STEP based Finish Machining CAPP system

This research paper presents various methodologies developed in a STEP based Computer Aided Process Planning (CAPP) system named "Finish Machining – CAPP" (FM-CAPP). It is developed to generate automatic process plans for finish machining prismatic parts. It is designed in a modular fashion consisting of three main modules, namely (i) Feature Recognition module (FRM) (ii) Machining Planning Module (MPM) and (iii) Setup Planning Module (SPM). The FRM Module analyses the geometrical and topological information of the inputted part in STEP AP 203/AP214 formats, and generates a text file with full dimensional details of features and machinable volumes. It is then passed on to the MPM for the selection of best suited machining process. Here, the selection is based on a 7 stage elimination strategy considering major manufacturing factors. After machining planning, the task of selecting the best suited setup is implemented in the SPM module. When these tasks are completed, the system generates the process-planning sheet containing the details of feature, finish cut machinable volume, machining processes with the cutting tool/ media, process parameters and the setup required for machining

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

Machine planning in a product model environment

The aim of this research was to understand and solve problems associated with the integration of a Machine Planner within a product model environment. This work was carried out in conjunction with other researchers, pursuing parallel integration issues related to pre-production proving and product data representation. Product data representations of component level planned, processes and feature level process data have been explored as sub-sets of -a product data model to aid integration. Geometric queries on a cell decomposition solid, model. have been explored as a means of providing feature geometric interaction data, while the dimensional interactions between features have also been addressed. Product data representations have been modelled using a prototype software tool, providing an environment for the exploration of the integration of a Machine Planner using a feature based design approach. Necessary Machine Planning functions have been implemented, using the ADA programming language, to explore the integrating capability of the product model environment, concentrating on the use of a prismatic benchmark component. Using the experimental implementation, setup and operation plans have been produced and machining part programs generated from product model representations of variants on the benchmark component. These have been successfully machined using a3 axis vertical machining centre. Such experiments, as well as others in conjunction with co-researchers, have shown that a product data model can provide a common base of data for the integration of a range of design and manufacturing activities

Feature technology and its applications in computer integrated manufacturing

A Thesis submitted for the degree of Doctor of Philosophy of University of LutonComputer aided design and manufacturing (CAD/CAM) has been a focal research area for the manufacturing industry. Genuine CAD/CAM integration is necessary to make products of higher quality with lower cost and shorter lead times. Although CAD and CAM have been extensively used in industry, effective CAD/CAM integration has not been implemented. The major obstacles of CAD/CAM integration are the representation of design and process knowledge and the adaptive ability of computer aided process planning (CAPP). This research is aimed to develop a feature-based CAD/CAM integration methodology. Artificial intelligent techniques such as neural networks, heuristic algorithms, genetic algorithms and fuzzy logics are used to tackle problems. The activities considered include: 1) Component design based on a number of standard feature classes with validity check. A feature classification for machining application is defined adopting ISO 10303-STEP AP224 from a multi-viewpoint of design and manufacture. 2) Search of interacting features and identification of features relationships. A heuristic algorithm has been proposed in order to resolve interacting features. The algorithm analyses the interacting entity between each feature pair, making the process simpler and more efficient. 3) Recognition of new features formed by interacting features. A novel neural network-based technique for feature recognition has been designed, which solves the problems of ambiguity and overlaps. 4) Production of a feature based model for the component. 5) Generation of a suitable process plan covering selection of machining operations, grouping of machining operations and process sequencing. A hybrid feature-based CAPP has been developed using neural network, genetic algorithm and fuzzy evaluating techniques

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market

Integrated process planning and scheduling for common prismatic parts in a 5-axis CNC environment

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Feature based workshop oriented NC planning for asymmetric rotational parts

This thesis describes research which is aimed at devising a framework for a feature based workshop oriented NC planning. The principal objective of this thesis is to utilize a feature based method which can rationalize and enhance part description and in particular part planning and programming on the shop-floor. This work has been done taking into account new developments in the area of shop floor programming. The importance of the techniques and conventions which are addressed in this thesis stems from the recognition that the most effective way to improve and enhance part description is to capture the intent of the engineering drawing by devising a medium in which the recurring patterns of turned components can be modelled for machining. Experimental application software which allows the user to describe the workpiece and subsequently generate the manufacturing code has been realized

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

Knowledge Capture in CMM Inspection Planning: Barriers and Challenges

Coordinate Measuring Machines (CMM) have been widely used as a means of evaluating product quality and controlling quality manufacturing processes. Many techniques have been developed to facilitate the generation of CMM measurement plans. However, there are major gaps in the understanding of planning such strategies. This significant lack of explicitly available knowledge on how experts prepare plans and carry out measurements slows down the planning process, leading to the repetitive reinvention of new plans while preventing the automation or even semi-automation of the process. The objectives of this paper are twofold: (i) to provide a review of the existing inspection planning systems and discuss the barriers and challenges, especially from the aspect of knowledge capture and formalization; and (ii) to propose and demonstrate a novel digital engineering mixed reality paradigm which has the potential to facilitate the rapid capture of implicit inspection knowledge and explicitly represent this in a formalized way. An outline and the results of the development of an early stage prototype - which will form the foundation of a more complex system to address the aforementioned technological challenges identified in the literature survey - will be given