A Survey of Automated Process Planning Approaches in Machining

Global industrial trend is shifting towards next industrial revolution Industry 4.0. It is becoming increasingly important for modern manufacturing industries to develop a Computer Integrated Manufacturing (CIM) system by integrating the various operational and information processing functions in design and manufacturing. In spite of being active in research for almost four decades, it is clear that new functionalities are needed to integrate and realize a completely optimal process planning which can be fully compliant towards Smart Factory. In order to develop a CIM system, Computer Aided Process Planning (CAPP) plays a key role and therefore it has been the focus of many researchers. In order to gain insight into the current state-of-the-art of CAPP methodologies, 96 research papers have been reviewed. Subsequent sections discuss the different CAPP approaches adopted by researchers to automate different process planning tasks. This paper aims at addressing the key approaches involved and future directions towards Smart Manufacturing

Manufacturing Feature Recognition With 2D Convolutional Neural Networks

Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research presents a feature recognition method based on 2D convolutional neural networks (CNNs). First, a novel feature representation scheme based on heat kernel signature is developed. Heat Kernel Signature (HKS) is a concise and efficient pointwise shape descriptor. It can present both the topology and geometry characteristics of a 3D model. Besides informative and unambiguity, it also has advantages like robustness of topology and geometry variations, translation, rotation and scale invariance. To be inputted into CNNs, CAD models are discretized by tessellation. Then, its heat persistence map is transformed into 2D histograms by the percentage similarity clustering and node embedding techniques. A large dataset of CAD models is built by randomly sampling for training the CNN models and validating the idea. The dataset includes ten different types of isolated v features and fifteen pairs of interacting features. The results of recognizing isolated features have shown that our method has better performance than any existing ANN based approaches. Our feature recognition framework offers the advantages of learning and generalization. It is independent of feature selection and could be extended to various features without any need to redesign the algorithm. The results of recognizing interacting features indicate that the HKS feature representation scheme is effective in handling the boundary loss caused by feature interactions. The state-of-the-art performance of interacting features recognition has been improved

Modeling of an automatic CAD-based feature recognition and retrieval system for group technology application

In recent time, many researches have come up with new different approaches and means for Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) integration. Computer-Aided Process Planning (CAPP) is considered to be a bridge that connects these both technologies. CAPP may involve such an important technique as automatic feature extraction - a procedure that is engaged in process plans generation to be used in producing a designed part. Also in terms of CAD, the feature extraction procedure facilitates a cooperative design and process planning within the entire product development process. The main objective of the thesis is to present a new automatic feature extraction and classification system that is able to process mechanical rotational and non-rotational parts from the Opitz Code System point of view. The implemented system takes Standard for Exchange of Product data (STEP) - a neutral product representation format as input and extracts features of parts required for further manufacturing. The STEP format is used to provide geometrical and topological information about machining parts. A methodology to extract shape features was developed based on these geometrical and topological data. As output, the proposed system codes the extracted part features to Opitz Code System. CAD product files were taken from official manufacturers of mechanical parts in order to evaluate the developed system

Process capability modelling: a review report of feature representation methodologies

Approximately 150 technical papers on the features methodology have been carefully studied and some selected papers have been commented upon. The abstracts of the comments are documented and attached to this report. The methodologies reviewed are mainly divided into two approaches, ie. feature recognition and design by features. Papers which deal with some specific topics such as feature taxonomies, dimensions and tolerances, feature concepts, etc. are also included in the document

Automatic Feature Recognition and Tool Path Generation Integrated with Process Planning

The simulation and implementation of Automatic recognition of features from Boundary representation solid models and tool path generation for precision machining of features with free form surfaces is presented in this thesis. A new approach for extracting machining features from a CAD model is developed for a wide range of application domains. Feature-based representation is a technology for integrating geometric modeling and engineering analysis for the life cycle. The concept of feature incorporates the association of a specific engineering meaning to a part of the model. The overall goal of feature-based representations is to convert low level geometrical information into high level description in terms of form, functional, manufacturing or assembly features. Using the boundary representation technique, the information required for manufacturing process can be directly extracted from the CAD model. It also consists of a parameterization strategy to extract user-defined parameters from the recognized features. The extracted parameters from the individual features are used to generate the tool path for machining operations regardless of the intersection of one or more features. The tool path generation is carried out in two phases such as roughing and finishing. Various types of tool paths such as one-way, zig-zag, contour parallel are generated according to the type of the feature for the roughing operation. The algorithm automatically plans the sequence of machining operation with respect to the feature location, and also selects the type of tool and tool path to be used according to the feature. The finishing operation uses the tool path generation strategy in the same manner as used in roughing operation. The algorithm is implemented using the Solid works API library and verified with CNC milling simulator. The results of the work proved the efficiency of this approach and it demonstrate the applicability

STEP- Based Assembly Feature Recognition Using Attribute Adjacency Graph for Prismatic Parts

This paper introduces the concept of STEP AP203(STandard for Exchange of Product model data) an ISO standard as a neutral format for exchange of CAD model data between different CAD/CAM systems, and how STEP AP 203 data is stored and how the feature information can be extracted and recognized from STEP file. In this paper a hybrid (graph and rule) based approach is used to recognize the features of mechanical prismatic parts. The Attribute Adjacency Graph (AAG) and Attribute Adjacency Matrix (AAM) approaches are used to recognize the form features, and rule based approach is used to recognize assembly features.The proposed methodology in this paper has been completely implemented by designing an integrated system called STEP-based Assembly Sequence Planning (ST-ASP) system. The (ST-ASP) system is built by using Visual Basic 6.0 supported by Solid works 2011 package and implemented on (HP Pavilion dv6) PC. The (ST-ASP) system is directed to 3D prismatic parts. The form features explored in this system include both depression and protrusion features, and the assembly mating relations explored in this system include; against, fit, and insert which is used in recognize assembly features. Finally the system has been tested to carry out a case study to demonstrate the feasibility of the proposed methodology.Keywords:STEP, feature recognition, form feature, assembly feature, mating relations, attribute adjacent graph (AAG)

Optimal choice of machine tool for a machining job in a CAE environment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Developments in cutting tools, coolants, drives, controls, tool changers, pallet changers and the philosophy of machine tool design have made ground breaking changes in machine tools and machining processes. Modern Machining Centres have been developed to perform several operations on several faces of a workpiece in a single setup. On the other hand industry requires high value added components, which have many quality critical features to be manufactured in an outsourcing environment as opposed to the traditional in-house manufacture. The success of this manufacture critically depends on matching the advanced features of the machine tools to the complexity of the component. This project has developed a methodology to represent the features of a machine tool in the form of an alphanumeric string and the features of the component in another string. The strings are then matched to choose the most suitable and economical Machine Tool for the component’s manufacture. Literature identified that block structure is the way to answer the question ‘how to systematically describe the layout of such a machining centre’. Incomplete attempts to describe a block structure as alphanumeric strings were also presented in the literature. Survey on sales literature from several machine tool suppliers was investigated to systematically identify the features need by the user for the choice of a machine tool. Combining these, a new alphanumeric string was developed to represent machine tools. Using these strings as one of the ‘key’s for sorting a database of machine tools was developed. A supporting database of machine tools was also developed. Survey on machining on the other hand identified, that machining features can be used as a basis for planning the machining of a component. It analysed various features and feature sets proposed and provided and their recognition in CAD models. Though a vast number of features were described only two sets were complete sets. The project was started with one of them, (the other was carrying too many unwanted details for the task of this project) machining features supported by ‘Expert Machinist’ software. But when it became unavailable a ‘Feature set’ along those lines were defined and used in the generation of an alphanumeric string to represent the work. Comparing the two strings led the choice of suitable machines from the database. The methodology is implemented as a bolt on software incorporated within Pro/Engineer software where one can model any given component using cut features (mimicking machining operation) and produce a list of machine tools having features for the machining of that component. This will enable outsourcing companies to identify those Precision Engineers who have the machine tools with the matching apabilities. Supporting software and databases were developed using Access Database, Visual Basic and C with Pro/TOOLKIT functions. The resulting software suite was tested on several case studies and found to be effective

CAD/CAM integration based on machining features for prismatic parts

The development of CAD and CAM technology has significantly increased efficiency in each individual area. The independent development, however, greatly restrained the improvement of overall efficiency from design to manufacturing. The simple integration between CAD and CAM systems has been achieved. Current integrated CAD/CAM systems can share the same geometry model of a product in a neutral or proprietary format. However, the process plan information of the product from CAPP systems cannot serve as a starting point for CAM systems to generate tool paths and NC programs. The user still needs to manually create the machining operations and define geometry, cutting tool, and various parameters for each operation. Features play an important role in the recent research on CAD/CAM integration. This thesis investigated the integration of CAD/CAM systems based on machining features. The focus of the research is to connect CAPP systems and CAM systems by machining features, to reduce the unnecessary user interface and to automate the process of tool path preparation. Machining features are utilized to define machining geometries and eliminate the necessity of user interventions in UG. A prototype is developed to demonstrate the CAD/CAM integration based on machining features for prismatic parts. The prototype integration layer is implemented in conjunction with an existing CAPP system, FBMach, and a commercial CAD/CAM system, Unigraphics. Not only geometry information of the product but also the process plan information and machining feature information are directly available to the CAM system and tool paths can be automatically generated from solid models and process plans

A concurrent approach to automated manufacturing process planning

textWith the increasing demand of fast-paced and hybrid manufacturing processes in modern industry, it is desirable to expedite the iterations between design and manufacturing through intelligent computational techniques. In this research, we propose a concurrent approach of this kind to streamline the design and manufacturing processes. With this approach, a CAD design is automatically analyzed in terms of its manufacturability in the early design stage. If the part is manufacturable, a set of process plans optimized in time, cost, fixture quality and tolerance satisfaction are reported in real time. If the part is not manufacturable, the potential design changes are provided for better manufacturing. In the approach, the geometric information of 3D models and the empirical knowledge in manufacturing processes, fixtures, and tolerances are combined and encapsulated into a graph-grammar based reasoning. The reasoning systematically extracts meaningful manufacturing details that later constitute complete process plans for any given solid model. The plans are then evaluated and optimized using a specially designed multi-objective best first search technique. The complete approach enables a concurrent and efficient manufacturability analysis tool that closely resembles real manufacturing planning practice. Numerous case studies with real engineering parts are presented to characterize the novelty and contributions of this approach. The optimality of the suggested plans is verified through computational comparisons, and the practicality of the plans is validated with hands-on implementations on the shop floor.Mechanical Engineerin

A knowledge-based approach for the extraction of machining features from solid models

Computer understanding of machining features such as holes and pockets is essential for bridging the communication gap between Computer Aided Design and Computer Aided Manufacture. This thesis describes a prototype machining feature extraction system that is implemented by integrating the VAX-OPS5 rule-based artificial intelligence environment with the PADL-2 solid modeller. Specification of original stock and finished part geometry within the solid modeller is followed by determination of the nominal surface boundary of the corresponding cavity volume model by means of Boolean subtraction and boundary evaluation. The boundary model of the cavity volume is managed by using winged-edge and frame-based data structures. Machining features are extracted using two methods : (1) automatic feature recognition, and (2) machine learning of features for subsequent recognition. [Continues.