4.5 Article

An allowance optimization method for near-net-shape blade considering material-saving, energy consumption and carbon emissions

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SPRINGER
DOI: 10.1007/s13762-022-04242-4

关键词

Blade; Allowance optimization; Carbon emission; Energy consumption; Material utilization

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This study proposes a method to reduce carbon emissions in free-form surface machining by optimizing the allowance of blade parts. Comparing with the traditional method, the proposed method shows significant improvements in energy consumption, material utilization, and carbon emissions. Furthermore, the feasibility of the proposed method is demonstrated by using a group of compressor blades for validation.
In the fabrication of aero-engine, the blade parts are difficult to cut, resulting in high energy consumption and pollution. Reducing energy consumption and improving material utilization are conducive to alleviating energy shortage and climate change problems. The blade billet is usually obtained by forging or casting, and then processed by computer numerical control (CNC) precision machining. They are widely used in the manufacturing industry with the characteristics of high precision and weak stiffness, resulting in long machining time and high energy consumption. The forging billet has a large allowance, low material utilization and high machining energy consumption. Therefore, this paper investigates the potential of allowance optimization to reduce carbon emissions in free-form surface machining. An innovative optimization model of reconstructing the machining model of free-form surface is proposed for the near-net-shape billet. By this way, the differences in energy consumption, material utilization and carbon emissions between the traditional method and the proposed method are compared. Finally, a group of blades of an aero-engine compressor is used to demonstrate the proposed method. The carbon emission reduced in the finishing phase is about 3.745 kgCO(2) for a compressor. If 1100 unit compressors are produced one year, the reduction is equivalent to absorptive amount of 228 trees.

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