CN111008475A - Hobbing carbon consumption model solving method based on chaos Henry gas solubility optimizer - Google Patents

Abstract

The invention discloses a hobbing carbon consumption model solving method based on a chaos Henry gas solubility optimizer, which comprises the following steps of: (1) a chaotic Henry gas solubility optimizer is realized; (2) and solving a hobbing carbon consumption model based on the chaotic Henry gas solubility optimizer. The invention has the following advantages: 1. the chaotic Henry gas solubility optimization algorithm is proposed for the first time: the chaotic mapping method is integrated into a Henry gas solubility optimization algorithm to generate a chaotic Henry gas solubility optimizer, so that the performance is more excellent; 2. synchronously improving hobbing processing parameters and carbon consumption: and continuously optimizing hobbing processing parameters by using a chaotic Henry gas solubility optimizer, and evaluating by using a carbon consumption model to enable the hobbing processing parameters and the carbon consumption to simultaneously reach an approximately optimal state. Thereby solving the difficult problem of solving the carbon consumption of the hobbing.

Description

Hobbing carbon consumption model solving method based on chaos Henry gas solubility optimizer

Technical Field

The invention relates to a hobbing carbon consumption model solving method based on a chaotic Henry gas solubility optimizer, and belongs to the technical field of gear machining.

Background

In actual hobbing, after a process worker establishes a hobbing carbon consumption model, the model is difficult to solve, time consumption for manually solving machining parameters is high, errors are easy to occur, and great influence is caused on production efficiency and carbon consumption optimization.

The solution research on the carbon consumption in the hobbing process is less in China, so that the chaotic Henry gas solubility optimizer is integrated into the hobbing carbon consumption solution, so that the processing parameters are obtained and the processing is guided, and the research on the aspect is deficient at present.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a hobbing carbon consumption model solving method based on a chaos Henry gas solubility optimizer, which can effectively avoid conservative problems caused by manual solving and achieve the purpose of reducing the carbon consumption.

The invention mainly adopts the technical scheme that:

a solution method of a hobbing carbon consumption model based on a chaos Henry gas solubility optimizer is used for solving hobbing parameters of a known hobbing carbon consumption model according to the following steps, and specifically comprises the following steps:

step 1: a chaotic Henry gas solubility optimizer is realized;

step 1.1 (initialization): henry gas population HAP is expressed as { X }₁,X₂,···,X_nN is a positive integer, X_iRepresenting the ith gas in the population, which may contain 1 or more attributes, i.e., X_i＝{x_i,1,x_i,2,···,x_i,mM is the number of gas attributes which is a positive integer, and X is input_iThe value range of each attribute, the HAP is randomly initialized within the value range, the number of gas species num _ gt and the coefficient a are set₁＝0.05，a₂＝100，a₃0.01, a Henry type coefficient H_j(t)＝a₁Rand (), t denotes an iteration count variable, t is initialized to 1, 0<j<num _ gt, j is a positive integer, gas X_iPressure P of_i,j＝a₂Rand (), coefficient C_j＝a₃Rand () which is a function capable of generating a random number between 0 and 1, with a maximum number of iterations mlter;

step 1.2 (classification): the Henry gas population HAP is divided into num _ gt clusters, the number of gases in the clusters is approximately equal, and the same Henry gas is used for each clusterType coefficient H_j；

Step 1.3 (evaluation): evaluating each cluster by using a mathematical model for solving the target, and sequencing the gases to obtain the optimal gas X in each cluster_j,bestAnd globally optimal gas X in Henry gas population HAP_best；

Step 1.4: if t < mIter, go to step 1.5; otherwise, go to step 1.10;

step 1.5 (update henry type coefficient, solubility, gas location): combining chaotic mapping, updating the Henry type coefficient by using a formula (3), updating the gas solubility by using a formula (2), and updating the position of the gas by using a formula (1);

where r represents a random real number between 0 and 1, α is the influence of other gases, α is 1, β is a constant, β is 1, and F is_i,j(t) represents the function value of the ith gas in the jth cluster in the tth generation, calculated by a mathematical model, F_best(t) represents the optimum gas X at the t-th generation_bestCorresponding function value, exp () representing an exponential function with the natural constant e as base, S_i,j(t) represents solubility, calculated from formulas (2) and (3);

in the formula, T^θ298.15, chao (t) represents a tent chaotic mapping function, and is calculated by using a formula (4), wherein the initial value chao (1) of chao (t) is 0.7;

step 1.6 (escape local optima): arranging and selecting the worst gas number by using a formula (5);

N_w＝n·(rand(0.1)+0.1) (5)

step 1.7 (update worst gas position): updating the location of the worst gas using equation (6);

G_k＝G_min+r·(G_max-G_min) (6)

in the formula, G_kIs the worst gas position, 0<k<N_wK is a positive integer, G_minAnd G_maxIs the value range of the position.

Step 1.8 (reevaluation): reevaluating each cluster using a solution objective mathematical model and ordering the gases to update X_j,bestAnd X_best；

Step 1.9: t +1, go to step 1.4;

step 1.10: output X_best。

Step 2: solving a hobbing carbon consumption model;

obtaining a hobbing carbon consumption model, and solving by using a chaotic Henry gas solubility optimizer, wherein the method comprises the following specific steps of:

step 2.1: setting a hobbing parameter range, generating n hobbing parameters in the parameter range, and forming a hobbing parameter population which corresponds to the Henry gas population HAP one by one;

step 2.2: using a hobbing carbon consumption model as a mathematical model to evaluate the quality of hobbing parameters, and removing corresponding hobbing processing parameters when the constraint conditions are not met;

step 2.3: step 1 is operated, num _ gt and mIter are input, and X is obtained_bestNamely the optimal hobbing processing parameter, and the corresponding carbon consumption value is the optimal carbon consumption.

Has the advantages that: the invention provides a hobbing carbon consumption model solving method based on a chaos Henry gas solubility optimizer, which has the following advantages:

1. the chaotic Henry gas solubility optimization algorithm is proposed for the first time: the chaotic mapping method is integrated into a Henry gas solubility optimization algorithm to generate a chaotic Henry gas solubility optimizer, so that the performance is more excellent;

2. synchronously improving hobbing processing parameters and carbon consumption: and continuously optimizing hobbing processing parameters by using a chaotic Henry gas solubility optimizer, and evaluating by using a carbon consumption model to enable the hobbing processing parameters and the carbon consumption to simultaneously reach an approximately optimal state. Thereby solving the difficult problem of solving the carbon consumption of the hobbing.

Drawings

FIG. 1 is a schematic diagram of a solution process for a chaotic Henry gas solubility optimizer-based hobbing carbon consumption model in an embodiment of the present invention;

FIG. 2 is a graphical representation of ten test value distributions of gear hobbing parameters and carbon consumption targets in accordance with an embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

As shown in fig. 1, a solution method for a hobbing carbon consumption model based on a chaos henry gas solubility optimizer is used for solving hobbing parameters of a known hobbing carbon consumption model according to the following steps:

step 1: a chaotic Henry gas solubility optimizer is realized;

step 1.1 (initialization): henry gas population HAP is expressed as { X }₁,X₂,···,X_nN is a positive integer, X_iRepresenting the ith gas in the population, which may contain 1 or more attributes, i.e., X_i＝{x_i,1,x_i,2,···,x_i,mM is gas genusThe number of sex is a positive integer, and X is input_iThe value range of each attribute, the HAP is randomly initialized within the value range, the number of gas species num _ gt and the coefficient a are set₁＝0.05，a₂＝100，a₃0.01, a Henry type coefficient H_j(t)＝a₁Rand (), t denotes an iteration count variable, t is initialized to 1, 0<j<num _ gt, j is a positive integer, gas X_iPressure P of_i,j＝a₂Rand (), coefficient C_j＝a₃Rand () which is a function capable of generating a random number between 0 and 1, with a maximum number of iterations mlter;

step 1.2 (classification): the Henry gas population HAP is divided into num _ gt clusters, the number of gases in the clusters is approximately equal, and each cluster uses the same Henry type coefficient H_j；

Step 1.3 (evaluation): evaluating each cluster by using a mathematical model for solving the target, and sequencing the gases to obtain the optimal gas X in each cluster_j,bestAnd globally optimal gas X in Henry gas population HAP_best；

Step 1.4: if t < mIter, go to step 1.5; otherwise, go to step 1.10;

step 1.5 (update henry type coefficient, solubility, gas location): combining chaotic mapping, updating the Henry type coefficient by using a formula (3), updating the gas solubility by using a formula (2), and updating the position of the gas by using a formula (1);

where r represents a random real number between 0 and 1, α is the influence of other gases, α is 1, β is a constant, β is 1, and F is_i,j(t) represents the function value of the ith gas in the jth cluster in the tth generation, calculated by a mathematical model, F_best(t) represents the optimum gas X at the t-th generation_bestThe value of the corresponding function is calculated,exp () represents an exponential function with a natural constant e as the base, S_i,j(t) represents solubility, calculated from formulas (2) and (3);

S_i,j(t)＝H_j(t+1)·P_i,j(t) (2)

in the formula, T^θ298.15, chao (t) represents a tent chaotic mapping function, and is calculated by using a formula (4), wherein the initial value chao (1) of chao (t) is 0.7;

step 1.6 (escape local optima): arranging and selecting the worst gas number by using a formula (5);

N_w＝n·(rand(0.1)+0.1)(5)

step 1.7 (update worst gas position): updating the location of the worst gas using equation (6);

G_k＝G_min+r·(G_max-G_min) (6)

in the formula, G_kIs the worst gas position, 0<k<N_wK is a positive integer, G_minAnd G_maxIs the value range of the position.

Step 1.8 (reevaluation): reevaluating each cluster using a solution objective mathematical model and ordering the gases to update X_j,bestAnd X_best；

Step 1.9: t +1, go to step 1.4;

step 1.10: output X_best。

Step 2: solving a hobbing carbon consumption model;

obtaining a hobbing carbon consumption model, and solving by using a chaotic Henry gas solubility optimizer, wherein the method comprises the following specific steps of:

step 2.1: setting a hobbing parameter range, generating n hobbing parameters in the parameter range, preferably setting n to 35, and forming a hobbing parameter population which corresponds to the Henry gas population HAP one by one;

step 2.2: using a hobbing carbon consumption model as a mathematical model to evaluate the quality of hobbing parameters, and removing corresponding hobbing processing parameters when the constraint conditions are not met;

step 2.3: running step 1, inputting num _ gt and mIter, preferably setting num _ gt to 5 and mIter to 500, and obtaining X_bestNamely the optimal hobbing processing parameter, and the corresponding carbon consumption value is the optimal carbon consumption.

Example (b):

one-time feeding certain-time gear hobbing, wherein the gear hobbing parameters comprise the rotating speed s of the main shaft₁(r/min) and feed quantity s₂(mm/r), the carbon loss model for this hobbing is known as:

solving for hobbing carbon consumption according to the specific steps of the invention, s_1min＝351.5r/min、s_1max＝388.5r/min、s_2min＝1.66mm/r、s_2max1.83mm/r, radius of cutting edge r_a0.4mm, roughness threshold [ Ra]3.2 μm, cut-in stroke d_in24.846mm, tooth width d_w13mm, cutting stroke d_out4.861mm, number of gear teeth z₁41, number of hob heads z₀1, cutting time threshold [ time]2 s. Obtaining the hobbing parameters of (388.5 r/min,1.83 mm/r) and the cutting carbon consumption of 0.0303kgCO₂。

In order to verify the stability of the method, the operation is carried out for ten times, the hobbing parameters and the carbon consumption target distribution diagram are obtained, and as shown in FIG. 2, no outlier appears, so that the method is proved to be extremely stable.

As can be seen from the result data, the method provided by the invention has good effects in the aspects of feasibility and stability. Therefore, the method provided by the invention can obtain a good solution when solving the problem of hobbing carbon consumption.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (1)

1. A method for solving a hobbing carbon consumption model based on a chaos Henry gas solubility optimizer is characterized in that the known hobbing carbon consumption model is solved according to the following steps, and the specific steps are as follows:

step 1: a chaotic Henry gas solubility optimizer is realized;

step 1.1: initialization

Henry gas population HAP is expressed as { X }₁,X₂,···,X_nN is a positive integer, X_iRepresenting the ith gas in the population, the gas containing 1 or more properties, i.e. X_i＝{x_i,1,x_i,2,···,x_i,mM is the number of gas attributes which is a positive integer, and X is input_iThe value range of each attribute, the HAP is randomly initialized within the value range, the number of gas species num _ gt and the coefficient a are set₁＝0.05，a₂＝100，a₃0.01, a Henry type coefficient H_j(t)＝a₁Rand (), t denotes an iteration count variable, t is initialized to 1, 0<j<num _ gt, j is a positive integer, gas X_iPressure P of_i,j＝a₂Rand (), coefficient C_j＝a₃Rand () which is a function capable of generating a random number between 0 and 1, with a maximum number of iterations mlter;

step 1.2: classification

The Henry gas population HAP is divided into num _ gt clusters, and the number of gases in the clusters is nearly equalEach cluster using the same Henry type coefficient H_j；

Step 1.3: evaluation of

Evaluating each cluster by using a mathematical model for solving the target, and sequencing the gases to obtain the optimal gas X in each cluster_j,bestAnd globally optimal gas X in Henry gas population HAP_best；

Step 1.4: if t < mIter, go to step 1.5; otherwise, go to step 1.10;

step 1.5: updating Henry type coefficient, solubility, gas location

Combining chaotic mapping, updating the Henry type coefficient by using a formula (3), updating the gas solubility by using a formula (2), and updating the position of the gas by using a formula (1);

where r represents a random real number between 0 and 1, α is the influence of other gases, α is 1, β is a constant, β is 1, and F is_i,j(t) represents the function value of the ith gas in the jth cluster in the tth generation, calculated by a mathematical model, F_best(t) represents the optimum gas X at the t-th generation_bestCorresponding function value, exp () representing an exponential function with the natural constant e as base, S_i,j(t) represents solubility, calculated from formulas (2) and (3);

S_i,j(t)＝H_j(t+1)·P_i,j(t) (2)

in the formula, T^θ298.15, chao (t) represents a tent chaotic mapping function, and is calculated by using a formula (4), wherein the initial value chao (1) of chao (t) is 0.7;

step 1.6: escape local optimality

Arranging and selecting the worst gas number by using a formula (5);

N_w＝n·(rand(0.1)+0.1) (5)

step 1.7: updating worst gas position

Updating the location of the worst gas using equation (6);

G_k＝G_min+r·(G_max-G_min) (6)

in the formula, G_kIs the worst gas position, 0<k<N_wK is a positive integer, G_minAnd G_maxIs the value range of the position;

step 1.8: reevaluation

Reevaluating each cluster using a solution objective mathematical model and ordering the gases to update X_j,bestAnd X_best；

Step 1.9: t +1, go to step 1.4;

step 1.10: output X_best；

Step 2: solving a hobbing carbon consumption model;

obtaining a hobbing carbon consumption model, and solving by using a chaotic Henry gas solubility optimizer, wherein the method comprises the following specific steps of:

step 2.1: setting a hobbing parameter range, generating n hobbing parameters in the parameter range, and forming a hobbing parameter population which corresponds to the Henry gas population HAP one by one;

step 2.2: using a hobbing carbon consumption model as a mathematical model to evaluate the quality of hobbing parameters, and removing corresponding hobbing processing parameters when the constraint conditions are not met;

step 2.3: step 1 is operated, num _ gt and mIter are input, and X is obtained_bestNamely the optimal hobbing processing parameter, and the corresponding carbon consumption value is the optimal carbon consumption.

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