Computer-aided manufacturing

Summary

Computer-aided manufacturing (CAM) also known as computer-aided modeling or computer-aided machining is the use of software to control machine tools in the manufacturing of work pieces. This is not the only definition for CAM, but it is the most common. It may also refer to the use of a computer to assist in all operations of a manufacturing plant, including planning, management, transportation and storage. Its primary purpose is to create a faster production process and components and tooling with more precise dimensions and material consistency, which in some cases, uses only the required amount of raw material (thus minimizing waste), while simultaneously reducing energy consumption. CAM is now a system used in schools and lower educational purposes. CAM is a subsequent computer-aided process after computer-aided design (CAD) and sometimes computer-aided engineering (CAE), as the model generated in CAD and verified in CAE can be input into CAM software, which then

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https://en.wikipedia.org/wiki/Computer-aided_manufacturing

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In the milling of large monolithic structural components for aircraft, 70-80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a(p)), radial immersion (a(e)), feed rate (f(t)) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine-tool-part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.

Taylor & Francis2011

Exceptions and events in a manufacturing environment

EPFL1996

This paper proposes a novel approach to predict the machining time of small-scale radial-inflow turbine impellers for organic Rankine cycle power systems based on preliminary design parameters. The time prediction method uses machining databases of different milling tools for the estimation of the cutting parameters, on which basis the flow channel volume and surfaces are discretized for the generation of preliminary milling toolpaths. The parameter selection comprises the material selection, the volume removed, the demanded surface quality and the tool selection governed by the accessibility due to geometrical limitations induced by the blades. The model is verified using computer-aided manufacturing software for two radial-inflow turbines cases: a state-of-the-art turbine using air and a turbine using the working fluid Novec 649 for a heat recovery application. The results highlight the governing role of the tool slenderness ratio on the actual manufacturing time. The relative time distribution among roughing, semi-finishing and finishing operations remain similar when considering the same production chain. The tool roughing strategy holds potential for optimization by contributing to approximately 60 % of the total milling time. Furthermore, the results indicate that the turbine manufacturing time can be improved by up to approximately 44 % when the maximum permissible load limits of the tools are exploited by customized cutting data. The presented machining time model can be applied in future works to predict the manufacturing costs of impellers and to quantify the influence of individual design features on the total cost by performing a sensitivity analysis. Moreover, the design space for optimal designs of small-scale radial-inflow turbines for organic Rankine power applications considering both the manufacturing time and the performance of the expander can be explored.

Technical University of Munich2021