Resource selection and route generation in discrete manufacturing environment

When put to various sources, the question of which sequence of operations and machines is best for producing a particular component will often receive a wide range of answers. When the factors of optimum cutting conditions, minimum time, minimum cost, and uniform equipment utilisation are added to the equation, the range of answers becomes even more extensive. Many of these answers will be 'correct', however only one can be the best or optimum solution. When a process planner chooses a route and the accompanying machining conditions for a job, he will often rely on his experience to make the choice. Clearly, a manual generation of routes does not take all the important considerations into account. The planner may not be aware of all the factors and routes available to him. A large workshop might have hundreds of possible routes, even if he did know it all', he will never be able to go through all the routes and calculate accurately which is the most suitable for each process - to do this, something faster is required. This thesis describes the design and implementation of an Intelligent Route Generator. The aim is to provide the planner with accurate calculations of all possible production routes m a factory. This will lead up to the selection of an optimum solution according to minimum cost and time. The ultimate goal will be the generation of fast decisions based on expert information. Background knowledge of machining processes and machine tools was initially required, followed by an identification of the role of the knowledge base and the database within the system. An expert system builder. Crystal, and a database software package, DBase III Plus, were chosen for the project. Recommendations for possible expansion of and improvements to the expert system have been suggested for future development

A distributed decision support system for turning and milling operations using the internet

The machine tool industry is highly dependent on the tooling which is needed to machine the components used to make the range of products seen in today's society. The range of tooling available to machinists is prolific and subject to continual growth. Those engineers faced with the task of process planning require advanced systems to support the decisions that need to be made for the production process to operate smoothly. The tooling data made available by these systems is a key factor in defining the efficiency with which the production processes can be carried out. This research examines the technical decision support systems made available to industrialists and highlights the scope to provide tooling engineers with up-to-date tooling performance and use data that can be used both in the planning stages as well as dealing with problems encountered during production. Specifically, this research identifies the role performed by widespread tool trials, associated with new tools or new materials, and goes on to show how the information obtained from tool trials can be collated in a structured manner and used to enhance the provision of data with which to carry out the process planning task. The goal of this research was to develop and implement a framework capable of collecting and disseminating data related to tool trials in a coherent and supportive fashion using distributed methods. This target resulted in the deployment of a system named JadeT, which is capable of receiving and analysing data from tool trials and subsequently enhancing the process planning task by basing cutting parameter selection on a combination of fundamental cutting parameter algorithms in parallel with using the approved data generated from tool trials. The JadeT system was tested via the creation of a database using actual tool trial reports, and the manner in which this data was used to provide cutting parameters was analysed. The JadeT system has been developed, deployed and evaluated. The opportunity to use data contained within tool trial reports to support process planning tasks has been identified and exploited. The testing of JadeT indicates that the system fulfils the initial goals and was able to provide suggestions for further research in this area

Application of ”ART SIMULATOR” for Manufacturing Similarity Identification in Group Technology Design - Chapter 10

This chapter 10 carried out the exceptional implementation of ART-1 neural network in the analysis of the manufacturing similarity of the cylindrical parts within the group technology design. Established concept of the group technology design begins from the complex part of the group or the group representative. Group representative has all the geometrical elements of the parts in group, and manufacturing procedure may be applied to the machining of any part in the group. The complex part may be realistic or a hypothetical one. The ART-1 artificial neural network provided manufacturing classification according to the geometrical similarities of work-pieces for the group of cylindrical parts. For the manufacturing similarity identification within the group technology design, software package "ART Simulator" is developed and presented in this chapter

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies

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

Machinability assessment and tool selection for milling.

SIGLEAvailable from British Library Document Supply Centre-DSC:DX204223 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Cutting data for automated turning tool selection in industry

This thesis is concerned with the determination of cutting parameters (cutting speed, feed rate and depth of cut) in turning operations within an industrial environment. The parameters are required for the purposes of tool selection, working with a variety of batches of different materials. Previous work of this nature, little of which has been transferred into industry, has concentrated primarily on deriving optimum cutting conditions, based on a variety of deterministic and non- deterministic approaches, with a general reliance on experimentally-derived input variables. However, this work is only suited to tool selection for a single material. Under industrial conditions tools will frequently need to be selected for more than one material, in tool/material combinations not recommended by tool makers. Consequently, the objective of the research described in this thesis was to employ existing cutting data technology and to use it as the basis for a cutting data system, suitable for multi-batch tool selection. Two companies collaborated in this work, by making available suitable personnel and the provision of shop floor facilities on their premises. The initial work concentrated on the development of an algorithmic model, based on established theory. This was then tested industrially, using the concept of shop floor approved data as a substitute for optimum cutting data. The model was found to work reasonably, but required further development to make it suitable for multi- batch tool selection. This development took three main forms: a) a reduction of input data, particularly in the number of experimentally-derived variables, b) the removal of the tool/material-specific constraints traditionally used in cutting data optimisation, c) a method of data correction based on adjustment of the mean and standard deviation of the data. Further industrial testing was carried out using the resulting system. It was demonstrated that it was possible for a relaxed system with reduced input variables and appropriate data correction to function within an industrial environment