Intelligent decision-making system for casting process parameters of aluminum alloy piston

By acquiring the composition of aluminum alloy melt in real time and constructing dynamic thermophysical parameters, the casting process parameters of aluminum alloy pistons were optimized, solving the casting instability problem caused by melt composition fluctuations and realizing real-time adaptive adjustment of process parameters and efficient production.

CN122337431AActive Publication Date: 2026-07-03QUFU JINHUANG TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610464086.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-09
Publication Date
2026-07-03
Estimated Expiration
2046-04-09

AI Technical Summary

Technical Problem

The existing aluminum alloy piston casting process lacks an online sensing and dynamic response mechanism for melt composition fluctuations, resulting in a continuous mismatch between process parameters and the actual state of the melt, which affects the stability of the casting process and the yield.

Method used

The composition sensing module is used to obtain the composition of aluminum alloy melt in real time. Dynamic thermophysical parameters are generated through a physical field mapping framework. Combined with casting quality objectives, a dynamic solidification sensitivity coefficient matrix is ​​constructed to optimize the pouring temperature, cooling intensity and pressurization timing, so as to realize real-time adaptive adjustment of process parameters.

Benefits of technology

Eliminate the negative impact of batch fluctuations in alloy composition on casting quality, improve process stability and yield, shorten the process parameter development cycle, and adapt to the needs of multi-variety, small-batch production.

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Abstract

The invention relates to the technical field of aluminum alloy piston casting, and particularly discloses an intelligent decision-making system for aluminum alloy piston casting process parameters, which is characterized in that real-time component data of a to-be-cast aluminum alloy melt is acquired, and specific component characteristics of a current heat are formed; inputting the special component characteristics into a physical field mapping framework to generate a dynamic thermophysical property parameter set; constructing a dynamic solidification sensitivity coefficient matrix according to the dynamic thermophysical parameter set in combination with geometric structure characteristics of the piston; taking the dynamic solidification sensitivity coefficient matrix as a constraint boundary, and solving an optimal process parameter combination enabling the mold filling integrity and the solidification compactness to simultaneously reach a preset threshold value; the optimal process parameter combination is output as a casting control reference, and the incidence relation of the physical field mapping framework is updated according to quality characteristic data fed back after casting; according to the method, process parameter self-adaptive decision making under component fluctuation is achieved, and the stability and the yield of the casting process are improved.
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