It is said that the standardization of process planning is a key to improving the efficiency of machining operations in practice. The preparation of machining operations commonly takes a long time, because commercial CAM systems require a variety of machining process information such as machining method, machining sequence, etc., to generate NC programs (Igari, et al, 2012). Therefore, a computer aided process planning (CAPP) system able to bridge CAD and CAM systems is urgently needed to reduce the time and effort involved in preparing machining operations (Isnaini and Shirase, 2014).…”

Section: Introductionmentioning

confidence: 99%

Process planning for die and mold machining based on pattern recognition and deep learning

Hashimoto

Nakamoto

2021

JAMDSM

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Dies and molds are necessary elements in the manufacturing of current industrial products. There is increasing pressure to machine high quality complicated surfaces at low cost. The standardization of process planning is said to be a key to improving the efficiency of machining operations in practice. Thus, computer aided process planning (CAPP) systems are urgently needed to reduce the time and effort of preparing machining operations. However, it is difficult to generalize process planning that continues to depend on skillful experts and requires long preparation time for die and mold machining. On the other hand, to overcome issues that are difficult to generalize, it is well known that machine learning has the capability to estimate valid values according to past case data. Therefore, this study aims to develop a CAPP system that can determine machining process information for complicated surfaces of die and mold based on pattern recognition and deep learning, a kind of machine learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Using a voxel model representing targeted shape, it becomes easier to deal with the complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. Cutting tool type and tool path pattern are treated as machining process information determined in a CAPP system. The results of case studies confirm that the developed CAPP system is effective in determining the machining process information even for complicated surfaces according to the implicit machining know-how.

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Section: Introductionmentioning

confidence: 99%

Process planning for die and mold machining based on pattern recognition and deep learning

Hashimoto

Nakamoto

2021

JAMDSM

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Review of Computer-Aided Process Planning Systems for Machining Operation – Future Development of a Computer-Aided Process Planning System –

Isnaini¹,

Shirase²

2014

Int. J. Automation Technol.

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The need to integrate the design and machining stages has become an important issue since the introduction of the Computer-Integrated Manufacturing (CIM) concept. The development of the Computer-Aided Process Planning (CAPP) system has been recognized to have made a significant contribution toward fulfilling the requirement for an integrated planning system. This paper reviews the development of the CAPP system, particularly for the metal removal process. Previous reviews on CAPP are gathered and discussed to show the evolution stage of CAPP in general. Main research topics that contribute to the CAPP system development are shown. Six elements of the CAPP system are identified as the most important tasks in generating a process plan. These elements consist of: (1) model convention, (2) manufacturing operation selection, (3) manufacturing resource selection, (4) cutting condition selection, (5) tool path selection, and (6) setup selection. Six elements for the development of CAPP that contribute to process planning for metal removal process are discussed. The evolution stages of each element easily show the involvement of several tools in order to support the corresponding element. For further guidance, the methods of comprehending the involvement of manufacturing information in CAPP are discussed. Knowledge structuring and logic reasoning are the main organizational steps that can be used to describe the CAPP data architecture of manufacturing information. Further, the examples of full-scale CAPP in actualizing machining process planning are presented. Finally, key technologies for future development of CAPP are discussed.

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Integrated Information Model for Design, Machining, and Measuring Using Annotated Features

Tanaka¹,

Abe²,

Igari³

et al. 2014

Int. J. Automation Technol.

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Annotations on Geometric Dimensioning and Tolerancing (GD&T), surface roughness, etc. are needed for machining or measuring. However, these annotations are not used for the digital format in the product development process, nor is there any clear, explicit relationship between annotation, machining information, and measuring results. In this research, an integrated information model for design, machining, and measuring based on annotated features is proposed. A model for surface texture is also proposed because surface texture parameters are closely related to machining process parameters. A modeling system for the proposed integrated model is also implemented.

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Computer-Aided Operation Planning for an Actual Machine Tool Based on Updatable Machining Database and Database-Oriented Planning Algorithm

Cited by 10 publications

References 4 publications

Process planning for die and mold machining based on pattern recognition and deep learning

Process planning for die and mold machining based on pattern recognition and deep learning

Review of Computer-Aided Process Planning Systems for Machining Operation – Future Development of a Computer-Aided Process Planning System –

Integrated Information Model for Design, Machining, and Measuring Using Annotated Features

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