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Tzivelekis C.A.,Northumbria University | Yiotis L.S.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Krimpenis A.A.,Technological Educational Institute of Central Greece TEICG
International Journal on Interactive Design and Manufacturing | Year: 2015

For product lifecycle management reasons, research trends impose the need of automated engineering tasks, such as computer-aided design and manufacturing. This paper proposes a novel approach of automating both the design and manufacturing processes of impeller-type geometries, when CAD/CAM technology is employed. To do so, a newly developed application was built; exploiting application programming interface objects of parametric instances, in order to automate time-consuming repetitive tasks for the preparation of 3D models and their direct manufacturing process. The developed application incorporates Simpson’s method, Bezier-Bernstein equation and Non-Uniform Rational B-Spline for curve approximation describing blades of centrifugal impellers, as a representative case study. The machining technology is that of 3-axis CNC, thereby; each curve extends along a constant x-y plane. In the first step of the application, the entire 3D model of the impeller-type model is automatically generated according to variable values taken as user-defined entities from the interface. The application then carries on by automatically modeling the manufacturing process and ultimately generating the NC program from the cutter location data for a given CNC machine tool. © 2015 Springer-Verlag France


Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Vaxevanidis N.M.,University of Piraeus | Stergiou C.I.,Kingston University | Benhadj-Djilali R.,Kingston University
Key Engineering Materials | Year: 2016

Industrial parts with sculptured surfaces are typically, manufactured with the use of CNC machining technology and CAM software to generate surface tool paths. To assess tool paths computed for 3- and 5-axis machining, the machining error is evaluated in advance referring to the parameter controlling the linearization of high-order curves, as well as the scallop yielded as a function of radial cutting engagement parameter. The two parameters responsible for the machining error are modeled and corresponding cutter location data for tool paths are utilized to compare actual trajectories with theoretical curves on a sculptured surface (SS) assessing thus the deviation when virtual tools are employed to maintain low cost; whilst ensuring high precision cutting. This operation is supported by applying a flexible automation code capable of computing the tool path; extracting its CL data; importing them to the CAD part and finally projecting them onto the part's surface. For a given tolerance, heights from projected instances are computed for tool paths created by changing the parameters under a cutting strategy, towards the identification of the optimum tool path. To represent a global solution rough machining is also discussed prior to finish machining where the new proposals are mainly applied. © 2016 Trans Tech Publications, Switzerland.


Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Fountas N.A.,Kingston University | Vaxevanidis N.M.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Stergiou C.I.,University of Piraeus | Benhadj-Djilali R.,Kingston University
Engineering with Computers | Year: 2016

Sculptured surface machining is a material removal operation essentially adopted to manufacture complex products. Computing optimal tool paths with reference to ideally designed CAD models is indispensable to be able to suggest machining improvements in terms of high quality and productivity. The present paper proposes a new methodology based on a virus-evolutionary genetic algorithm for enhancing sculptured surface tool path planning through an automated selection of values for standard 5-axis end milling strategies’ machining parameters to be decided upon in the context of a simulation-based; software-integrated, multi-objective optimization problem. The problem involves surface machining error as the first quality objective represented via the mean value of chordal deviations that tool path interpolation yields and effective radius of inclined tools that affects scallop. Machining time is the second quality objective entering the problem to assess productivity; whereas the number of cutter location points created for each tool path evaluation is also considered. Tool type, tool axis inclination angles as well as longitudinal and transversal steps are considered as the independent parameters in the case of 5-axis machining. Results obtained by conducting evaluation experiments and simulation tests accompanied by an actual machining process provided significant insight concerning the methodology’s efficiency and ability of suggesting practically viable results. © 2016 Springer-Verlag London


Vaxevanidis N.M.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Kechagias J.D.,Technological Educational Institute of Larissa | Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Manolakos D.E.,National Technical University of Athens
Open Construction and Building Technology Journal | Year: 2014

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. © Vaxevanidis et al.


Vaxevanidis N.M.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Kechagias J.D.,Technological Educational Institute of Larissa | Manolakos D.E.,National Technical University of Athens
OPT-i 2014 - 1st International Conference on Engineering and Applied Sciences Optimization, Proceedings | Year: 2014

The present paper investigates the influence of main cutting parameters on process performance during longitudinal turning of Ti-6Al-4V alloy. Although Ti-6Al-4V is widely used in the aerospace, medical and automotive industries for its superior properties, its application is limited by tool wear and chattering, phenomena that result in significant increase of manufacturing costs. By suitably selecting cutting conditions, machinability can be improved, leading thus to lower machining costs. In the series of experiments reported, the selected operational parameters were the spindle speed (n), the feed rate (s) and the depth of cut (a). The outputs were the main cutting force (Fz) and the centre line average (mean) surface roughness (Ra). A Ti-6Al-4V solid bar was used as a test specimen whilst a cemented carbide cutting tool was used to conduct the series of turning experiments. Twenty seven runs were conducted, according to L27 orthogonal array. The results were analyzed using analysis of variance. Interaction charts were produced showing strong interactions between process parameters. Finally, a feed-forward back-propagation neural network was developed to simulate the data. The results obtained indicate that the approach can be utilized to predictand optimize Fz and Ra; thus facilitating decision making during process planning since costly and time-consuming experimentation is avoided.


Fountas N.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Fountas N.,Kingston University | Vaxevanidis N.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Stergiou C.,Technological Educational Institute of Piraeus | Benhadj-Djilali R.,Kingston University
International Journal of Computer Integrated Manufacturing | Year: 2015

Sculptured surface machining (SSM) is an operation widely applied to several industrial fields such as aerospace, automotive and mould/die. The number of the parameters and strategies involved to program such machining operations can be enormously large owing to surface complexity and advanced design features. This study focuses on the examination of machining strategies and related parameters for the assessment of roughing and finishing stages. A fractional factorial design implementing an L27 Taguchi orthogonal array (OA) was established to conduct machining experiments with the use of a computer-aided manufacturing (CAM) software. Fractional factorial design specifics involve the statistical elimination of unimportant parameters, thus reducing experimental runs without the loss of useful information. Two scenarios were considered to machine a sculptured part; one involving 3-axis roughing/3-axis finish machining experiments and the other one involving 3-axis roughing/5-axis finish machining experiments. Roughing operation was common for both scenarios. The problem was subjected to discrete technological constraints to reflect the actual industrial status. For each machining phase, two quality objectives reflecting productivity and part quality were determined. Roughing experiments were tested to minimise machining time and remaining volume, whilst finishing experiments were subjected to minimise machining time and surface deviation between the designed and the machined 3D model. Quality characteristics were properly weighted to formulate a single objective criterion for both machining phases. Results indicated that DOE applied to CAM software, enables NC programmers to have a clear understanding about the influence of process parameters for SSM operations, thus generating efficient toolpaths to improve productivity, part quality and process efficiency. Practically the work contributes to machining improvement by through the proposition of machining experimentation methods using safe and useful platforms such as CAM systems; the investigation of approaches to avoid problem oversimplification mainly when large number of machining parameters should be exploited and the evaluation of quality criteria which allow their assessment directly form CAM software. © 2014 Taylor & Francis.


Fountas N.A.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Zalimidis P.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT | Vaxevanidis N.M.,Laboratory of Manufacturing Processes and Machine Tools LMProMaT
International Journal of Machining and Machinability of Materials | Year: 2015

This paper proposes a simple philosophy of machining 2D spiraltype sculptured surfaces by analysing a given mathematical expression which describes such surfaces and then generating a shape interpolator via the implementation of already known numerical techniques for CNC machining such as linear and circular approximation. An algorithm discretises the ideal curve of a given surface so as to maintain the lowest possible value in terms of chordal deviation existed between the theoretical curve and the real tool path trajectory. The algorithm is applied to 2D geometries only and is going to be developed in the near future. Nevertheless, experiments conducted in CAM software simulations to approximate the curve of a centrifugal impeller's blade as a paradigm for three axis machining, have show promising directions for building an entire software-based optimisation system integrated to cutting edge manufacturing systems. Copyright © 2015 Inderscience Enterprises Ltd.

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