Heat treatment furnace distribution and process control method in metal processing process

Abstract

The invention discloses a heat treatment furnace configuration and process control method in a metal processing process, and relates to the technical field of metal processing, mapping matching is carried out by combining a set formed by regional temperature control capability indexes Zon of all furnace chamber positions in a furnace chamber, and after a scoring matrix MatSet is generated, the scoring matrix MatSet is calculated; an optimal charging combination is obtained based on a maximum matching priority mechanism and a global heat load balancing mechanism, comprehensive verification data such as a temperature distribution consistency score Unif, a maximum local temperature difference value Maxd and a furnace area average temperature rise rate equilibrium value Rate are obtained through heat treatment analogue simulation, and finally an execution instruction is generated according to a comparison result with a preset process control threshold value. The method does not only depend on space matching of geometric shapes of the workpieces, but considers differences of heat capacities, heat conduction capacities and quality parameters of different workpieces through a workpiece thermal mass load factor Wlc, so that the problem of local overheating or local temperature failure caused by unreasonable thermal load distribution is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal processing, in particular to a heat treatment furnace arrangement and process control method in the metal processing process. BACKGROUND

[0002] Metal processing technology, as a crucial link in the manufacturing industry, covers various processes such as casting, forging, welding, cutting, and heat treatment. In particular, during the deep processing stage aimed at improving the mechanical properties of parts, heat treatment technology becomes an indispensable key link as it can control the microstructure and stress state of metal materials. In the specific classification of heat treatment, processes such as quenching, tempering, annealing, and carburizing often need to be performed in a sealed furnace chamber, and precise control of parameters such as temperature gradient, heating rate, and holding time is required.

[0003] The existing loading method of heat treatment furnace mainly relies on the spatial matching principle of workpiece geometry to plan the furnace loading, i.e., mainly using workpiece size as the arrangement standard for placement and combination. However, this method ignores an important physical fact: different workpieces have significant differences in thermal capacity, heat conduction ability, and mass parameters. Even if the furnace chamber can be accurately filled in space, it is difficult to ensure that the heat load distribution in each region of the furnace is reasonable. During the heating process, different workpieces with different thermal capacities will disturb the surrounding thermal field, leading to uneven temperature distribution in the furnace chamber, resulting in "local overheating" or "local temperature not reached" problems, affecting the final heat treatment quality.

[0004] At the same time, the existing furnace loading optimization strategy lacks the ability to adapt to the differences in temperature control capabilities within the furnace chamber. In actual industrial furnace equipment, different furnace zones, such as upper, middle, and lower layers, furnace wall periphery and central axis regions, have different controlled heating element numbers, heat flux densities, and circulating airflow channel structures, resulting in natural differences in temperature control capabilities within the furnace chamber. The existing method does not correspondingly match the temperature control capabilities with the workpiece heat load capabilities, often causing "high heat load workpieces in weak temperature control areas" or "low heat load workpieces wasting high-quality furnace area resources", further amplifying the difficulty of temperature control. SUMMARY

[0005] To overcome the deficiencies of the prior art, the present application provides a heat treatment furnace arrangement and process control method in the metal processing process, which solves the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a heat treatment furnace arrangement and process control method in the metal processing process, comprising the following steps:

[0007] S1, collect the basic data of each workpiece to be heat treated, obtain the workpiece thermal mass load factor Wlc of the workpiece to be heat treated based on the basic data, and construct a workpiece thermal load factor set WlcSet;

[0008] S2, collect equipment data in the furnace cavity, generate a regional temperature control capability index Zon of each furnace cavity position in the furnace cavity based on the equipment data, and form a furnace zone temperature control capability distribution set ZonSet;

[0009] S3, map the workpiece thermal load factor set WlcSet and the furnace zone temperature control capability distribution set ZonSet, construct a scoring matrix MatSet of workpiece and furnace zone matching based on the constructed matching scoring mechanism;

[0010] S4, based on the scoring matrix MatSet, combine the maximum matching priority mechanism and the global thermal load balancing mechanism to generate an optimal furnace loading combination, and integrate it into an optimal furnace loading layout result set LaySet;

[0011] S5, taking the optimal furnace loading layout result set LaySet as input, performing heat treatment simulation, obtaining temperature distribution consistency score Unif, maximum local temperature difference value Maxd and furnace zone average heating rate balance value Rate, and combining them into a comprehensive verification data set SimSet;

[0012] S6, traverse and compare the comprehensive verification data set SimSet and the preset process control threshold, and generate a furnace loading operation instruction according to the comparison result, form a furnace loading execution scheduling instruction set ComSet and issue it.

[0013] Preferably, the S1 includes S11 and S12;

[0014] S11, collect the basic parameters of the workpiece to be heat treated, and the basic data includes specific heat parameter Heat, mass parameter Mass, volume parameter Volu and surface area parameter Area;

[0015] The basic parameters are extracted from the basic parameters of the workpiece to be heat treated through the preset database query, forming a one-to-one workpiece parameter item, and all the data of the workpieces to be processed are uniformly summarized to generate a workpiece thermal property data set BasSet;

[0016] S12, taking the workpiece thermal property data set BasSet as input, respectively constructing a corresponding workpiece thermal mass load factor Wlc for each workpiece;

[0017] The workpiece thermal mass load factor Wlc is obtained by the following calculation formula:

[0018] Wlc=Heat×Mass×(Volu÷Area);

[0019] After all the workpieces are calculated, the workpiece thermal mass load factors Wlc corresponding to all the workpieces are sorted and collected to form a workpiece thermal load factor set WlcSet.

[0020] Preferably, S2 includes S21 and S22.

[0021] S21, before the start of the heat treatment operation, multi-dimensional parameter acquisition is performed on the physical structure and operating state of the heat treatment furnace cavity, and the acquisition targets include temperature control related performance indicators of each spatial region inside the furnace cavity, the temperature control related performance indicators including: heating unit distribution density Heat, gas circulation flow rate Flow, furnace wall heat conduction characteristics Cond, and historical temperature control error Hist.

[0022] The temperature control related performance indicators are jointly obtained through multi-point sensor nodes arranged inside the furnace cavity, a temperature control scheduling system interface, and a heat treatment history record database, and the temperature control related performance indicators are structured coordinate index data.

[0023] The entire furnace cavity is then spatially modeled using a three-dimensional finite element subdivision method, and the spatial modeling process is as follows:

[0024] Modeling process one: the furnace cavity is divided into a plurality of cubic unit bodies with determined volumes and spatial positions;

[0025] Modeling process two: each unit body is uniquely numbered and corresponds to a furnace cavity region, and each unit body is a thermal behavior evaluation unit.

[0026] Based on the spatial modeling process, a complete furnace cavity region gridding model MeshMap is constructed in space, and the temperature control related performance indicators are then summarized to form a unified data set, named cavity device structure data set DevSet.

[0027] Preferably, S22, based on the cavity device structure data set DevSet as the basic data, the regional thermal control capability of each unit body is comprehensively calculated to establish a regional temperature control capability indicator Zon reflecting the actual temperature control performance.

[0028] The regional temperature control capability indicator Zon is obtained by normalizing the temperature control related performance indicators corresponding to each unit body and using the following linear normalization evaluation calculation formula:

[0029] Zon = α × Heat + β × Flow + γ × Cond - δ × Hist.

[0030] In the formula, α, β, γ and δ respectively represent the process weight coefficients of the heating unit distribution density Heat, the gas circulation flow rate Flow, the furnace wall heat conduction characteristic Cond and the historical temperature control error Hist, the specific values are set by the user, and α+β+γ+δ=1;

[0031] The calculation results of the zone temperature control capability indicators Zon of all unit bodies are numbered and summarized according to the spatial grid index to construct a complete furnace zone temperature control capability distribution set ZonSet.

[0032] Preferably, the S3 comprises S31 and S32.

[0033] S31, normalizing the workpiece heat load factor set WlcSet to obtain a normalized heat load indicator WlcNorm, and then taking the furnace zone temperature control capability distribution set ZonSet as input data set to construct a matching score function Fscor for evaluating the thermal adaptation degree of the workpiece and the furnace position, which is used to quantify the matching effect of the workpiece and the furnace position in terms of thermal load distribution;

[0034] The matching score function Fscor is constructed by the following calculation formula:

[0035] Scor=1-|(WlcNorm÷Zon)-1|;

[0036] In the formula, Scor represents the matching score value, and WlcNorm represents the normalized workpiece heat quality load factor.

[0037] Preferably, S32, according to the two-dimensional index of all workpiece numbers and all furnace zone numbers, the matching score function Fscor is called in sequence to calculate the matching score of each combination of workpiece number and furnace position number.

[0038] All matching score values Scor are constructed into a two-dimensional score matrix, and output as a workpiece and furnace zone matching score matrix MatSet, which has a structure that the row dimension is the workpiece index, corresponding to the workpiece heat load factor set WlcSet, and the column dimension is the furnace zone index, corresponding to the furnace zone temperature control capability distribution set ZonSet.

[0039] Preferably, the S4 comprises S41.

[0040] S41, based on the score matrix MatSet, a furnace loading optimization model is constructed, and an optimal furnace loading layout result set LaySet is obtained based on the furnace loading optimization model;

[0041] The furnace loading optimization model is formed by combining the maximum matching priority mechanism and the global thermal load balancing mechanism.

[0042] The maximum matching priority mechanism generates a furnace loading distribution scheme by selecting workpiece-furnace position combinations in the scoring matrix MatSet in descending order of matching score value Scor;

[0043] The global thermal load balancing mechanism identifies the highest and lowest thermal load zones as thermal distribution deviation zones by calculating the cumulative normalized thermal load total of each furnace zone, and then selects workpiece pairs with a matching score value Scor difference of no more than 0.1 from the two zones for position exchange, and iterates until the thermal load difference no longer decreases.

[0044] The cumulative normalized thermal load total represents the sum of the normalized thermal load indicators WlcNorm of all allocated workpieces in each furnace zone.

[0045] Preferably, S5 includes S51 and S52.

[0046] S51, taking the optimal furnace loading layout result set LaySet as input data, simulating based on the discrete temperature field calculation method of the steady-state heat conduction model, mapping the workpiece arrangement information in the optimal furnace loading layout result set LaySet and the corresponding furnace zone number to a three-dimensional discrete coordinate system; then taking the temperature control capability Zon of each furnace zone as the basis of the heat input intensity, and taking the normalized thermal load indicator WlcNorm as the heat absorption characteristic, establishing a heat balance relationship; based on the steady-state heat conduction calculation model, calculating the temperature response value of each discrete coordinate node;

[0047] Finally, all temperature response values are extracted in the holding phase, and the following three thermal control evaluation indicators are obtained according to the temperature response values: temperature distribution consistency score Unif, maximum local temperature difference value Maxd, and furnace zone average heating rate balance value Rate, and combined into a comprehensive verification data set SimSet;

[0048] The temperature distribution consistency score Unif is obtained by statistical all temperature nodes and the average deviation degree of the preset target temperature, and is normalized as a consistency score;

[0049] The maximum local temperature difference value Maxd is obtained by extracting the difference between the maximum and minimum values from all temperature nodes, representing the local temperature difference extreme value;

[0050] The furnace zone average heating rate balance value Rate is obtained by comparing the temperature growth curves of each furnace zone in the heating phase, and is normalized as a consistency score.

[0051] Preferably, S6 includes S61.

[0052] S61, taking the comprehensive verification data set SimSet as an input basis, comparing the comprehensive verification data set SimSet with a preset process control threshold value, and generating a furnace charging operation instruction according to a comparison result, forming a furnace charging execution scheduling instruction set ComSet, and issuing the instruction to a relevant operator based on the furnace charging execution scheduling instruction set ComSet.

[0053] Preferably, the preset process control threshold value includes a temperature distribution consistency score standard threshold value ValUnif, a maximum local temperature difference allowable value threshold value ValMaxd, and a temperature rise rate balance score standard threshold value ValRate.

[0054] The furnace charging operation instruction is generated through the following comparison process:

[0055] Comparison result one: the temperature distribution consistency score Unif is greater than or equal to the temperature distribution consistency score standard threshold value ValUnif.

[0056] Comparison result two: the maximum local temperature difference value Maxd is less than or equal to the maximum local temperature difference allowable value threshold value ValMaxd.

[0057] Comparison result three: the furnace zone average temperature rise rate balance value Rate is greater than or equal to the temperature rise rate balance score standard threshold value ValRate.

[0058] When the comparison results one, two and three are all true, the furnace charging operation instruction is generated, and the content in the optimal furnace charging layout result set LaySet is converted into a structured scheduling instruction to form the furnace charging execution scheduling instruction set ComSet.

[0059] The present application provides a heat treatment furnace charging and process control method in a metal processing process, which has the following beneficial effects:

[0060] (1) After the mapping matching is performed on the set formed by the zone temperature control ability index Zon of each zone position in the furnace cavity, the score matrix MatSet is generated, the optimal furnace charging combination is obtained based on the maximum matching priority mechanism and the global heat load balance mechanism, the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd, and the furnace zone average temperature rise rate balance value Rate, etc. are obtained through heat treatment simulation, and finally the execution instruction is generated according to the comparison result with the preset process control threshold value. This method effectively makes up for the shortcomings of the existing furnace charging method: instead of relying only on the spatial matching of the workpiece geometric shape, the thermal capacity, heat conduction ability and mass parameter differences of different workpieces are considered by the workpiece thermal mass load factor Wlc, and the problems of "local overheating" or "local temperature not reached" caused by unreasonable heat load distribution are avoided.

[0061] (2) By collecting the specific heat parameter Heat, the mass parameter Mass, the volume parameter Volu and the surface area parameter Area of the workpiece to be heat treated, forming the workpiece thermal property data set BasSet, and then calculating the workpiece thermal mass load factor Wlc and constructing the set WlcSet, the heat storage and heat transfer characteristics of different workpieces can be accurately quantified, providing a reliable basis for identifying the thermal sensitivity of the workpiece, and avoiding the one-sidedness of judging the thermal characteristics of the workpiece only according to the geometric shape; the heating unit distribution density Heat, the gas circulation flow rate Flow, the furnace wall heat conduction characteristics Cond and the historical temperature control error Hist and other temperature control related performance indicators of each region of the furnace cavity are collected, a three-dimensional finite element subdivision method is used to construct a furnace cavity regional gridding model MeshMap and form a cavity device structure data set DevSet, and then the regional temperature control ability index Zon and the furnace zone temperature control ability distribution set ZonSet are obtained through a linear normalization evaluation formula, realizing a detailed description of the temperature control ability of different regions of the furnace cavity, laying a data foundation for the accurate matching of the workpiece and the furnace zone in the subsequent process, and solving the problem of fuzzy understanding of the temperature control ability difference of the furnace zone in the past.

[0062] (3) Calculate the matching score value Scor and form the score matrix MatSet, accurately quantify the thermal adaptation degree of the workpiece and the furnace position, provide a scientific scoring basis for subsequent matching, and solve the problem of lack of quantitative standard for matching of workpieces and furnace zones in the past; based on the score matrix MatSet, combined with the maximum matching priority mechanism and the global thermal load balancing mechanism, the optimal furnace loading combination is generated, the position of the workpiece pair with a matching score value Scor difference of not more than 0.1 in the highest and lowest thermal load zones is exchanged, realizing the balanced distribution of global thermal load, and avoiding the situation of local thermal load being too high or too low; taking the optimal furnace loading layout result set LaySet as input for simulation, obtaining the comprehensive verification data set SimSet composed of the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd and the furnace zone average heating rate balance value Rate, verifying the thermal control effect of the furnace loading layout from multiple dimensions, ensuring the reliability of the furnace loading scheme; comparing the comprehensive verification data set SimSet with the preset process control threshold, generating the furnace loading execution scheduling instruction set ComSet, so that the furnace loading operation has a clear basis, ensuring the consistency of the actual operation and the optimized scheme, and improving the stability and accuracy of the heat treatment process. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 It is a schematic diagram of the steps of the heat treatment furnace loading and process control method in the metal processing process. DETAILED DESCRIPTION

[0064] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0065] Embodiment 1

[0066] The present application provides a heat treatment furnace matching and process control method in a metal processing process, please refer to Figure 1 , comprising the following steps:

[0067] S1, collecting the basic data of each workpiece to be heat treated, obtaining the workpiece heat mass load factor Wlc of the workpiece to be heat treated based on the basic data, and constructing the workpiece heat load factor set WlcSet;

[0068] S2, collecting the equipment data in the furnace cavity, generating the regional temperature control capability index Zon of each furnace cavity position in the furnace cavity based on the equipment data, and forming the furnace zone temperature control capability distribution set ZonSet;

[0069] S3, mapping the workpiece heat load factor set WlcSet and the furnace zone temperature control capability distribution set ZonSet, constructing the scoring matrix MatSet of the workpiece and furnace zone matching based on the constructed matching scoring mechanism;

[0070] S4, on the basis of the scoring matrix MatSet, combining the maximum matching priority mechanism and the global heat load balancing mechanism, generating the optimal furnace loading combination, and integrating into the optimal furnace loading layout result set LaySet;

[0071] S5, taking the optimal furnace loading layout result set LaySet as input, performing heat treatment simulation, obtaining the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd and the furnace zone average heating rate balance value Rate, and combining them into the comprehensive verification data set SimSet;

[0072] S6, traversing and comparing the comprehensive verification data set SimSet and the preset process control threshold, and generating the furnace loading operation instruction according to the comparison result, forming the furnace matching execution scheduling instruction set ComSet for issuing.

[0073] In this embodiment, the mapping matching is performed based on the set formed by the zone temperature control capability indicators Zon of each furnace cavity position, a score matrix MatSet is generated, and then the optimal furnace loading combination is obtained based on the maximum matching priority mechanism and the global thermal load balancing mechanism. Then, the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd, and the furnace zone average heating rate balance value Rate are obtained through heat treatment simulation, and finally the execution instruction is generated according to the comparison result with the preset process control threshold. This method effectively makes up for the shortcomings of the existing furnace loading method: instead of relying only on the spatial matching of the workpiece geometric shape, the thermal capacity, heat conduction ability and mass parameter differences of different workpieces are considered by the workpiece thermal load factor Wlc, avoiding the problem of "local overheating" or "local temperature not reached" caused by unreasonable thermal load distribution; at the same time, the corresponding matching of workpiece thermal load and furnace zone temperature control capability is realized by means of the zone temperature control capability indicator Zon, solving the phenomenon of "high thermal load workpiece located in the weak temperature control area" or "low thermal load workpiece wasting high-quality furnace area resources", thereby improving the heat treatment quality.

[0074] Embodiment 2

[0075] Specifically, the S1 includes S11 and S12.

[0076] S11, collect the basic parameters of the workpieces to be heat treated, and the basic data includes specific heat parameter Heat, mass parameter Mass, volume parameter Volu, and surface area parameter Area;

[0077] The basic parameters are extracted from the preset database to obtain the basic parameters of the workpieces to be heat treated, forming a one-to-one corresponding workpiece parameter item, and the data of all workpieces to be processed are uniformly summarized to generate a workpiece thermal property data set BasSet.

[0078] S12, taking the workpiece thermal property data set BasSet as input, respectively constructing a corresponding workpiece thermal load factor Wlc for each workpiece;

[0079] The workpiece thermal load factor Wlc is obtained by the following calculation formula:

[0080] Wlc = Heat x Mass x (Volu ÷ Area);

[0081] After calculating all workpieces, the workpiece thermal load factors Wlc corresponding to all workpieces are sorted and collected to form a workpiece thermal load factor set WlcSet. The workpiece thermal load factor Wlc comprehensively reflects the heat storage and heat transfer characteristics of the workpiece, and is an important basis for the control algorithm to identify the thermal sensitivity of the workpiece.

[0082] The S2 includes S21 and S22.

[0083] S21, before the heat treatment operation starts, multi-dimensional parameter collection is performed on the physical structure and operating state of the heat treatment furnace chamber, and the collection targets include temperature control related performance indicators of each spatial region inside the furnace chamber, and the temperature control related performance indicators include: heating unit distribution density Heat, gas circulation flow rate Flow, furnace wall heat conduction characteristic Cond, and historical temperature control error Hist;

[0084] The heating unit distribution density Heat represents the density of the number and power of heating elements configured in the region;

[0085] The gas circulation flow rate Flow represents the intensity of gas circulation in the region, which affects the heat convection efficiency;

[0086] The furnace wall heat conduction characteristic Cond represents the heat conduction performance of the inner wall material in the region, including the heat conduction coefficient, thickness, etc.;

[0087] The historical temperature control error Hist represents the average temperature control deviation of the region in the past heat treatment process, reflecting the local temperature control stability;

[0088] The temperature control related performance indicators are obtained through the multi-point sensor nodes arranged inside the furnace chamber, the temperature control scheduling system interface, and the heat treatment history record database, and the temperature control related performance indicators are structured coordinate index data;

[0089] Then, a three-dimensional finite element subdivision method is used to model the entire furnace chamber, and the process of the spatial modeling is as follows:

[0090] Modeling process one: the furnace chamber is divided into a plurality of cubic unit bodies with determined volume and spatial position;

[0091] Modeling process two: each unit body is uniquely numbered and corresponds to a furnace chamber region, and each unit body is a heat behavior evaluation unit;

[0092] Based on the spatial modeling process, a complete furnace chamber region gridding model MeshMap is constructed in space, and the temperature control related performance indicators are summarized to form a unified data set, named as chamber device structure data set DevSet.

[0093] S22, taking the furnace chamber device structure data set DevSet as the basic data, the regional heat control capacity of each unit body is calculated, and the regional temperature control capacity indicator Zon reflecting the actual temperature control performance is established;

[0094] The regional temperature control capacity indicator Zon is obtained by normalizing the temperature control related performance indicators corresponding to each unit body and using the following linear normalization evaluation calculation formula:

[0095] Zon = a x Heat + b x Flow + g x Cond - d x Hist;

[0096] In the formula, a, b, g, and d represent the process weight coefficients of the heating unit distribution density Heat, the gas circulation flow rate Flow, the furnace wall heat conduction characteristic Cond, and the historical temperature control error Hist, respectively, the specific values are set by the user, and a + b + g + d = 1; the physical meaning of the formula is that the thermal control capability evaluation result is obtained by normalizing and fusing the above-mentioned multiple physical parameters in the spatial structure, the Zon index is high, which represents that the region has better thermal response capability and is suitable for carrying workpieces with higher thermal load, and the Zon is low, which indicates that the region has weak points in structure or historical performance, and should be matched with workpieces with lower thermal load.

[0097] The Zon calculation results of all unit regions are numbered and summarized according to the spatial grid index to construct a complete furnace region temperature control capability distribution set ZonSet, wherein each item in the furnace region temperature control capability distribution set ZonSet is composed of a uniquely numbered spatial unit and a corresponding Zon value, forming a mapping distribution map of the thermal control capability in the furnace cavity space.

[0098] In the embodiment, the specific heat parameter Heat, the mass parameter Mass, the volume parameter Volu, and the surface area parameter Area of the workpiece to be heat treated are collected to form a workpiece thermal property data set BasSet, and the workpiece thermal mass load factor Wlc is calculated and a set WlcSet is constructed, which can accurately quantify the heat storage and heat transfer characteristics of different workpieces, and provide a reliable basis for identifying the thermal sensitivity of the workpiece, avoiding the one-sidedness of judging the thermal characteristics of the workpiece according to the geometric shape only; the heating unit distribution density Heat, the gas circulation flow rate Flow, the furnace wall heat conduction characteristic Cond, and the historical temperature control error Hist and other temperature control related performance indicators of each region of the furnace cavity are collected, a furnace cavity region gridding model MeshMap is constructed by combining the three-dimensional finite element subdivision method, and a cavity device structure data set DevSet is formed, and then the regional temperature control capability index Zon and the furnace region temperature control capability distribution set ZonSet are obtained through the linear normalization evaluation formula, realizing the detailed description of the temperature control capability of different regions of the furnace cavity, laying a data foundation for the accurate matching of the workpiece and the furnace region in the future, and solving the problem of fuzzy understanding of the temperature control capability difference of the furnace region in the past.

[0099] Embodiment 3

[0100] Specifically, the S3 includes S31 and S32.

[0101] S31, normalize the workpiece heat load factor set WlcSet to obtain a normalized heat load index WlcNorm, and then use the furnace zone temperature control capability distribution set ZonSet as input data set to construct a matching score function Fscor for evaluating the thermal adaptation degree of the workpiece and the furnace position, which is used to quantify the matching effect of the workpiece and the furnace position in terms of thermal load distribution;

[0102] The matching score function Fscor is constructed by the following calculation formula:

[0103] Scor = 1-|(WlcNorm ÷ Zon)-1|;

[0104] In the formula, Scor represents the matching score value, and WlcNorm represents the normalized workpiece thermal mass load factor. The physical meaning of the function is that the closer the normalized workpiece thermal mass load factor WlcNorm and the zone temperature control capability index Zon are to each other, the closer the matching score value Scor is to 1, indicating a higher matching degree. The greater the difference, the closer the score is to 0, indicating an unbalanced thermal load distribution.

[0105] S32, according to the two-dimensional index of all workpiece numbers and all furnace zone numbers, the matching score function Fscor is called in sequence to calculate the matching score of each combination of workpiece number and furnace position number;

[0106] All matching score values Scor are constructed into a two-dimensional score matrix, and output as a workpiece and furnace zone matching score matrix MatSet, which has a structure of row dimension corresponding to the workpiece heat load factor set WlcSet and column dimension corresponding to the furnace zone temperature control capability distribution set ZonSet;

[0107] Each element in the workpiece and furnace zone matching score matrix MatSet is a matching score value Scor, representing the thermal load matching effect generated by arranging the workpiece in the furnace position, as shown in Table 1.

[0108] Table 1: Matrix example structure:

[0109] Furnace position 1 Furnace position 2 Furnace position 3 ... Workpiece A 0.92 0.74 0.51 ... Workpiece B 0.87 0.93 0.66 ... Workpiece C 0.45 0.61 0.90 ... ... ... ... ... ... .

[0110] S41, based on the score matrix MatSet, an optimal furnace loading model is constructed, and an optimal furnace loading layout result set LaySet is obtained based on the optimal furnace loading model;

[0111] The optimal furnace loading layout result set LaySet records the furnace cavity number, furnace position index and matching score result corresponding to each workpiece, which is used for subsequent simulation verification and execution scheduling;

[0112] The furnace charging optimization model is formed by combining a maximum matching priority mechanism and a global thermal load balancing mechanism;

[0113] The maximum matching priority mechanism generates a preliminary furnace charging distribution scheme by sequentially selecting workpiece-furnace position combinations in the scoring matrix MatSet in descending order of matching score value Scor;

[0114] The global thermal load balancing mechanism identifies the highest and lowest thermal load zones as thermal distribution deviation zones by calculating the cumulative normalized thermal load total of the assigned workpieces in each furnace zone, and on this basis, selects workpiece pairs with a matching score value Scor difference of no more than 0.1 from the two zones, exchanges their positions, and iterates until the thermal load difference no longer decreases.

[0115] The cumulative normalized thermal load total represents the summation of the normalized thermal load indicators WlcNorm of all assigned workpieces in each furnace zone, indicating the overall thermal load level currently borne by the furnace zone.

[0116] The S5 includes S51;

[0117] S51, taking the optimal furnace charging layout result set LaySet as input data, simulating based on the discrete temperature field calculation method of the steady-state heat conduction model, mapping the workpiece arrangement information in the optimal furnace charging layout result set LaySet and the corresponding furnace zone number to a three-dimensional discrete coordinate system; then taking the temperature control capability Zon of each furnace zone as the basis for the heat input intensity, and taking the normalized thermal load indicator WlcNorm as the heat absorption characteristic, establishing a heat balance relationship; based on the steady-state heat conduction calculation model, calculating the temperature response value of each discrete coordinate node;

[0118] Finally, all temperature response values are extracted in the holding phase, and the following three thermal control evaluation indicators are obtained according to the temperature response values: temperature distribution consistency score Unif, maximum local temperature difference value Maxd, and furnace zone average heating rate balance value Rate, and combined into a comprehensive verification data set SimSet;

[0119] The temperature distribution consistency score Unif is obtained by statistically analyzing the average deviation of all temperature nodes from the preset target temperature, and is normalized as a consistency score;

[0120] The maximum local temperature difference value Maxd is obtained by extracting the difference between the maximum and minimum values from all temperature nodes, representing the local temperature difference extreme value;

[0121] The furnace zone average heating rate balance value Rate is obtained by comparing the heating speed difference according to the temperature growth curve of each furnace zone in the heating phase, and is normalized as a consistency score.

[0122] The S6 includes S61;

[0123] S61, taking the comprehensive verification data set SimSet as an input basis, comparing the comprehensive verification data set SimSet with the preset process control threshold, and generating a furnace charging operation instruction according to the comparison result, forming a furnace charging execution scheduling instruction set ComSet, and issuing the instruction to the relevant operating personnel based on the furnace charging execution scheduling instruction set ComSet.

[0124] The preset process control threshold includes a temperature distribution consistency score standard threshold ValUnif, a maximum local temperature difference allowed value threshold ValMaxd, and a temperature rise rate balance score standard threshold ValRate.

[0125] The furnace charging operation instruction is generated through the following comparison process:

[0126] Comparison result one: temperature distribution consistency score Unif≥temperature distribution consistency score standard threshold ValUnif;

[0127] Comparison result two: maximum local temperature difference value Maxd≤maximum local temperature difference allowed value threshold ValMaxd;

[0128] Comparison result three: furnace zone average temperature rise rate balance value Rate≥temperature rise rate balance score standard threshold ValRate;

[0129] When comparison results one, two and three are all true, a furnace charging operation instruction is generated, the content in the optimal furnace charging layout result set LaySet is converted into a structured scheduling instruction, forming a furnace charging execution scheduling instruction set ComSet, which provides clear furnace charging layout execution instructions to operating personnel or equipment control systems under the premise of meeting heat treatment process conditions, ensures that actual operation is consistent with simulation optimization results, has direct guidance significance for implementation, including workpiece number, allocated furnace cavity number, corresponding furnace position index, execution order and operation prompt information (such as “preferential placement” and “close to heat source”).

[0130] Wherein, the workpiece number comes from the workpiece thermal load factor set WlcSet established in step S1; the allocated furnace cavity number comes from the optimal furnace charging layout result set LaySet; the corresponding furnace position index comes from the optimal furnace charging layout result set LaySet; the execution order comes from the score sorting or thermal load scheduling strategy in the optimal furnace charging layout result set LaySet; and the operation prompt information is derived from the matching relationship between the zone temperature control capability Zon in the furnace zone temperature control capability distribution set ZonSet and the workpiece normalized thermal load index WlcNorm.

[0131] In the embodiment, the matching score Scor is calculated and the score matrix MatSet is formed, the heat adaptation degree of the workpiece and the furnace position is accurately quantified, a scientific score basis is provided for subsequent matching, and the problem of lack of quantitative standard for matching of the workpiece and the furnace area in the past is solved; based on the score matrix MatSet, the optimal furnace loading combination is generated by combining the maximum matching priority mechanism and the global heat load balancing mechanism, the position exchange is performed on the workpiece pairs in the highest and lowest heat load areas with a difference of no more than 0.1 in the matching score Scor, the balanced distribution of the global heat load is realized, and the situation of excessively high or low local heat load is avoided; the optimal furnace loading layout result set LaySet is taken as input to perform simulation, the comprehensive verification data set SimSet composed of the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd and the average furnace area heating rate balancing value Rate is obtained, the heat control effect of the furnace loading layout is verified from multiple dimensions, and the reliability of the furnace loading scheme is ensured; the comprehensive verification data set SimSet is compared with the preset process control threshold value, the furnace loading execution scheduling instruction set ComSet is generated, the furnace loading operation has a clear basis, the consistency between the actual operation and the optimization scheme is ensured, and the stability and accuracy of the heat treatment process are improved.

[0132] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for furnace selection and process control in metal processing, characterized in that: Includes the following steps: S1. Collect basic data for each workpiece to be heat-treated, obtain the workpiece heat mass load factor Wlc based on the basic data, and construct the workpiece heat load factor set WlcSet. S2. Collect equipment data in the furnace cavity, and based on the equipment data, generate the regional temperature control capability index Zon for each location in the furnace cavity, forming the furnace area temperature control capability distribution set ZonSet; S3. Map the workpiece heat load factor set WlcSet with the furnace zone temperature control capability distribution set ZonSet, and construct the workpiece-furnace zone matching score matrix MatSet based on the constructed matching score mechanism; S4. Based on the scoring matrix MatSet, combined with the maximum matching priority mechanism and the global heat load balancing mechanism, the optimal furnace loading combination is generated and integrated into the optimal furnace loading layout result set LaySet. S5. Using the optimal furnace layout result set LaySet as input, perform heat treatment simulation to obtain the temperature distribution consistency score Unif, the maximum local temperature difference value Maxd, and the average heating rate equilibrium value Rate of the furnace area, and combine them into a comprehensive verification dataset SimSet. S6. Iterate through and compare the comprehensive verification dataset SimSet with the preset process control threshold, and generate furnace loading operation instructions based on the comparison results to form the furnace allocation execution scheduling instruction set ComSet for distribution.

2. The method for heat treatment furnace preparation and process control in metal processing according to claim 1, characterized in that: S1 includes S11 and S12; S11. Collect the basic parameters of the workpiece to be heat-treated. The basic data includes: specific heat parameter Heat, mass parameter Mass, volume parameter Volu and surface area parameter Area. The basic parameters are extracted from the preset database to form a one-to-one corresponding workpiece parameter entry. The data of all workpieces to be processed are summarized to generate a workpiece thermal property data set BasSet. S12. Using the workpiece thermal property data set BasSet as input, construct the corresponding workpiece thermal mass load factor Wlc for each workpiece. The workpiece thermal mass load factor Wlc is obtained by the following calculation formula: Wlc=Heat×Mass×(Volu÷Area); After calculating all workpieces, the workpiece thermal mass load factors Wlc corresponding to all workpieces are sorted and collected to form a workpiece thermal load factor set WlcSet.

3. The method for heat treatment furnace preparation and process control in metal processing according to claim 2, characterized in that: S2 includes S21 and S22; S21. Before the heat treatment operation begins, multi-dimensional parameters are collected on the physical structure and operating status of the heat treatment furnace cavity. The collection targets include the temperature control-related performance indicators of each space area inside the furnace cavity. The temperature control-related performance indicators include: heating unit distribution density Heat, gas circulation flow rate Flow, furnace wall thermal conductivity Cond, and historical temperature control error Hist. The temperature control-related performance indicators are jointly obtained through multiple sensor nodes deployed inside the furnace cavity, the temperature control scheduling system interface, and the heat treatment historical record database, and the temperature control-related performance indicators are indexed by structured coordinates. The entire furnace cavity is then spatially modeled using the three-dimensional finite element method. The spatial modeling process is as follows: Modeling process 1: Divide the furnace cavity into several cubic unit bodies with definite volume and spatial location; Modeling process 2: Each unit is uniquely numbered and corresponds to a furnace cavity region. Each unit is a thermal behavior evaluation unit. Based on the spatial modeling process, a complete mesh model of the furnace cavity area, MeshMap, was constructed in space. Then, the temperature control-related performance indicators were summarized to form a unified data set, named the cavity equipment structure data set DevSet.

4. The method for heat treatment furnace preparation and process control in metal processing according to claim 3, characterized in that: S22. Using the DevSet set of furnace cavity equipment structure data as the basic data, perform comprehensive calculations on the regional thermal control capability of each unit and establish the Zon index, which reflects the actual temperature control performance. The Zon index, representing the regional temperature control capability, is obtained by normalizing the temperature control-related performance indicators corresponding to each unit cell and then using the following linear normalization evaluation calculation formula: Zon=α×Heat+β×Flow+γ×Cond-δ×Hist; In the formula, α, β, γ and δ represent the process weighting coefficients of heating unit distribution density Heat, gas circulation flow rate Flow, furnace wall thermal conductivity Cond and historical temperature control error His, respectively. The specific values ​​are set by the user, and α+β+γ+δ=1; Then, the Zon calculation results of the regional temperature control capability index of all units are numbered and summarized according to the spatial grid index to construct a complete furnace area temperature control capability distribution set ZonSet.

5. The method for heat treatment furnace preparation and process control in metal processing according to claim 4, characterized in that: S3 includes S31 and S32; S31. Normalize the workpiece heat load factor set WlcSet to obtain the normalized heat load index WlcNorm, and then use it together with the furnace area temperature control capability distribution set ZonSet as the input data set to construct a matching score function Fscor to evaluate the thermal compatibility between the workpiece and the furnace position, which is used to quantify the matching effect between the workpiece and the furnace position in terms of heat load distribution. The matching score function Fscor is constructed using the following formula: Scor=1-|(WlcNorm÷Zon)-1|; In the formula, Scor represents the matching score value, and WlcNorm represents the normalized workpiece thermal mass load factor.

6. The method for heat treatment furnace preparation and process control in metal processing according to claim 5, characterized in that: S32. According to the two-dimensional index of all workpiece numbers and all furnace area numbers, call the matching scoring function Fscor in sequence to calculate the matching score for each pair of workpiece number and furnace position number combination. All matching score results Scor are constructed into a two-dimensional score matrix, and the output is the workpiece and furnace zone matching score matrix MatSet. Its structure is as follows: the row dimension is the workpiece index, corresponding to the workpiece heat load factor set WlcSet, and the column dimension is the furnace zone index, corresponding to the furnace zone temperature control capability distribution set ZonSet.

7. The method for heat treatment furnace preparation and process control in metal processing according to claim 6, characterized in that: S4 includes S41; S41. Based on the scoring matrix MatSet, construct a furnace loading optimization model, and obtain the optimal furnace loading layout result set LaySet based on the furnace loading optimization model; The furnace loading optimization model is formed by combining the maximum matching priority mechanism and the global heat load balancing mechanism. The maximum matching priority mechanism selects workpiece-furnace position combinations in the scoring matrix MatSet in descending order of matching score value Scor to initially generate a furnace loading allocation scheme. The global heat load balancing mechanism calculates the total normalized heat load of the workpieces assigned to each furnace zone, identifies the highest and lowest heat load zones as heat distribution deviation areas, and selects workpiece pairs with a matching score difference of no more than 0.1 from these two zones, swaps their positions, and iterates until the heat load difference no longer decreases. The total cumulative normalized heat load represents the sum of the normalized heat load index WlcNorm for all assigned workpieces in each furnace zone.

8. The method for heat treatment furnace preparation and process control in metal processing according to claim 5, characterized in that: S5 includes S51; S51. Using the optimal furnace layout result set LaySet as input data, simulation is performed based on the discrete temperature field calculation method of the steady-state heat conduction model. The workpiece arrangement information in the optimal furnace layout result set LaySet is mapped to the corresponding furnace area number to a three-dimensional discrete coordinate system. Then, the temperature control capability Zon of each furnace area is used as the basic heat input intensity, and the normalized heat load index WlcNorm is used as the heat absorption characteristic to establish a heat balance relationship. Based on the steady-state heat conduction calculation model, the temperature response value of each discrete coordinate node is calculated. Finally, during the heat preservation stage, all temperature response values ​​were extracted, and the following three thermal control evaluation indicators were obtained based on the temperature response values: temperature distribution consistency score Unif, maximum local temperature difference Maxd, and furnace area average heating rate equilibrium value Rate, which were then combined into a comprehensive validation dataset SimSet. The temperature distribution consistency score Unif is obtained by statistically analyzing the average deviation of all temperature nodes from the preset target temperature and normalizing it into a consistency score. The maximum local temperature difference value Maxd is obtained by extracting the difference between the maximum and minimum values ​​from all temperature nodes, and represents the local temperature difference extreme value; The average heating rate equilibrium value (Rate) of the furnace area is obtained by comparing the differences in heating rate based on the temperature growth curves of each furnace area during the heating stage, and then normalized into a consistency score.

9. A method for heat treatment furnace preparation and process control in metal processing according to claim 8, characterized in that: S6 includes S61; S61. Using the comprehensive verification dataset SimSet as input, iterate through the comprehensive verification dataset SimSet and compare it with the preset process control threshold. Based on the comparison results, generate furnace loading operation instructions to form the furnace matching execution scheduling instruction set ComSet. Then, issue instructions to relevant operators based on the furnace matching execution scheduling instruction set ComSet.

10. A method for heat treatment furnace preparation and process control in metal processing according to claim 9, characterized in that: The preset process control thresholds include the temperature distribution consistency scoring standard threshold ValUnif, the maximum local temperature difference allowable value threshold ValMaxd, and the heating rate equalization scoring standard threshold ValRate. The furnace loading operation command is generated through the following comparison process: Comparison Result 1: Temperature distribution consistency score Unif ≥ Temperature distribution consistency score standard threshold ValUnif; Comparison Result 2: Maximum local temperature difference value Maxd ≤ Maximum local temperature difference threshold ValMaxd; Comparison Result 3: The average heating rate equilibrium value of the furnace area (Rate) is greater than or equal to the threshold value (ValRate) of the heating rate equilibrium scoring standard. When all three comparison results are true, a furnace loading operation instruction is generated, and the contents of the optimal furnace loading layout result set LaySet are converted into structured scheduling instructions, forming a furnace loading execution scheduling instruction set ComSet.

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