Process planning for an Additive/Subtractive Rapid Pattern Manufacturing system

This dissertation presents a rapid manufacturing process for sand casting patterns using a hybrid additive/subtractive approach. This includes three major areas of research that will enable highly automated process planning; a critical need for a rapid methodology. The first research area yields a model for automatically determining the locations of layers, given the slab height, material types and part geometry. Layers are chosen such that it will avoid catastrophic failures and poor machining conditions in general. First, features that are possible thin material machining positions are defined, and methods for detecting these feature positions from an STL model are studied. Next, a layer thickness calculation model is presented according to positions of these features. The second area focuses on tools and parameters for the subtractive side of processing each layer. A tool size and machining parameter selection model is presented that can automatically select tool sizes and machining parameters, given layer thickness, part geometry, and material types. Machining strategies and related machining parameters are studied first. Then the method for Stepdown parameter calculation is presented. Finally, an algorithm based on both accessibility and machining efficiency is proposed for the selection of tool sizes for the rough cutting operation, finish cutting operation and optional semi-rough cutting operation. The final research area focuses on a cutting force analysis for thin material machining with additional layer thickness & tool size interaction. Popular cutting force models are reviewed, and a suitable model for cutting force calculation in this process is evaluated. Then, a cantilever beam model is used to analyze the thin material machining failure problem, and a minimum layer thickness model is presented. Third, a combined layer thickness & tool size model is constructed based on the machining tool deflection under cutting forces. This rapid pattern manufacturing process and related software has been implemented, and experimental data is presented to illustrate the efficacy of this system and its process planning methods

Automated Tool Selection and Tool Path Planning for Free-Form Surfaces in 3-Axis CNC Milling using Highly Parallel Computing Architecture

This research presents a methodology to automatically select cutters and generate tool paths for all stages in 3-axis CNC Milling of free-form surfaces. Tools are selected and tool paths are planned in order to minimize the total machining time. A generalized cutter geometry model is used to define available cutters and an arbitrary milling surface is initially defined by a triangular mesh. The decisions made by process engineers in selecting cutting geometry and generating tool paths for milling dramatically influence the final result. Often, the resulting tool path is non-optimal, because the engineers cannot consider all the available information. However, making these decisions can be delegated to a computing system that can find a better result. The developed methodology selects the cutters to use for milling from the set of all available cutters, assigns milling zones to every selected cutter, based on its performance, and builds iso-scallop and contour parallel tool paths for every cutter and its milling zone. After generating all tool paths for both milling stages (rough milling and finishing), the tool selection sequence is defined and all the tool paths for one tool are connected into the single tool path. The tool paths should be connected in the best possible manner in order to minimize the time of CNC non-cutting motions. This is similar to the travelling salesman problem with constraints. A heuristics solution is provided here. At the end, the total machining time for one tool set is calculated. Finally, the set of cutters used is changed to minimize the total machining time. A digital, voxel-based model is used to represent a workpiece and the available tools. This model is selected so that the algorithms is simpler and they can be easily paralleled for thousands of computing cores. The parallel processing framework is implemented to work with multiple graphics processing units. Tool paths generated from this framework are post-processed into G-code and the representative part is machined

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