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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 pa...

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.

Two critical objectives in sculptured surface tool path optimization are machining accuracy and process efficiency. The former objective is characterized as the combined effect of chord error and scallop height, known as surface machining error, whilst the latter may be reflected by the number of cutting tool locations constituting the tool path. These objectives are entirely depended on the values to be selected for computing tool paths under a given cutting strategy and preset tolerance. In order to determine optimal tool path parameters that will simultaneously satisfy the trade-off incurred between these objectives, a novel; generic and unbiased environment integrated with a virus-evolutionary heuristic for intelligent tool path optimization is presented. The proposed environment has been developed using the open architecture of a cutting edge CAM system whilst it deploys a set of interactive functions to straightforwardly assess criteria without the formal knowledge of any objective function; but directly from computer-aided manufacturing attributes; fully responsible to formulate efficient tool paths. A utility based on weighted summation for multi-objective optimization has been introduced to capture the direct output of globally optimized tool paths avoiding this way problem oversimplification and statistical errors that mathematical relations involve. Results have been rigorously validated both computationally and experimentally with the aid of a benchmark sculptured part that has been previously tested by several noticeable research contributions. Based on the quality of research outputs it is shown that the proposed framework for optimizing sculptured surface CNC tool paths may gain a prominent role for further extending the capabilities of current industrial strategies and be the flagship of allied; not-yet industrially interfaced approaches for deploying similar software integration tools to transfer significant results to production.

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.

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 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.

The optimization of process parameters referring to sculptured surface tool path planning increases efficiency and enhances product quality; thus, it is for the major research subject for many noticeable studies. Optimization for process parameters is usually conducted by working with a two-phase scheme; regression modeling based on the results obtained by a design of experiments, and optimization by employing an intelligent algorithm. Currently, new artificial algorithms have been developed and deployed to address different kinds of problems in engineering. In the present work, six new intelligent algorithms have been tested to sculptured surface tool path optimization problems, namely particle swarm optimization (PSO), invasive weed optimization (IWO), shuffled frog-leaping algorithm (SFLA), shuffled complex evolution (SCE), teaching–learning-based optimization (TLBO), and virus-evolutionary genetic algorithm (VGA). Except from the VGA which has been developed from scratch, the rest of the algorithms have been adopted from the literature whilst the case studies the algorithms are applied to have been established using design of machining simulation experiments on benchmark sculptured surfaces. The results obtained from case studies are compared with each other to investigate the capabilities of the aforementioned algorithms in terms of their application to the sculptured surface machining problem.

To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality.

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.

This paper investigates the profitability of a dual-curve driven surface finish tool path under the concept of optimizing crucial machining parameters such as toroidal end-mill diameter, lead angle and tilt angle. Surface machining error as well as tool path time are treated as optimization objectives under a multi-criteria sense, whilst a central composite design is conducted to obtain experimental outputs for examination and, finally, fit a full quadratic model considered as the fitness function for process optimization by means of a genetic algorithm. A benchmark sculptured surface given as a second-order parametric equation was tested and simulated using a cutting-edge manufacturing modeling software and best parameters recommended by the genetic algorithm were implemented for validation. Further assessment involves the virtual inspection to selected profile sections on the part. It was shown that the approach can produce dual-curve driven tool trajectories capable of eliminating sharp scallop heights, maximizing machining strip widths as well as maintaining smoothness quality and machining efficiency.

Unlike materials subtractive technologies, additive manufacturing (AM) works on producing near-net-shape components according to a specific design at which the synthesis is achieved layer by layer. Additive manufacturing allows design freedom, making design-driven manufacturing a reality. However, its poor surface quality is considered as one of the key challenges that are worth to overcome. The main objective of this chapter is to report a comprehensive overview of the techniques used to improve the surface finish and their advancements of products made by metal additive manufacturing (AM) technologies and to highlight experimental processes and data. Powder bed fusion (PBF) and direct laser deposition (DLD) are the main processes covered in this review. The chapter starts with the literature review and introduction to the main metal AM processes and their surface roughness limitations, the effect of their parameters and the effect of the laser re-melting on the surface quality. Next, it is followed by a number of surface finishing techniques such as laser polishing, chemical and electropolishing. Experimental results of post-surface finishing of AM parts by microelectrical discharge machining are also presented.

In this paper the failure behaviour of natural stitched composite materials in the skin-stiffener of wind turbine blade structures are investigated. For this study, the laminated composite beams were stitched using Flax yarns before curing process. Two stiffener structures of T-beam and Box-beam are studied in this paper. These specimens were tested under quasi-static loading condition to compare the failure resistance of adhesive and stitched bonding methods. Furthermore, the cohesive zone modelling (CZM) which is known as a variation in the cohesive stresses with the interfacial opening displacement along the localised fracture process zone is used to predict bonding failure in the skin-stiffener of wind turbine blade structures.

The paper presents the results of experimental work aimed at studying the effect of porous layer parameters on the thermal performance of water evaporators. The results show that there are optimum particle sizes that are associated with maximum boiling heat rates and heat transfer coefficients. The results also show that the layer thickness has monotonic effect on the evaporators’ performance but its impact is less pronounced compared to that of the particle size. The trends are explained in terms of the contradicting effects that exist within the layer. The research also showed that the effect of the heat load, represented by temperature and velocity of the load air, was negligible. Boiling heat transfer coefficients of up to 5 kW/m2 °C were achieved.

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.

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.

Sculptured surface machining (SSM) is an operation widely applied to several industrial fields such as aerospace, automotive and mold/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. To properly reduce the number of parameters, design of experiments (DOE) methodology along with statistical analysis can be adopted. In this paper DOE and respective analysis were used to conduct machining experiments with the use of a computer aided manufacturing (CAM) software. Major goal is to investigate which of process parameters are worthy of optimization through intelligent systems. Two scenarios were considered to machine a sculptured part; one involving 3-axis roughing/3-axis finish machining experiments and 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 conducted to minimize machining time (t mr ) and remaining volume (v r ); whilst finishing experiments were targeted to minimize machining time (t mf ) and surface deviation (s dev ) 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 numerical control (NC) programmers to have a clear understanding about the influence of process parameters for sculptured surface machining operations generating thus efficient tool paths to improve productivity, part quality and process efficiency. Practically the work contributes to machining improvement through the proposition of machining experimentation methods using safe and useful platforms such as CAM systems; the application of techniques 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 from CAM software.

This paper outlines the integration of a two-dimensional vision system with a pneumatic proximity-to-tactile sensing device to form a Co-ordinated ‘Hand-Eye’ system. With the aid of a knowledge base this system is utilised as an intelligent condition monitoring tool for recognition and detection of orientation of parts in a flexible manufacturing environment. An expert system is formulated to interrogate the acquired data streams for the purpose of comparative studies with the knowledge base. Appropriate data processing methods are employed to ensure rapid manipulation of data for real-time applications.