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

Fountas, Nikolaos A. (2019) A virus-evolutionary, multi-objective intelligent tool path optimisation methodology for sculptured surface CNC machining. (PhD thesis), Kingston University, .


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.

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