A virus-evolutionary, multi-objective intelligent tool path optimisation methodology for sculptured surface CNC machining

Today’s production environment faces multiple challenges involving fast adaptation to modern technologies, flexibility in accommodating them to current industrial practices and cost reduction through automating repetitive tasks. At the same time the requirements for manufacturing functional, aesthetic and versatile products, turn these challenges to clear and present industrial problems that need to be solved by delivering at least semi-optimal results. Even though sculptured surfaces can meet such requirements when it comes to product design, a critical problem exists in terms of their machining operations owing to their arbitrary nature and complex geometrical features as opposed to prismatic surfaces. Current approaches for generating tool paths in computer-aided manufacturing (CAM) systems are still based on human intervention as well as trial-and-error experiments. These approaches neither can provide optimal tool paths nor can they establish a generic approach for an advantageous and profitable sculptured surface machining (SSM). Major goal of this PhD thesis is the development of an intelligent, automated and generic methodology for generating optimal 5-axis CNC tool paths to machine complex sculptured surfaces. The methodology considers the tool path parameters “cutting tool”, “stepover”, “lead angle”, “tilt angle” and “maximum discretisation step” as the independent variables for optimisation whilst the mean machining error, its mean distribution on the sculptured surface and the minimum number of tool positions are the crucial optimisation criteria formulating the generalized multi-objective sculptured surface CNC machining optimisation problem. The methodology is a two-fold programming framework comprising a virus-evolutionary genetic algorithm as the methodology’s intelligent part for performing the multi-objective optimisation and an automation function for driving the algorithm through its argument-passing elements directly related to CAM software, i.e., tool path computation utilities, objects for programmatically retrieving tool path parameters’ inputs, etc. These two modules (the intelligent algorithm and the automation function) interact and exchange information as needed towards the achievement of creating globally optimal tool paths for any sculptured surface. The methodology has been validated through simulation experiments and actual machining operations conducted to benchmark sculptured surfaces and corresponding results have been compared to those available from already existing tool path generation/optimisation approaches in the literature. The results have proven the methodology’s practical merits as well as its effectiveness for maintaining quality and productivity in sculptured surface 5-axis CNC machining

Precision sculptured surface CNC machining using cutter location data

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.</jats:p

Experimental investigation and statistical analysis of surface roughness parameters in milling of PA66-GF30 glass-fibre reinforced polyamide

A multi-parameter analysis of surface finish imparted to PA66-GF30 glass-fibre reinforced polyamide by milling is presented. The interrelationship between surface texture parameters is emphasized. Surface finish parameters studied include arithmetic mean deviation of the assessed profile Ra; maximum height of profile, Rt; ten point height Rz; mean width of the profile elements Rsm; skewness of the assessed profile, Rsk and kurtosis of the assessed profile, Rku. The correlation of these parameters with the machining conditions was investigated. By applying analysis of variance and regression analysis to the experimental data close correlation was obtained among certain surface finish parameters and the machining conditions. To facilitate industrial operations full quadratic prediction models were developed for capturing trends for machining quality in advance

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two populations: One comprising the hosts and one comprising the viruses. Viruses act as information carriers and thus they contribute to the algorithm by boosting efficient schemata in binary coding to facilitate both the arrival at global optimal solutions and rapid convergence speed. Three cases related to abrasive flow machining have been selected from the literature to implement the algorithm, and the results corresponding to them have been compared to those available by the selected contributions. It has been verified that the results obtained by the virus-evolutionary genetic algorithm are not only practically viable, but far more promising compared to others as well. The three cases selected are the traditional “abrasive flow finishing,” the “rotating workpiece” abrasive flow finishing, and the “rotational-magnetorheological” abrasive flow finishing

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two populations: One comprising the hosts and one comprising the viruses. Viruses act as information carriers and thus they contribute to the algorithm by boosting efficient schemata in binary coding to facilitate both the arrival at global optimal solutions and rapid convergence speed. Three cases related to abrasive flow machining have been selected from the literature to implement the algorithm, and the results corresponding to them have been compared to those available by the selected contributions. It has been verified that the results obtained by the virus-evolutionary genetic algorithm are not only practically viable, but far more promising compared to others as well. The three cases selected are the traditional “abrasive flow finishing,” the “rotating workpiece” abrasive flow finishing, and the “rotational-magnetorheological” abrasive flow finishing

Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm

This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the density of Ti6Al4V specimens, with layer thickness, linear energy density, hatching space and scanning strategy as the independent process parameters. The second one refers to the formulation of a two-objective optimization problem aimed at maximizing both the hardness and tensile strength of Ti6Al4V samples, with laser power, scanning speed, hatch spacing, scan pattern angle and heat treatment temperature as the independent process parameters. Finally, the third case deals with the formulation of a three-objective optimization problem aimed at minimizing mean surface roughness, while maximizing the density and hardness of laser-melted L316 stainless steel powder. The results obtained by the proposed algorithm are statistically compared to those obtained by the Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), and dragonfly (DA) algorithms. Algorithm-specific parameters for all the algorithms including those of the virus-evolutionary genetic algorithm were examined by performing systematic response surface experiments to find the beneficial settings and perform comparisons under equal terms. The results have shown that the virus-evolutionary genetic algorithm is superior to the heuristics that were tested, at least on the basis of evaluating regression models as fitness functions

Quality research on the performance of a virus-evolutionary genetic algorithm for optimized sculptured surface CNC machining, through standard benchmarks

This paper presents experimental results on a benchmark functions set, used for performance evaluation of Heuristics. Computational quality and robustness of a Virus-Evolutionary Genetic Algorithm developed to optimize manufacturing applications is assessed by conducting experiments and adjusting its intelligent operators so that its general computational behaviour is tuned up. Parameters considered include the generation number, population size, and virus infection operators