A Case Retrieving System for Machining Processes of Axisymmetric Parts Based on Geometric Indexing and Process Hierarchical Structure

1998 ◽  
Author(s):  
Hsin-chi Chang ◽  
Wen F. Lu ◽  
Xiaoqing Frank Liu
Keyword(s):  
Hierarchical Structure ◽  
Process Planning ◽  
Global Market ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Machining Processes ◽  
Similarity Metric ◽  
The Core ◽  
Feature Based ◽  
Cutting Processes

Abstract To develop a new machining process plan in the competitive global market, one effective way is to retrieve a relevant case similar to the new desired case and then adapt it to meet the new situations. This paper proposes a retrieving system for process planning cases from two aspects — the representation and manufacturing processes of a part. The core of the retrieving system contains 1) the feature-based representation of a part, 2) indexing of a part based on two-dimensional peripheral indexing and feature indexing, 3) a hierarchical structure of cutting processes from part cutting history, 4) a similarity metric. The similarity metric is used to measure the similarity between the new desired part and one old case based on the indexes of the parts. The application domain here is the process planning for axisymmetric parts. The proposed retrieving system is implemented in a Sun workstation using ACIS 3D-Toolkit and C++.

Späne- und Stauberfassung bei der CFK-Zerspanung*/Extraction of chips and dust in machining operations of CFRP. Investigation on CFRP dry machining in industrial applications

2018 ◽  
Vol 108 (06) ◽  
pp. 473-478
Author(s):  
A. Gebhardt ◽  
M. Schneider
Keyword(s):  
Industrial Applications ◽  
Machining Process ◽  
Dry Machining ◽  
Machining Processes ◽  
Dry Cutting ◽  
Clamping System ◽  
Cutting Processes ◽  
Machining Operations

Bauteile aus CFK (kohlenstofffaserverstärkte Kunststoffe) werden meist spanend endbearbeitet. Diese Bearbeitung kann unter Überflutung durch KSS (Kühlschmierstoffe) oder trocken stattfinden. Die hier vorgestellte Studie zeigt für die Trockenzerspanung, wie die notwendige Erfassung von Stäuben und Spänen stattfindet, welche Technologien eingesetzt werden und wie eine Maschinen- und Bauteilreinigung aussieht.   A machining process is mostly used as a last step in the production of workpieces made of CFRP. In this machining process lubricants may be used or dry cutting processes are applicated. The here presented study shows for dry machining processes, which technologies are used for the dust and chip extraction. Furthermore, the techniques for the cleaning of the machine, the clamping system and workpiece are presented.

Integrated Feature Modeller for Certain Manufacturing Processes

2005 ◽  
Author(s):  
C. Jebaraj ◽  
D. Kingsly Jeba Singh
Keyword(s):  
Process Planning ◽  
Cutting Tools ◽  
Manufacturing Processes ◽  
Second Phase ◽  
Optimal Sequence ◽  
Planning Stage ◽  
System Maps ◽  
Feature Based ◽  
Manufacturing Applications ◽  
Feature Based Design

This work explains the development of an integrated modeler, which is applied in the design-to-manufacturing stages of manufacturing processes namely machining, sheet metal processing and forging. Its system architecture is broadly divided into four modules namely, Feature Based Design (FBD), Virtual Factory Environment (VFE), Process Based Feature Mapping (PBFM) and Process Planning (PP). Feature based design is used for the design, modeling, synthesis, representation and validation of the components for manufacturing applications. New set of features namely integrated features are pre-defined as feature templates and instanced to get / derive the information required for the design-to-manufacturing stages of the components. VFE defines the factory, which provides the database for operations, machines, cutting tools, work pieces etc. The knowledge base of the developed system maps validated features of the component into operation sets in the first phase of the PBFM. Each operation in the operation sets can be executed using different machines and tools in a factory. All these possible choices are obtained in the second phase of PBFM. Genetic algorithm is used to find the optimal sequence of operations, machines and tools for different criteria in the process planning stage. This paper explains the developed system with case studies.

Machining Feature Modeling and Process Intermediate Model Generation in Process Planning

2013 ◽  
Author(s):  
Xu Zhang ◽  
Chao Liang ◽  
Tiedong Si ◽  
Ding Ding
Keyword(s):  
Process Planning ◽  
Feature Recognition ◽  
3D Models ◽  
Machining Process ◽  
Model Generation ◽  
Machining Features ◽  
Intermediate Model ◽  
Machining Feature ◽  
Feature Based ◽  
Solid Modeler

In process planning of machined part, machining feature recognition and representation, feature-based generative process planning, and the process intermediate model generation are the key issues. While many research results have been achieved in recent years, the complete modeling of machining features, process operations, and the 3D models in process planning are still need further research to make the techniques to be applied in practical CAPP systems. In this paper, a machining feature definition and classification method is proposed for the purpose of process planning based on 3D model. Machining features are defined as the surfaces formed by a serious of machining operation. The classification scheme of machining features is proposed for the purpose of feature recognition, feature-based machining operations reasoning, and knowledge representation. Recognized from B-Rep representation of design model, machining features are represented by adjacent graph and organized by feature relations. The machining process plan is modeled as operations and steps, which is the combination and sequencing of machining feature’s process steps. The process intermediate models (PIM) are important for process documentation, analysis and NC programming. An automatic PIM generation approach is proposed using local operations directly on B-Rep model. The proposed data structure and algorithm is adopted in the development of CAPP tool on solid modeler ACIS/HOOPS.

Computer Aided Process Planning for Machining the Gears

2011 ◽  
Vol 264-265 ◽  
pp. 1551-1556
Author(s):  
Deepak Byotra ◽  
Rajesh Kumar Bhushan
Keyword(s):  
Process Planning ◽  
Hybrid Approach ◽  
Machining Processes ◽  
Machining Time ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Set Up ◽  
Cutting Processes ◽  
Machining Operations ◽  
Grinding Operations

Bulk of power transmitting metal gears of machinery is produced by machining processes from cast, forged or hot rolled blanks. It includes a number of versatile machining operations that use a milling cutter, a multi tooth tool to produce a variety of configurations. The aim of the computer aided process planning (CAPP) is to develop a programme for milling cutting processes. This paper reveals the hybrid approach to computer aided process planning for milling and grinding operations on gear blank, so that the plan can be generated taking into account the availability of machines and the material. The developed computer aided process plan has reduced the set up time and machining time by 40.90 and 30.15 % respectively.

INTEGRATION OF PRODUCT AND PROCESS PLANNING BASED ON NEURAL NETWORKS IN COOPERATIVE DESIGN METHODOLOGY

2013 ◽  
Vol 12 (02) ◽  
pp. 107-129 ◽  
Author(s):  
C. NUMTHONG ◽  
S. TICHKIEWITCH
Keyword(s):  
Neural Networks ◽  
Process Planning ◽  
Machining Process ◽  
Machining Processes ◽  
Product Representation ◽  
Cooperative Design ◽  
Team Members ◽  
The Neural Networks ◽  
Rotational Parts ◽  
Product And Process

Integration of product representation and process planning is necessarily to couple features of product data and machining processes. The product representation and knowledge of design and process planning should be shared and distributed between a product designer and process planner. Cooperative design and process planning works are proposed in this paper in order to integrate and exchange information between team members to obtain the efficiency of product design and process planning. In addition, this paper presents the integration of product representation and process planning of rotational parts based on neural networks technique. The neural networks technique is used to contribute in selecting suitable machining process of rotational parts. In addition, we also present CoDeMo (Cooperative Designing Modeler). It is a platform using support cooperative methodology for integrating the rotational product representation and process planning. The case study is tested with a sample part from the exemplary company.

Classification and Automatic Feature-Based Extraction Approach for Cylindrical and Milling Parts

2021 ◽  
Vol 11 (3) ◽  
pp. 55-73
Author(s):  
Sathish Kumar Adapa ◽  
Dowluru Sreeramulu ◽  
Jagadish
Keyword(s):  
Case Studies ◽  
Process Planning ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Machining Process ◽  
Automatic Extraction ◽  
Java Program ◽  
Feature Based ◽  
Logical Rules ◽  
Conventional Machining

This paper reports classification and automatic extraction of various cylindrical and milling features in conventional machining process parts. In this work, various algorithms like hole recognition algorithm (HRA) and milling feature recognition algorithm (MFRA) have been used for identification of different cylindrical and milling features. A cylindrical feature is identified based on specific logical rules, and milling feature is identified based on the concept of concave decomposition of edges. In-house developed JAVA program is used to write algorithm, and then validation of the algorithm is done through two case studies. The HRA and MFRA algorithms extract the cylindrical features (through holes, blind holes, taper holes, and bosses) and milling features (slot, blind slot, step, blind step, pockets) precisely. The current work is well suitable to extract the features in conventional machining parts and thereby improve the downstream applications likes process planning, CAPP, CAM, etc.

Fuzzy Logic for Machining Applications

2019 ◽  
pp. 341-361
Author(s):  
Divya Zindani ◽  
Apurba Kumar Roy ◽  
Kaushik Kumar ◽  
J. Paulo Davim
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Optimization Techniques ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Machining Performance ◽  
Machining Processes ◽  
Performance Variables ◽  
Increasing Trend ◽  
Modern Manufacturing

There have been umpteen research reports on the usage of artificial intelligence (AI) strategies for modelling various machining processes. One of the well-known AI strategies is that of fuzzy logic (FL) techniques that has been used for prediction of machining performance variables for both the categories of machining processes and controls the machining process. Given the increasing trend of FL in machining, the chapter reviews the application of fuzzy logic in modelling and controlling the machining processes. The work begins with introduction section and then proceeds to discuss the importance role played by FL strategies in the traditional and modern manufacturing processes. The work summarizes some of the major applications of FL-based systems in various machining processes. Limitations, advantages, and the improvements to minimize the limitations are then discussed. The authors of the chapter hope that the review will aid all those researching in the domain of manufacturing sciences and their optimization techniques.

Quality Assured Setup Planning Based on the Stream-of-Variation Model for Multi-Stage Machining Processes

2006 ◽  
Author(s):  
Jian Liu ◽  
Jianjun Shi ◽  
S. Jack Hu
Keyword(s):  
Product Quality ◽  
Cost Effective ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Setup Planning ◽  
Machining Processes ◽  
Multi Stage ◽  
Variation Propagation ◽  
Stream Of Variation ◽  
The Cost

Setup planning is a set of activities to arrange manufacturing features into an appropriate sequence for processing. As such, setup planning can significantly impact the product quality in terms of dimensional variation in the Key Product Characteristics (KPC’s). Current approaches in setup planning are experience-based and tend to be conservative by selecting unnecessarily precise machines and fixtures to ensure final product quality. This is especially true in multi-stage manufacturing processes because it has been difficult to predict the variation propagation and its impact on KPC quality. In this paper, a new methodology is proposed to realize cost-effective, quality ensured setup planning for multi-stage manufacturing processes. Setup planning is formulated as an optimization problem based on quantitative evaluation with the Stream-of-Variation (SoV) models. The optimal setup plan minimizes the cost related to process precision and satisfies the quality specifications. The effectiveness of the proposed approach is demonstrated through setup planning for a multi-stage machining process.

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