Nikolaos A. Fountas

Single and Multi-objective Optimization Methodologies in CNC Machining

Aiming to optimize both productivity and quality in modern manufacturing industries, a wide range of optimization techniques and strategies has been presented by numerous researchers. Optimization modules such as Genetic Algorithms, Evolutionary Algorithms and Fuzzy systems are capable of exploiting manufacturing data with high efficiency and reliability, in order to provide optimal sets of solutions for machining processes. The main scope of this chapter is to present the fundamentals in formulating and developing optimization methodologies, which ultimately offer optimal cutting conditions for both prismatic and sculptured surface part machining and actually improve industrial practice.

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (Ra) and main cutting force component (FC) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.

Prediction of Cutting Forces during Turning PA66 GF-30 Glass Fiber Reinforced Polyamide by Soft Computing Techniques

In the present paper the influence of the main cutting parameters on process performance during longitudinal turning of PA66 GF-30 Glass Fiber Reinforced Polyamide is investigated. The selected cutting parameters are cutting speed and feed-rate whilst depth of cut is kept constant. As outputs (responses), cutting force components Ft, FV and Fr were selected. Test specimens in the form of round bars and cemented carbide cutting tool were used during the experimental process. Fifteen experiments were conducted having all different combinations of cutting parameter values. Analysis of Variance (ANOVA), statistical approaches and soft computing techniques (artificial neural network) were applied in order to formulate stochastic models for relating the responses with main cutting parameters. The results obtained, indicate that the proposed soft computing techniques can be effectively used to predict the cutting force components (Ft, FV and Fr) thus; facilitating decision making during process planning since costly and time-consuming experimentation can be avoided.

Intelligent CNC Tool Path Optimization for Sculptured Surface Machining Through a Virus-Evolutionary Genetic Algorithm

Priorities for manufacturers worldwide include their attempt towards optimizing modern manufacturing systems to satisfy the needs of their customers. Major goal of the proposed study is to present a novel optimization methodology based on Artificial Intelligence using the Virus Theory of Evolution. The methodology implements a Virus-Evolutionary Genetic Algorithm to undertake sculptured surface tool path optimization in terms of geometrical machining error to reflect part quality and machining time to reflect productivity for both 3- and 5-axis sculptured surface machining. The algorithm implements its virus operators to create efficient solution representations, to rabidly reproduce enhanced schemata during the evaluations’ loops, and finally come up with the optimum machining parameters based on the available resources and constraints ought to be imposed. Through a fully automated environment, time-consuming activities and repetitive tasks are no more of the CNC programmers’ concern since the algorithm handles the CAM system’s routines to handle them for its own benefit. The proposed methodology is deemed capable of providing uniform tool paths with low geometric machining error distribution as well as high productivity rates to the best possible extent.

Optimizing 5-Axis Sculptured Surface Finish Machining Through Design of Experiments and Neural Networks

Five axis machining and CAM software play key role to new manufacturing trends. Towards this direction, a series of 5 axis machining experiments were conducted in CAM environment to simulate operations and collect results for quality objectives. The experiments were designed using an L27 orthogonal array addressing four machining parameters namely tool type, stepover, lead angle and tilt angle (tool inclination angles). Resulting outputs from the experiments were used for the training and testing of a feed-forward, back-propagation neural network (FFBP-NN) towards the effort of optimizing surface deviation and machining time as quality objectives. The selected ANN inputs were the aforementioned machining parameters. The outputs were the surface deviation (SD) and machining time (tm). Experimental results were utilized to train, validate and test the ANN. Major goal is to provide results robust enough to predict optimal values for quality objectives, thus; support decision making and accurate machining modelling.

Programmable Support Functions to Assist Process Planning for Aeronautical Parts Manufacturing

Despite the fast development and the continuous evolution of computer-aided systems for product design, analysis and manufacturing, an unlinked gap appears between the interfaces of computer-aided design (CAD) and computer-aided process planning (CAPP) modules. Various CAPP systems have been built to address this problem and forward a “passage” to link the design phase and the planning of manufacturing processes; hence, providing precise technical instructions in the shop-floor. To support the manufacturing trends and contribute to the research efforts for the realization of precise, reliable and efficient process plans, a set of programmable support functions are presented in the form of an object-oriented software application that enable process planners to produce accurate process plans for aircraft parts and components.Copyright

Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness

Global optimisation for manufacturing problems is mandatory for obtaining versatile benefits to facilitate modern industry. This paper deals with an original approach of globally optimising tool paths to CNC-machine sculptured surfaces. The approach entails the development of a fully automated manufacturing software interface integrated by a non-conventional genetic/evolutionary algorithm to enable intelligent machining. These attributes have been built using already existing practical machining modelling tools such as CAM systems so as to deliver a truly viable computer-aided manufacturing solution. Since global optimisation is heavily based on the formulation of the problem, emphasis has been given to the definition of optimisation criteria as crucial elements for representing performance. The criteria involve the machining error as a combined effect of chord error and scallop height, the tool path smoothness and productivity. Experiments have been designed considering several benchmark sculptured surfaces as well as tool path parameters to validate the aforementioned criteria. The new approach was implemented to another sculptured surface which has been extensively tested by previous research works. Results were compared to those available in the literature and it was found that the proposed approach can indeed constitute a promising and trustworthy technique for the global optimisation of sculptured surface CNC tool paths.

A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining

This paper focuses on the development of a multi-axis post-processor engine with a curvature-based feed adaptation module, capable of extracting generic CNC data for high precision machining. The motivation of this work stems from the drawback of standard and commercial post-processors to modify their internal source codes so as to be implemented to newly-developed functions which integrate modern CNC units. The multi-axis post-processor proposed in this work operates as a stand-alone function of an artificial intelligent module that optimizes machining parameters for standard swept cut multi-axis surface tool-paths. The post-processor developed receives APT source files previously been optimized by means of a genetic algorithm that handles cutting tool selection; radial cut engagement; maximum discretization step; lead and tilt angles. The algorithm optimizes the aforementioned machining parameters towards the minimization of the number of cutter locations found in a specific APT source file as well as the surface machining error as a combined effect of chordal deviation and scallop height. The final APT output is then properly handled by the post-processor engine so as to extract the final ISO code for a double-pivoted head 5-axis CNC machine and compute optimal values for feed rate in each NC block considering the interpolation error and curvature analysis given the surface properties. To simulate and verify our proposals, the MAZAK Vortex 1000 gantry-type 5-axis CNC machine tool equipped with a Fanuc 15i CNC unit has been selected as the manufacturing resource corresponding to the final CNC output that the proposed post-processor computes. A benchmark sculptured part is created and used for the virtual material removal simulation in CATIA® V5 R18. For that part, both the proposed post-processor engine and a commercially available post-processor were employed to extract G-code data whilst it was shown that identical outputs were obtained.

A Multi-parameter Experimental and Statistical Analysis of Surface Texture in Turning of a New Aluminum Matrix Steel Particulate Composite

Metal matrix composites (MMCs) represent a new generation of engineering materials in which a strong reinforcement is incorporated into a metal matrix to improve its properties including specific strength, specific stiffness, wear resistance, corrosion resistance and elastic modulus. Aluminum matrix composites (AMCs), a specific type of MMCs, are rapidly replacing conventional materials in various engineering applications, especially in the aerospace and automobile industries due to their attractive properties. From the literature already published it is evident that the machining of AMCs is an important area of research, but only very few if any studies have been carried out using metal particles reinforced AMCs. A multi-parameter analysis of surface finish imparted by turning to a new L316 stainless steel flake-reinforced aluminum matrix composite is presented. Surface finish is investigated by examining a number of surface texture parameters. Spindle speed as well as feed rate was treated as the independent variables under a constant depth of cut whilst roughness parameters were considered as the responses under an L9 orthogonal array experimental design. ANOVA analysis was also conducted to study the effect of the two cutting variables on the surface texture responses.

Optimizing CNC wood milling operations with the use of genetic algorithms on CAM software

Abstract Main focus of this paper is to introduce an appropriate methodology and create a Genetic Algorithm that optimizes wood milling operations, since wood is one of the primary materials used in both structures and musical instruments building. Mechanical properties of wood were considered in the cutting process modeling on CAM software as far as machining parameter values are concerned. Key machining parameters are explained along with their effect on the final product. The proposed Genetic Algorithm was specifically built for wood milling applications and its operators are examined in detail. The undertaken quality characteristics were minimum machining time and optimal surface quality, thus covering both productivity and quality goals at the same time. As a case study, a novel custom 3D CAD model of an electric guitar body was introduced, on which the proposed optimization methodology was implemented, so as to reveal its efficiency and effectiveness.