Showing 4 results for Process Planning
Davood Manafi, Mohammad Javad Nategh,
Volume 15, Issue 10 (1-2016)
Abstract
Computer-aided process planning (CAPP) is a bridge for integrating computer-aided design (CAD) and computer-aided manufacturing (CAM). One of the basic computer-aided process planning tasks is sequencing of machining features. Sequencing of machining features is determined based on technical and geometrical rules. In this paper, the technical rules, geometrical rules and sequencing of machining features method were discussed. At first, some of the technical rules were pointed. Then, the geometrical interactions were studied and two new geometrical rules were introduced for sequencing the machining features having geometrical interaction. These rules can yield unique results and they are identified easily by the computer systems. Also, an algorithm was introduced for automated application of these geometrical rules in computer systems. The conflict between the technical and geometrical rules that may occur in some cases was studied. This conflict must be considered in the sequencing of machining features methods. Finally, an algorithm was introduced for sequencing of machining features based on permutation. In this algorithm the technical and geometric rules were applied separately and step by step. If there is any conflict between technical and geometrical rules, this conflict could detect automatically in this algorithm. Algorithms were programmed and verified in PythonOCC.
, ,
Volume 22, Issue 10 (10-2022)
Abstract
Extracting the required information from the design file is one of the main steps in the computer aided process planning. In previous methods of extracting machining features, various methods such as graph-based method, volume analysis method, logic rules method and other methods have been used. In all the previous methods, whether traditional methods or methods based on artificial intelligence, the input data to the machine feature identification system is the output information of a computer-aided design system. Converting the output information of a computer-aided design system to input data of a machining feature identification system is faced with limitations such as the variety of format and type of data arrangement, deleting some data from the design file due to geometric interference of features, slow extraction of features due to extensive information in the design file and the limitation of identifying different types of machining features by a unity feature identification system. In the present study, using artificial intelligence techniques based on deep learning, machining features are extracted directly from the two-dimensional image of a workpiece. The image may be prepared by a computer-aided design file, or it can be taken by a camera.
Davoud Manafi, Mohammad Javad Nategh,
Volume 22, Issue 10 (10-2022)
Abstract
Computer-aided process planning is one of the challenges for researchers to achieve computer-integrated manufacturing, and setup planning is the core of the CAPP system. Based on the literature survey, it has been observed that researchers use different methods for setup planning, and there is a lack of mathematical models in their methods. However, the mathematical model is necessary to implement and develop the setup planning method. Therefore, in this paper, the permutation-based setup planning was selected to determine the setups, and then the setup planning rules were cast into the mathematical model. Finally, the mathematical model is implemented and evaluated in MATLAB software to ensure the accuracy of this model.
Naser Mohammadi, Mohammad Javad Nategh,
Volume 23, Issue 6 (5-2023)
Abstract
In the production of industrial parts, machining is one of the most important operations in the field of manufacturing parts. The production of an industrial part takes place in three stages: design, process planning and manufacturing, and in all these stages, the computer is used as a powerful tool. In computer-aided process planning, the stage of identifying machining features is a prerequisite and an introduction to the next steps. Extracting information and identifying features from computer-aided design information has been continuously improved due to the increasing complexity of parts, but the research to find an optimal solution is endless. Over the past few decades, several methods have been introduced and applied by researchers to extract and identify machining features from design file information. In all the previous methods, the number and type of features are extracted as independent variables in the machining features identification pattern and from the part design file data. In this research, the charectrestics required to identify the machining features are extracted from the pixel values of the machining feature image by the artificial intelligence system automatically. The artificial intelligence system produced to identify the machining features in this research is able to identify all the information required for machining, including the name, the coordinates of the location of the feature relative to the part, and the dimensions required for the machining, by viewing the image of a part, and the information of the features present in the image the input to the system in a table.
