The consumption of resources and the impact of manufacturing on the environment have long been known, especially in the widely used traditional manufacturing industry. With the increasing voice of global carbon emission reduction, the manufacturing industry is facing increasing pressure to improve energy efficiency and reduce environmental impact. Moreover, as a major source of global carbon emissions, low-carbon emission reduction of manufacturing industry has long been put on the agenda of governments, and a series of measures have been implemented to control the carbon emissions of manufacturing industry. As a pillar industry of manufacturing industry, sand casting is the cornerstone of casting industry and belongs to a typical traditional manufacturing industry. In its high energy consumption In the case of high emissions, the demand for energy conservation and emission reduction is obvious.
Because the process design parameters have a direct impact on the sand casting process, there are also studies on the calculation and modeling of the carbon emission of sand casting. However, through the sand casting process design parameters, there are few studies on the low-carbon design of sand casting in the design stage. Therefore, the carbon source of the sand casting process is studied, It is of practical significance to carry out low-carbon optimization design in the process design stage of sand casting under the voice of global energy conservation and emission reduction. The main purposes are:
(1) Analyze the process parameters of sand casting, combined with the composition of carbon source, establish the relationship between process parameters and carbon source, and calculate the carbon emission.
(2) Combined with genetic algorithm, the process parameter design scheme of sand casting is optimized, and the optimized process design parameters need to be verified.
(3) Aiming at the comparison of carbon emissions of various carbon sources before and after optimization and the comparison of carbon emissions under various parameters, key low-carbon optimization objects are proposed.