CN-121614952-B - Forge piece heat treatment process optimization method based on temperature control curve

Abstract

The invention discloses a forge piece heat treatment process optimization method based on a temperature control curve, which relates to the technical field of data processing and comprises the steps of constructing a temperature control performance prediction model based on a process task data packet and performing inverse solution to generate a core temperature control reference curve, performing thermal response mapping on the core temperature control reference curve to obtain a furnace temperature control track, acquiring an online core tracking control scheme through heat treatment risk time sequence evaluation, performing the online core tracking control scheme, synchronously recording process data, acquiring an execution temperature control curve through thermal process inversion operation by combining the furnace temperature control track, optimizing the temperature control performance prediction model based on the execution temperature control curve, and acquiring a process quality evaluation result through performance deviation evaluation. According to the invention, by constructing the temperature control performance prediction model and executing inverse solution and feedback optimization, the computing consistency of the process parameter design and the quantifiable and iterative optimization capacity of result evaluation are improved.

Inventors

• ZHOU JIWEI

Assignees

• 福建申达重工机械有限公司

Dates

Publication Date

20260508

Application Date

20260130

Claims (4)

1. 1. A forge piece heat treatment process optimizing method based on a temperature control curve is characterized by comprising the following steps of, Acquiring heat treatment process task parameters of the forging, and acquiring a process task data packet through pretreatment; Based on the process task data packet, a temperature control performance prediction model is constructed, and inverse solution is executed to generate a core temperature control reference curve, specifically comprising the following steps, Based on the temperature control performance prediction model, acquiring an inverse solution target set through target performance reconstruction; performing temperature control variable search on the inverse solving target set to generate an initial temperature control variable sequence, and performing performance deviation direction analysis to generate a temperature control variable correction direction sequence; Carrying out temperature time axis reconstruction operation on the temperature control variable correction direction sequence and the inverse solution initial temperature control variable sequence to obtain a core temperature control reference curve; performing thermal response mapping on the core temperature control reference curve to obtain a furnace temperature control track, and obtaining an online core tracking control scheme through thermal treatment risk time sequence evaluation, wherein the method comprises the following specific steps, Converting the temperature sequence into a hearth target temperature sequence which changes with time through a thermal response mapping algorithm based on a core temperature control reference curve, and generating a furnace temperature control track; Combining the furnace temperature control track and the process task data packet, calculating a heat treatment risk level through a heat treatment risk time sequence evaluation algorithm, and generating a heat treatment risk time sequence curve; Determining a regulating variable combination through an on-line control strategy synthesis method according to the heat treatment risk time sequence curve and the furnace temperature control track to generate an on-line core tracking control scheme; executing an online core tracking control scheme, synchronously recording process data, and acquiring an execution temperature control curve by combining a furnace temperature control track through thermal history inversion operation, Based on an online core tracking control scheme, executing furnace temperature adjustment, synchronously recording furnace temperature, surface temperature and core temperature, and generating a process data sequence; comparing the core temperature control reference curve with a process data sequence, and obtaining an actual core temperature control curve through inversion operation of thermal history; combining the actual temperature control curve of the core with the temperature control track of the furnace temperature control track to obtain an execution temperature control curve; based on the execution temperature control curve, optimizing the temperature control performance prediction model, and obtaining a process quality evaluation result through performance deviation evaluation, wherein the specific steps are as follows, Generating a performance prediction time sequence set by replaying a temperature time history of the execution temperature control curve based on the execution temperature control curve; according to the performance prediction time sequence set and the performance requirement data, constructing a time index two-dimensional performance deviation distribution, extracting a deviation peak value and a deviation accumulation amount, and generating a performance deviation field; According to the performance deviation field, performing self-consistent write-back optimization on parameters of the temperature control performance prediction model to generate an optimized temperature control performance prediction model; Based on the optimized temperature control performance prediction model, a process quality assessment result is generated by comprehensively quantifying and grading the performance achievement degree, the deviation distribution characteristics and the model convergence state.
2. 2. The forging heat treatment process optimization method based on the temperature control curve as set forth in claim 1, wherein the forging heat treatment process task parameters comprise forging materials, geometric dimensions, furnace loading modes, hardness requirements and residual stress requirements; The preprocessing includes integrity checking, unit unification, and structured finishing.
3. 3. The method for optimizing forging heat treatment process based on temperature control curve according to claim 2, wherein the process task data packet comprises forging information data and performance requirement data.
4. 4. The method for optimizing the heat treatment process of the forge piece based on the temperature control curve according to claim 3, wherein the temperature control performance prediction model is constructed based on the process task data packet, and comprises the following specific steps of, Carrying out field analysis on the process task data packet, and decomposing the process task data packet into a forging information data set and a performance requirement data set through classification operation; Based on the forge piece information data set, extracting and mapping the temperature and time related attributes by using a temperature control feature extraction algorithm to generate a temperature control variable set; Generating a temperature control performance mapping structure through constraint driving mapping according to the performance requirement data set and the temperature control variable set; the generation of the temperature control performance mapping structure through constraint driving mapping refers to comparing the relation between a temperature range corresponding to a temperature-related attribute and a target hardness range in hardness requirement data, comparing the relation between duration corresponding to a time-related attribute and a control time range in residual stress requirement data, determining matched temperature, hardness constraint and time and residual stress constraint for each temperature control variable record, and combining the temperature control variable record, the hardness constraint and the residual stress constraint into one mapping record; summarizing all mapping records according to the reading sequence of the temperature control variable records, and arranging a performance requirement data set and constraint conditions to generate a temperature control performance mapping structure; Based on the process task data packet, calculating a hardness predicted value and a residual stress predicted value, and adjusting parameters in the temperature control performance mapping structure to obtain a temperature control performance predicted model.

Description

Forge piece heat treatment process optimization method based on temperature control curve Technical Field The invention relates to the technical field of data processing, in particular to a forge piece heat treatment process optimization method based on a temperature control curve. Background Along with the continuous development of data processing, industrial informatization and complex process modeling methods, process parameter modeling, prediction and optimization based on multi-source data gradually become an important research direction of digital upgrading. In the prior art, for a complex industrial process, a process performance prediction model is constructed by collecting multidimensional parameters related to process execution, and parameter configuration schemes are evaluated and adjusted to realize continuous optimization of process quality. In recent years, with the improvement of model computing capability and data processing capability, part of technologies begin to introduce prediction models to support the intellectualization of parameter configuration processes, and a new technical path is provided for the digital management of complex processes. However, existing data processing techniques based on process data still have certain limitations in practical applications. On the one hand, focusing on forward prediction or post-hoc analysis of process execution results, the lack of a reverse reasoning mechanism with target performance constraints as a core, results in the generation of process parameters still depending on empirical rules. On the other hand, the utilization mode of the feedback data of the execution process is relatively coarse, and the feedback data is only used for simple correction and offline analysis, and a deviation evaluation and model self-consistency updating mechanism based on time dimension is lacked, so that the adaptability and decision reliability of the data processing in a complex process optimization scene are limited. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the forge piece heat treatment process optimization method based on the temperature control curve solves the problem that reverse generation of the temperature control curve and process risk quantification optimization are difficult. In order to solve the technical problems, the invention provides the following technical scheme: The invention provides a forge piece heat treatment process optimization method based on a temperature control curve, which comprises the steps of collecting forge piece heat treatment process task parameters, obtaining a process task data packet through preprocessing, constructing a temperature control performance prediction model based on the process task data packet, performing inverse solution to generate a core temperature control reference curve, performing thermal response mapping on the core temperature control reference curve, obtaining a furnace temperature control track, obtaining an online core tracking control scheme through heat treatment risk time sequence evaluation, performing the online core tracking control scheme, synchronously recording process data, obtaining an execution temperature control curve through thermal process inversion operation by combining the furnace temperature control track, optimizing the temperature control performance prediction model based on the execution temperature control curve, and obtaining a process quality evaluation result through performance deviation evaluation. As a preferable scheme of the forge piece heat treatment process optimization method based on the temperature control curve, the forge piece heat treatment process task parameters comprise forge piece materials, geometric dimensions, a charging mode, hardness requirements and residual stress requirements; The preprocessing includes integrity checking, unit unification, and structured finishing. As an optimal scheme of the forge piece heat treatment process optimization method based on the temperature control curve, the process task data packet comprises forge piece information data and performance requirement data. As a preferable scheme of the forge piece heat treatment process optimizing method based on the temperature control curve, the method comprises the steps of constructing a temperature control performance prediction model based on a process task data packet, specifically comprising the following steps of, Carrying out field analysis on the process task data packet, and decomposing the process task data packet into a forging information data set and a performance requirement data set through classification operation; Based on the forge piece information data set, extracting and mapping the temperature and time related attributes by using a temperature control feature extraction algorithm to generate a temperature control variable set; Generating a temperature cont