Five-Axis Numerical Control Machining of the Tooth Flank of a Logarithmic Spiral Bevel Gear Pinion

In this paper, the production of a logarithmic spiral bevel gear prototype is illustrated by the manufacture of the gear pinion. Firstly, the conical gear body of a logarithmic spiral bevel gear pinion was shaped on a C6140A1 lathe. A kinematic model of a five-axis vertical machining centre DMG DMU40 monoBLOCK, with the position and orientation of each axis relative to the movement of the workpiece, was created. In addition, the processing coordinate transformation formula between the workpiece coordinate system and the cutter coordinate system was devised. The cutter location file was converted to the numerical control code of the DMG DMU40 monoBLOCK. Finally, the pinion of a logarithmic spiral bevel gear was machined on the DMG DMU40 monoBLOCK as a prototype to be used in further research of the logarithmic spiral bevel gear

Evolutionary approaches to optimisation in rough machining

This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation’ is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops