Computer aided process planning for multi-axis CNC machining using feature free polygonal CAD models

This dissertation provides new methods for the general area of Computer Aided Process Planning, often referred to as CAPP. It specifically focuses on 3 challenging problems in the area of multi-axis CNC machining process using feature free polygonal CAD models. The first research problem involves a new method for the rapid machining of Multi-Surface Parts. These types of parts typically have different requirements for each surface, for example, surface finish, accuracy, or functionality. The CAPP algorithms developed for this problem ensure the complete rapid machining of multi surface parts by providing better setup orientations to machine each surface. The second research problem is related to a new method for discrete multi-axis CNC machining of part models using feature free polygonal CAD models. This problem specifically considers a generic 3-axis CNC machining process for which CAPP algorithms are developed. These algorithms allow the rapid machining of a wide variety of parts with higher geometric accuracy by enabling access to visible surfaces through the choice of appropriate machine tool configurations (i.e. number of axes). The third research problem addresses challenges with geometric singularities that can occur when 2D slice models are used in process planning. The conversion from CAD to slice model results in the loss of model surface information, the consequence of which could be suboptimal or incorrect process planning. The algorithms developed here facilitate transfer of complete surface geometry information from CAD to slice models. The work of this dissertation will aid in developing the next generation of CAPP tools and result in lower cost and more accurately machined components

Network part program approach based on the STEP-NC data structure for the machining of multiple fixture pallets

partially_open4noThe adoption of alternative process plans, that is, process plans that include alternative ways of machining a workpiece, can improve system performance through a better management of resource availability. Unfortunately even if this opportunity is deeply analysed in literature, it is not frequently adopted in real manufacturing practice. In order to fill this gap, this article presents the network part program (NPP) approach for the machining of multiple fixture pallets. The NPP approach is based on the STEP-NC data structure which supports nonlinear sequences of operations and process flexibility. In the NPP approach, a machining system supervisor defines the machining sequences and generates the related part programs just before the execution of the pallet. This article provides an approach with high scientific value and industrial applicability based on the integration of new and existing process planning methods. A real industrial case study is considered in order to show that in real applications the final quality is unaffected by the change of the sequence of the operations due to the employment of nonlinear process plans. Since the results appear very encouraging, the proposed approach is a possible solution to accelerate the adoption of nonlinear process planning in real manufacturing practice.S. Borgia; S. Pellegrinelli; S. Petro'; T. TolioBorgia, Stefano; Pellegrinelli, Stefania; Petro', Stefano; Tolio, TULLIO ANTONIO MARI

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government

Feasibility analysis of using special purpose machines for drilling-related operations

This work focuses on special purpose machine tools (SPMs), providing a modular platform for performing drilling-related operations. One of the main challenges in using SPMs is selecting the most appropriate machine tool among many alternatives. This thesis introduces a feasibility analysis procedure developed to support decision-making through the assessment of the strengths and limitations of SPMs. To achieve this, technical and economic feasibility analyses, a sensitivity analysis, and an optimisation model were developed and a case study was provided for each analysis. The results indicated that although technical feasibility analysis leads decision-makers to select a feasible machine tool, complementary analyses are required for making an informed decision and improving profitability. Accordingly, a mathematical cost model was developed to perform economic and sensitivity analyses and investigate the profitability of any selected SPM configuration. In addition, an optimisation procedure was applied to the cost model in order to investigate the effect of process parameters and the SPM configuration on the decision-making. Finally, the developed analyses were then integrated into a model in a proper sequence that can evaluate whether the SPM is appropriate for producing the given part and achieving higher productivity. To validate this integrated model three different case studies were presented and results were discussed. The results showed that the developed model is a very useful tool in assisting manufacturers to evaluate the performance of SPMs in comparison with other alternatives considered from different perspectives

Optimization of multi-holes drilling path using particle swarm optimization

Multi-hole drilling is a manufacturing process that is commonly used in industries. In this process, the tool movement and switching, on average, take 70% of the total machining time. There are many applications of multi-hole drilling, such as in mould, die-making and printed circuit board (PCB). One way to improve the multi-hole drilling is by optimising the tool path in the process. This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. The study begins by modelling the multi-hole drilling problems using the Travelling Salesman Problem (TSP) concept. The objective function was set to minimise the total tool path distance. Then, the PSO was formulated to minimise total length in multi-hole drilling. The main issue in this stage was to convert the continuous encoding in PSO to permutation problems as in multi-hole drilling. For this purpose, a topological sorting procedure based on the most prominent particle rule was implemented. The algorithm was tested on 15 test problems where between 10 to 150 holes were randomly generated. The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). Then, a validation experiment was conducted by implementing the PSO generated tool path against the commercial CAD-CAM path. In this stage, the machining time was measured. The results from the computational experiment indicated that the proposed PSO algorithm came out with the best solution in 10 out of the 15 test problems. In the meantime, the validation experiment result proved that the PSO generated tool path provides faster machining time compared with the commercial CAD-CAM path by 5% on average. The results clearly showed that PSO has a great potential to be applied in the multi-hole drilling process. The findings from this research could benefit the manufacturing industry to improve their productivity using existing resources

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

Cloud manufacturing system for sheet metal processing

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.fi=vertaisarvioitu|en=peerReviewed

Determination of Cost-Effective Range in Surface Finish for Single Pass Turning

Surface finish is considered a critical characteristic for manufacturing components when manufacturers strive to produce components with high-quality characteristics predefined by design engineers. The objective of this research is to provide a cost-effective range in surface finish for single pass turning that enables the design engineers to explore a wider spectrum of alternative solutions without significantly affecting the functionality of the part. Apart from the one optimal solution, the proposed methodology, which is based on Geometric Programming, would provide a range of cutting conditions solutions that satisfy the economic and functional needs for the designer. This can be achieved by switching cost reduction focus from tooling to labor cost, particularly by adjusting variables values such as spindle speed and feed. An algorithm has been developed to find the new variables values. In addition, a sensitivity analysis model, based on metaheuristic techniques, will also be developed to further give a set of possible solutions that are practically preferable to the practitioners. In addition, the developed methodology can be applied to other engineering applications. The proposed methodology will provide a tool that enhances the design for manufacturability for companies to become more competitive

Manufacturing Technology Today

Manufacturing Technology Today, Manufacturing Technology Abstracts, Vol. 14, No. 4, September 2015, Bangalore, India