Alloy manufacturing method based on robust optimization algorithm

Abstract

A method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention may include: (a) a step of inputting initial data values ​​required for each finished alloy product; (b) a step of determining whether production of a target finished alloy product is possible by executing a robustness optimization model considering the case where the value representing the uncertainty of recycled scrap metal is at its maximum; (c) a step of, if production of the target finished alloy product is possible, resetting the value representing the uncertainty of the recycled scrap metal to a predetermined value (Θ), executing a robustness optimization model to check the result value of the input amount of each raw material, and then inputting an amount of each raw material corresponding to a part of the input amount result value into a furnace; (d) a step of performing an intermediate inspection of the alloy; (e) a step of determining whether the target production quantity is satisfied; and (f) a step of determining whether the alloy satisfies the good product condition when the target production quantity is satisfied, and terminating the alloy production process when the good product condition is satisfied.

Description

Technology Field

[0001] The present invention relates to a method for manufacturing an alloy using recycled scrap metal, and more specifically, to a method for manufacturing an alloy based on a robustness optimization algorithm. Background Technology

[0002] Aluminum alloys are used in various fields, such as automobiles and home appliances. The production of such aluminum has adverse effects on the environment, including acidification and greenhouse gas emissions. As such, while aluminum alloys are useful materials used in the manufacturing processes of various products, they pose a problem in that they cause environmental pollution due to the large-scale emission of carbon and other substances during the primary production process of aluminum.

[0003] To address this, a method of producing aluminum alloys by recycling aluminum scrap is employed. Producing aluminum alloys by recycling aluminum scrap can save energy and resolve environmental pollution issues such as acidification and greenhouse gas emissions.

[0004] Recycled scrap metal exhibits variability in composition and impurities, which can lead to product quality issues in alloy manufacturing processes, such as for aluminum alloys. Minimizing and controlling these variability factors is a critical task for the successful operation of aluminum alloy processes. Prior art literature

[0005] (Patent Document 0001) KR 10-2139358 B1 The problem to be solved

[0006] The present invention was devised as a result of research on the 3rd stage of the Industry-Academic Cooperation Leading University Development Project (LINC3.0), and the objective of the present invention is to provide a method for manufacturing an alloy based on a robustness optimization algorithm.

[0007] The objective of the present invention is to solve the above-mentioned problems by providing an alloy manufacturing method based on a robust optimization algorithm that can minimize production costs while satisfying the component ratio of the finished product in an alloy process where variability exists in the raw materials.

[0008] Furthermore, the objective of the present invention is to provide an alloy manufacturing method based on a robust optimization algorithm that can automatically determine the input amount of raw materials without specialized knowledge when only data on the finished product and raw materials is provided, thereby solving the problem of the existing inefficient raw material input method that requires repeating the same process until the component ratio is satisfied, and can cope with problems occurring on-site by flexibly adjusting the number of operations.

[0009] Furthermore, the objective of the present invention is to provide an alloy manufacturing method based on a robust optimization algorithm that resolves uncertainties arising during the process of producing aluminum alloys using recycled scrap metal through mathematical modeling and presents an optimized algorithm, thereby improving upon the conventional situation where the amount of raw materials input had to be determined solely by the experience of skilled workers; enabling the determination of the amount of raw materials input for intermediate processes without professional experience or know-how, provided only the raw material data of the finished product is given, and preventing process operational failures and production delays even in the event of unavoidable worker replacement.

[0010] In addition, the objective of the present invention is to provide a method for manufacturing an alloy based on a robust optimization algorithm that can help determine optimal conditions to reduce total production costs, which can help reduce raw material loss caused by discarding some molten material or excessively adding pure material when a person manually controls the process operation in the past. means of solving the problem

[0011] An alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention is,

[0012] (a) A step of inputting initial data values ​​required for each alloy finished product;

[0013] (b) a step of determining whether production of the target alloy finished product is possible by running a robust optimization model considering the case where the value representing the uncertainty of the recycled scrap metal is maximized;

[0014] (c) If the production of the target alloy finished product is possible, the value representing the uncertainty of the recycled scrap metal is reset to a predetermined value (Θ), and after verifying the result value of the input amount of each raw material by running a robust optimization model, the amount of each raw material corresponding to a part of the input amount result value is input into the furnace;

[0015] (d) A step of performing an intermediate inspection of the alloy;

[0016] (e) a step of determining whether the target production volume is met; and

[0017] (f) When the target production volume is met, the process may include a step of determining whether the alloy meets the good product conditions, and if the good product conditions are met, terminating the alloy production process.

[0018] In a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, if the target production quantity is not met or the good product condition is not met, and if the number of repetitions is less than or equal to (total number of inputs (Num)-1), the data value is updated and steps (b) to (d) can be repeated.

[0019] In addition, the alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention may further include the step of executing a robustness optimization model when steps (b) to (d) are repeated (total number of inputs (Num)-1), considering the case where the target production volume is not met or the good product condition is not met, and the value representing the uncertainty of recycled scrap metal is at its maximum, thereby determining the input amount of each raw material, and inputting the determined input amount of each raw material into a blast furnace.

[0020] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, in the step of introducing an amount of each raw material corresponding to a part of the input amount result value into the furnace in step (c), an amount of (input amount result value × (100 × Θ)%) may be introduced into the furnace.

[0021] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the initial data values ​​may include the minimum and maximum values ​​of the raw material composition ratio for each raw material, the inventory quantity of each raw material which is the quantity that can be input on-site during the production process, and the total number of inputs (Num).

[0022] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the value representing the uncertainty is a first budget parameter ( ) and the second budget parameter( Includes ),

[0023] The above first budget parameter ( ) is a budget parameter for the loss rate, which represents the uncertainty related to the loss of raw materials that may occur as raw materials are lost in the production process and decrease in the total input amount.

[0024] The above second budget parameter ( ) may be a budget parameter for the raw material composition ratio, which represents the uncertainty related to the content of the raw material's composition arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material.

[0025] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the first budget parameter ( ) and the second budget parameter ( The values ​​of ) are real values ​​that take values ​​between 0 and 1.0 respectively, and

[0026] When the value representing the uncertainty of the above recycled scrap metal is at its maximum, the above first budget parameter ( ) and the second budget parameter ( This is the case where the value of ) is 1.0, and

[0027] The above predetermined value (Θ) may be a real value that takes a value between 0 and 1.0.

[0028] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, in step (b) of determining whether the production of the target alloy finished product is possible, the robustness optimization model is executed, and if it is feasible, the production process is carried out, and if it is not feasible, the production process is stopped.

[0029] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the robustness optimization model determines the result value of the input amount of each raw material based on Equation 8, and

[0030] [Mathematical Formula 8]

[0031]

[0032] The above raw materials include N types of raw materials from i=1 to N, where Xi represents the input amount of raw material i and Ci represents the cost of raw material i.

[0033] Xi is limited by mathematical formulas 9 and 10, and

[0034] [Mathematical Formula 9]

[0035]

[0036] [Mathematical Formula 10]

[0037]

[0038] Q represents the capacity of the blast furnace, R represents the amount of casting in the furnace based on quality inspection results, and I is a set of raw materials where I={1, 2, Representing , N}, represents the inventory quantity of raw material i, and It can represent the amount of raw material i used up to the quality inspection result.

[0039] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the good product condition is based on Equation 11 and Equation 12 for meeting the minimum and maximum conditions of the constituent component ratios included in the finished alloy product, and

[0040] [Mathematical Formula 11]

[0041]

[0042] [Mathematical Formula 12]

[0043]

[0044] represents the central value of the loss rate by raw material, and

[0045] represents the first budget parameter for the loss rate, which signifies the uncertainty related to the loss of raw materials that may occur as a result of raw materials being lost in the production process and decreasing the total input amount.

[0046] represents a second budget parameter for the raw material composition ratio, which signifies the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material, and

[0047] represents the maximum variation in the loss rate of raw material i, and

[0048] represents the central value of the raw material composition ratio, and j is And, J is the set of components of the finished product, J={1, 2, , M} and,

[0049] represents the minimum proportion of component j in the finished product, and

[0050] represents the maximum proportion of component j in the finished product, and

[0051] Y can represent the target production quantity.

[0052] In addition, in an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention, the target production quantity (Y) is determined by Equation 13 and Equation 14, and

[0053] [Mathematical Formula 13]

[0054]

[0055] [Mathematical Formula 14]

[0056]

[0057] D can represent the production requirement.

[0058] In addition, in a method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention, the alloy may include one alloy selected from the group comprising aluminum alloy, copper alloy, iron alloy, nickel alloy, titanium alloy, tin alloy, and zinc alloy. Effects of the invention

[0061] As described above, according to the alloy manufacturing method based on a robust optimization algorithm of one embodiment of the present invention, the uncertainty arising during the process of producing aluminum alloys using recycled scrap metal can be resolved through mathematical modeling, and an optimized algorithm can be presented. In other words, while conventionally the amount of raw material input had to be determined solely by relying on the experience of skilled workers, this situation is improved so that if only the raw material data of the finished product is provided, the amount of raw material input for the intermediate process can be determined without professional experience or know-how, and process operation disruptions and production delays can be prevented even in the event of unavoidable worker replacement.

[0062] In addition, according to the alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention, a robustness optimization algorithm is applied to solve the problem of the existing inefficient raw material input method, which requires repeating the same process until the component ratio is satisfied. As a result, not only can the number of operations be flexibly adjusted, but problems occurring on-site can also be addressed more accurately.

[0063] In addition, according to the alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention, raw material loss occurred in the past when a person manually controlled the process operation, such as discarding some of the molten material or excessively adding pure material, but the robustness optimization algorithm can help determine the optimal conditions to reduce total production costs.

[0064] In addition, according to the alloy manufacturing method based on a robust optimization algorithm according to one embodiment of the present invention, if the range of variability, namely the maximum and minimum values, is known, the uncertainty arising from the difficulty in accurately determining the ratio of metal components constituting the raw material due to the characteristics of the recycling process can be addressed by determining optimal process conditions that reflect the uncertainty.

[0065] Meanwhile, the effects obtainable from the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present invention belongs from the description below. Brief explanation of the drawing

[0067] FIG. 1 is a flowchart of an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention. FIG. 2 is a block diagram of an apparatus in which an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention can be performed. Specific details for implementing the invention

[0068] The objects, specific advantages, and novel features of the present invention will become more apparent from the following detailed description and preferred embodiments in conjunction with the accompanying drawings. In this case, identical components in each drawing are denoted by the same reference numerals whenever possible. Additionally, detailed descriptions of already known functions and / or configurations are omitted.

[0069] The contents disclosed below focus on the parts necessary for understanding the operation according to various embodiments, and omit descriptions of elements that may obscure the gist of the explanation. Additionally, some components of the drawings may be exaggerated, omitted, or depicted schematically. The size of each component does not entirely reflect its actual size, and therefore, the contents described herein are not limited by the relative sizes or spacing of the components depicted in each drawing.

[0070] In describing the embodiments of the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such detailed descriptions may unnecessarily obscure the essence of the invention. Furthermore, the terms described below are defined in consideration of their functions within the present invention, and these may vary depending on the intentions or practices of the user or operator. Therefore, such definitions should be based on the content throughout this specification. Terms used in the detailed description are intended merely to describe the embodiments of the present invention and should not be limiting in any way. Unless explicitly stated otherwise, expressions in the singular form include the meaning of the plural form. In this description, expressions such as "include" or "comprise" are intended to refer to certain characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof, and should not be interpreted to exclude the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof other than those described.

[0071] Additionally, terms such as first, second, etc., may be used to describe various components, but said components are not limited by said terms, and said terms are used only for the purpose of distinguishing one component from another.

[0072] Hereinafter, with reference to the attached drawings, a method for manufacturing an alloy based on a robustness optimization algorithm according to an embodiment of the present invention will be described.

[0073] In an alloy manufacturing method based on a robust optimization algorithm according to one embodiment of the present invention, the problem of optimizing the amount of raw materials input in the aluminum alloy manufacturing process of Company C, a domestic aluminum alloy manufacturer, is addressed. Company C is a company that manufactures aluminum alloys using recycled scrap metal and produces various types of aluminum alloys, including its main product, ALDC 12.x. Company C removes dirt and impurities from the scrap metal, melts it in a blast furnace, and then measures the component content of each metal using a spectrum analyzer. Subsequently, the company inspects whether the resulting product meets the compositional ratio of the finished product; if it meets this ratio, the product is produced, and if it does not, additional raw materials are input to meet the ratio. The compositional ratio of the finished product is given as a range, and if it falls outside this range, it is judged as defective. A daily target production volume is given, and production must exceed the target volume. Additionally, due to limitations on the size of the blast furnace, the total input volume must be smaller than the furnace capacity.

[0074] This process faces issues regarding the variability of the loss rate—the proportion of the total input lost due to burning or dust during melting in the furnace—and the composition ratio of the raw materials. Since the product's raw material is recycled scrap metal, these two factors cannot be accurately identified, and due to this variability, different results are produced even with the same input amount. The precise distribution of these two variables is unknown, and the range of variation, represented by minimum and maximum values, is assigned to each raw material. For this reason, in actual production sites, the target production volume is not input at once; instead, only a portion is input, followed by a compositional inspection to determine the next input amount. This process is repeated until both the target production volume and the product composition ratio are satisfied.

[0075] ALDC12.x, the main product, consists of 12 components including copper, silicon, magnesium, zinc, iron, manganese, nickel, titanium, lead, tin, chromium, and aluminum, and is generally added twice. Since the decision regarding the addition of these components is based on the subjective judgment of experts, there are instances where some castings are discarded or pure materials are added excessively in order to match the component ratios of the finished product. Furthermore, fluctuations in raw material market prices may lead to an increase in the unit price of the product.

[0076] The present invention can provide a method for manufacturing an alloy based on a robust optimization algorithm that determines the optimal amount of raw material input to minimize production costs by simultaneously considering the loss rate of raw materials and the variability of the compositional ratio of raw materials without undergoing a separate process.

[0077] An alloy manufacturing method based on a robust optimization algorithm according to one embodiment of the present invention addresses the problem of optimizing the amount of raw materials input for producing ALDC12.x, a major product of Company C. The objective of this problem is to minimize the cost of producing aluminum alloys in an automated manner without the assistance of experts, and constraints such as minimum production quantity constraints, raw material inventory quantity constraints, input quantity constraints based on furnace size, and the composition ratio of the finished product may be considered. The parameters used for the mathematical modeling of this problem are as shown in Table 1.

[0078]

[0079] The mathematical model for this problem can be expressed as follows.

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Equation 1 is the objective function, aiming to minimize production costs associated with raw material input. Equation 2 represents the determination of production quantity, where variable Y signifies the final production quantity considering the loss rate of each raw material. Equations 3 and 4 represent input quantity constraints based on the component ratios of the products. Since the component ratios are given as a range, the constraint on the component content of the finished product based on the target production quantity is also given as a range. Equation 5 represents the raw material input quantity constraint. The total input of raw materials cannot exceed the furnace capacity. Equation 6 represents the constraint on the target production quantity. The final production quantity, expressed by variable Y, must be greater than or equal to the target production quantity of the finished product. Equation 7 represents the input quantity constraint based on the inventory quantity of raw materials. Since each raw material has a limited inventory quantity, input cannot exceed the inventory amount. The problem can be modeled in this form.

[0088] However, since Li and Wij in the model are coefficients with variability, this form cannot be practically applied to Company C's problem. To solve this, an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention presents a robustness optimization-based algorithm.

[0089] A model that optimizes for the worst-case scenario that may occur using a min-max objective function has been developed under the name of robust optimization. Compared to existing scenario-based probabilistic programming methods, this robust optimization has the advantage that the scale of the problem does not increase with the number of uncertain parameters. Therefore, it can handle various results that occur depending on uncertain data. Accordingly, in an alloy manufacturing method based on a robust optimization algorithm according to an embodiment of the present invention, the robust optimization method is utilized to optimize the amount of raw materials input in the aluminum alloy manufacturing process of Company C.

[0090] FIG. 1 is a flowchart of an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention, and FIG. 2 is a block diagram of an apparatus (200) in which the alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention can be performed.

[0091] In the alloy manufacturing method based on a robust optimization algorithm according to one embodiment of the present invention illustrated in FIG. 1, it is assumed that an aluminum alloy is manufactured. However, the present invention is not limited thereto and can be used to manufacture various alloys such as aluminum alloys, copper alloys, iron alloys, nickel alloys, titanium alloys, tin alloys, or zinc alloys.

[0092] Referring to FIG. 1, an alloy manufacturing method based on a robust optimization algorithm according to an embodiment of the present invention comprises the steps of: inputting initial data values ​​required for each aluminum alloy finished product (step S100); determining and setting a total input count Num (step S102); setting a variable n representing the number of iterations to 1 (step S104); setting a budget parameter to a maximum value to account for the case where the value representing the uncertainty of recycled scrap metal is at its maximum (step S106); executing a robust optimization model using the budget parameter set to a maximum value (step S108); determining whether production of a target aluminum alloy finished product is possible under the worst-case uncertainty condition (step S110); if production of the target aluminum alloy finished product is possible, resetting the value representing the uncertainty of the recycled scrap metal to a predetermined value (Θ) (step S114); executing a robust optimization model to determine the input amount of each raw material using the budget parameter reset to the predetermined value (Θ) (step S116); and checking the result value of the input amount of each raw material (step S118), a step of feeding an amount of each raw material corresponding to a part of the input amount result value into the furnace (step S120), a step of performing an intermediate inspection of the aluminum alloy (step S122), a step of determining whether the target production quantity is met (step S124), if the target production quantity is met, a step of determining whether the aluminum alloy satisfies the composition constraint (good product condition) (step S126), a step of terminating the aluminum alloy production process if the composition constraint is met, if the target production quantity is not met or the composition constraint is not met, a step of determining whether steps S106 to S122 have been repeated (set total input count (N)-1) times (step S128), a step of updating data if steps S106 to S122 have not been repeated (set total input count (N)-1) times (step S130),The method may include a step of increasing variable n by 1 and proceeding to step S106 (step S132); a step of setting the budget parameter to a maximum value to account for cases where the value representing the uncertainty of recycled scrap metal is maximum, such as when the target production volume is not met or the component constraint is not met, and when steps S106 through S122 are repeated (total number of inputs (N)-1) times (step S134); and a step of executing a robust optimization model with the maximum budget parameter to determine the input amount of each raw material to be input last, and inputting the determined input amount of each raw material into the furnace (step S136). If the production of the target aluminum alloy finished product in step S110 is impossible, the process may be terminated.

[0093] Meanwhile, in the method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention shown in FIG. 1, steps related to control can be performed by the device (200) shown in FIG. 2.

[0094] An apparatus (200) capable of performing an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention illustrated in FIG. 2 may include at least one processor (202), a memory (204), an input / output interface (206), and a network communication interface (208).

[0095] The processor (202) can perform steps related to control in an alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention shown in FIG. 1.

[0096] The memory (204) stores instructions that allow the processor (202) to perform control-related steps in an alloy manufacturing method based on a robust optimization algorithm, the input / output interface (206) can provide input and output functions, and the network communication interface (208) can provide a function to communicate with a network.

[0097] A method for manufacturing an alloy based on a robustness optimization algorithm according to one embodiment of the present invention configured as described above will be explained in detail below.

[0098] First, we will explain the mathematical modeling of the robustness optimization algorithm used in the alloy manufacturing method based on the robustness optimization algorithm according to one embodiment of the present invention.

[0099] In the robust optimization algorithm used in the alloy manufacturing method based on the robust optimization algorithm according to one embodiment of the present invention, a robust linear programming model with coefficient variability can be designed by using a parameter called a budget parameter. The budget parameter can be represented as shown in [Table 2].

[0100]

[0101] Meanwhile, in the alloy manufacturing method based on a robustness optimization algorithm according to one embodiment of the present invention, a linear equation suitable for the problem to be solved can be expressed as the following mathematical equations.

[0102] The robust optimization model (RO Model) can determine the result value of the input amount of each raw material based on mathematical equation 8.

[0103]

[0104] The above raw materials include N types of raw materials from i=1 to N, where Xi represents the input amount of raw material i and Ci represents the cost of raw material i.

[0105] Xi can be limited by mathematical formulas 9 and 10.

[0106]

[0107]

[0108] Q represents the capacity of the blast furnace, R represents the amount of casting in the furnace based on quality inspection results, and I is a set of raw materials where I={1, 2, Representing , N}, represents the inventory quantity of raw material i, and It can represent the amount of raw material i used up to the quality inspection result.

[0109] The above component constraints (good product conditions) may be based on Equations 11 and 12 for meeting the minimum and maximum conditions of the component ratios included in the finished aluminum alloy product.

[0110]

[0111]

[0112] Mathematical Equations 11 and 12 are formulas for meeting good product conditions, respectively intended to meet the minimum and maximum conditions of the components included in ALDC12.x. At this time, the budget parameter ( By utilizing ), one can prepare for the variability in the component content and loss rate of each raw material. If the user budget parameter ( If ) is set to (1.0, 1.0) respectively, the most conservative solution can be derived.

[0113] represents the central value of the loss rate by raw material, and represents the first budget parameter for the loss rate, which signifies the uncertainty related to the loss of raw materials that may occur as a result of raw materials being lost in the production process and decreasing the total input amount. represents a second budget parameter for the raw material composition ratio, which signifies the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material, and represents the maximum variation in the loss rate of raw material i, and represents the central value of the raw material composition ratio, and j is And, J is the set of components of the finished product, J={1, 2, ⪋, M}, and represents the minimum proportion of component j in the finished product, and represents the maximum proportion of component j in the finished product, and Y can represent the target production quantity.

[0114] The target production quantity (Y) can be determined by Equation 13 and Equation 14.

[0115]

[0116]

[0117] Equation 13 represents the determination of production quantity, and the total production quantity considering the loss rate can be represented through the budget parameter. D can represent the production requirement.

[0118] Referring to FIG. 1, in step S100, the user can input initial data values ​​required for each aluminum alloy finished product through an input / output interface (206 in FIG. 2). The initial data values ​​may include the minimum and maximum values ​​of the raw material composition ratio for each raw material, and the inventory quantity of each raw material, which is the quantity that can be input on-site during the production process. Therefore, in this step, the user can first input the minimum and maximum values ​​of the composition ratio of the raw materials currently held, and input the inventory quantity of each raw material.

[0119] Next, in step S102, the user defines the total number of inputs as Num and sets Num to a predetermined value. In one embodiment of the present invention, the total number of inputs (Num) is exemplarily set to 3. Then, in step S104, the variable n representing the number of repetitions can be set to 1.

[0120] Subsequently, the degree to which volatility is reflected can be determined through the budget parameter. Budget parameter ( , ) is a parameter for controlling the conservatism of the solution, and takes a real value ranging from 0 to the parameter value. Budget parameter( , We will explain ) below.

[0121] In step S106, the value representing the uncertainty of recycled scrap metal is set to the maximum value of 1.0. The value representing the uncertainty is the first budget parameter as shown in Table 2 ( ) and the second budget parameter( It may include ).

[0122] The above first budget parameter ( ) may be a budget parameter for the loss rate, which represents the uncertainty related to the loss of raw materials that may occur as raw materials are lost in the production process and decrease in the total input amount.

[0123] The above second budget parameter ( ) may be a budget parameter for the raw material composition ratio, which represents the uncertainty related to the content of the raw material's composition arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material.

[0124] The above first budget parameter ( ) and the second budget parameter ( The value of ) is a real value taking a value between 0 and 1.0, and when the value representing the uncertainty of the recycled scrap metal is maximum, the first budget parameter ( ) and the second budget parameter ( It may be a case where the value of ) is 1.0 each.

[0125] Therefore, in order to set the value representing the uncertainty of recycled scrap metal to the maximum, in step S106, the first budget parameter ( ) and the second budget parameter ( ) can be set to 1.0 each.

[0126] In one embodiment of the present invention, a first budget parameter ( ) and the second budget parameter ( Setting each to 1.0 is intended to account for the case where uncertainty is maximum, that is, the worst-case scenario.

[0127] Next, in step S108, a solution considering the worst-case uncertainty can be derived using a Robust Optimization (RO) algorithm to determine the feasibility of the problem.

[0128] In step S108, the case where the value representing the uncertainty of recycled scrap metal is maximized is considered, that is, the first budget parameter ( ) and the second budget parameter ( A robust optimization model (RO Model) can be executed when each of ) is set to 1.0. In this case, if there is no solution to the problem, the problem is incorrect, so the algorithm does not operate. That is, in step S110, it is determined whether the production of the target aluminum alloy finished product is possible, and if the production of the alloy finished product is impossible, in step S112, it is determined that the production of the alloy finished product is impossible, and the process can be terminated.

[0129] If the production of finished aluminum alloy products is possible, in step S114, a first budget parameter (Budget parameter) which is a value representing the uncertainty of the recycled scrap metal ( ) and the second budget parameter ( Each of ) can be reset to a predetermined value (Θ). For example, the predetermined value (Θ) may be 0.5.

[0130] In one embodiment of the present invention, in step S114, the first budget parameter ( ) and the second budget parameter( Although the values ​​of ) were each set to 0.5, the present invention is not limited thereto, and the first budget parameter ( ) and the second budget parameter( The value of ) can be set to a real number between 0 and 1.0.

[0131] In step S116, the budget parameter value Θ set by the user is each the first budget parameter ( ) and the second budget parameter( The first solution can be derived by substituting the value into the robust optimization model (RO Model), and the result value of the input amount of each raw material can be checked in step S118.

[0132] In step S120, an amount of each raw material corresponding to a portion of the result of the input amount of each raw material can be introduced into the furnace. For example, an amount of each raw material corresponding to (100×Θ)% of the result of the input amount of each raw material can be introduced into the furnace.

[0133] In one embodiment of the present invention, Θ is a first budget parameter set in step S114 ( ) and the second budget parameter( It can be set to the value of ). Accordingly, in one embodiment of the present invention, Θ may be 0.5.

[0134] That is, in the case of one embodiment of the present invention, at step S120, an amount of each raw material corresponding to 50% (100 × 0.5 = 50)% of the result value of the input amount of each raw material can be input into the furnace. That is, at step S120, an amount of each raw material corresponding to 50% of the input amount of each raw material determined by the robust optimization model can be input into the furnace. This is to account for cases where the conditions for good quality, i.e., the constraints on the constituent components, are not met due to variability.

[0135] In step S122, an intermediate inspection of the aluminum alloy may be performed. The intermediate inspection may include determining the production volume and quality inspection to determine whether the aluminum alloy meets production volume requirements and whether it meets good product conditions.

[0136] In step S124, it can be determined whether the target production volume is met based on the intermediate inspection.

[0137] If the target production volume is met, in step S126, it is determined whether the aluminum alloy meets the good product conditions based on the intermediate inspection, and if the good product conditions are met, the alloy production process can be terminated.

[0138] Meanwhile, if the target production volume is not met or the aluminum alloy does not meet the compositional constraints, i.e., the quality conditions, the process may proceed to step S128.

[0139] Step S128 is for determining the number of repetitions (n) in which steps S106 to S122 are executed, i.e., the number of times raw materials are input, and can determine whether the number of repetitions (n) in which steps S106 to S122 are executed is greater than or equal to (total number of inputs (Num)-1). That is, in one embodiment of the present invention, since the total number of inputs (Num) is 3, it can determine whether the number of repetitions (n) in which steps S106 to S122 are executed, i.e., the number of times raw materials are input, is 2 or more.

[0140] Accordingly, in one embodiment of the present invention, since n is initially set to 1, step S128 is not satisfied, so the process proceeds to step S130 to update the data, and in step S132, the number of repetitions (n) is increased by 1 so that n is set to 2. After Num is set to 2, the process proceeds again to step S106, and the steps after step S106 can be repeated.

[0141] Accordingly, in one embodiment of the present invention, when the target production quantity is not met or the good product condition is not met, and when the number of repetitions (n) of steps S106 to S122 executed is not greater than or equal to (total number of inputs (Num)-1), the data value is updated, and since the steps after step S106 can be repeated, steps S106 to S122 can be repeated (total number of inputs (Num)-1) times. That is, in one embodiment of the present invention, since the total number of inputs Num is 3, when the target production quantity is not met or the good product condition is not met, the data value is updated, and steps S106 to S122 can be repeated 2 times.

[0142] Meanwhile, when the number of repetitions n=2 in step S132, in step S106, in order to set the value representing the uncertainty of recycled scrap metal to the maximum, the first budget parameter ( ) and the second budget parameter( ) can be set to 1.0 each.

[0143] In one embodiment of the present invention, a first budget parameter ( ) and the second budget parameter ( Setting each to 1.0 is intended to account for the case where uncertainty is maximum, that is, the worst-case scenario.

[0144] Next, in step S108, a solution considering the worst-case uncertainty can be derived using a Robust Optimization (RO) algorithm to determine the feasibility of the problem.

[0145] In step S108, the case where the value representing the uncertainty of recycled scrap metal is maximized is considered, that is, the first budget parameter ( ) and the second budget parameter ( A robust optimization model (RO Model) can be executed with each of the values ​​set to 1.0. In this case, if there is no solution to the problem, the problem is incorrect, so the algorithm does not operate. That is, in step S110, it is determined whether the production of the target aluminum alloy finished product is possible, and if production is not possible, in step S112, it is determined that the production of the alloy finished product is impossible, and the process can be terminated.

[0146] If the production of finished aluminum alloy products is possible, in step S114, a first budget parameter (Budget parameter) which is a value representing the uncertainty of the recycled scrap metal ( ) and the second budget parameter ( Each of ) can be set to a predetermined value (Θ). In this case, the predetermined value (Θ) can be reset to 0.5 or to a real number between 0 and 1. That is, in the second iteration, the predetermined value (Θ) can be set to 0.4 or 0.6.

[0147] In one embodiment of the present invention, in step S114, the first budget parameter ( ) and the second budget parameter( Although the values ​​of ) were each set to 0.5, the present invention is not limited thereto, and the first budget parameter ( ) and the second budget parameter( The value of ) can be set to a real number between 0 and 1.0.

[0148] In step S116, the budget parameter value Θ set by the user is each the first budget parameter ( ) and the second budget parameter( The first solution can be derived by substituting the value into the robust optimization model (RO Model), and the result value of the input amount of each raw material can be checked in step S118.

[0149] In step S120, an amount of each raw material corresponding to a portion of the result of the input amount of each raw material can be introduced into the furnace. For example, an amount of each raw material corresponding to (100×Θ)% of the result of the input amount of each raw material can be introduced into the furnace.

[0150] In one embodiment of the present invention, Θ is a first budget parameter set in step S114 ( ) and the second budget parameter( It can be set to the value of ). Accordingly, in one embodiment of the present invention, Θ may be 0.5.

[0151] That is, in the case of one embodiment of the present invention, at step S120, an amount of each raw material corresponding to 50% (100 × 0.5 = 50)% of the result value of the input amount of each raw material can be input into the furnace. That is, at step S120, an amount of each raw material corresponding to 50% of the input amount of each raw material determined by the robust optimization model can be input into the furnace. This is to account for cases where the conditions for good quality, i.e., the constraints on the constituent components, are not met due to variability.

[0152] In step S122, an intermediate inspection of the aluminum alloy may be performed. The intermediate inspection may include determining the production volume and quality inspection to determine whether the aluminum alloy meets production volume requirements and whether it meets good product conditions.

[0153] In step S124, it can be determined whether the target production volume is met based on the intermediate inspection.

[0154] If the target production volume is met, in step S126, it is determined whether the aluminum alloy meets the good product conditions, and if the good product conditions are met, the alloy production process can be terminated.

[0155] Accordingly, when the number of repetitions n is less than the total number of inputs (Num), which is the last number of repetitions, in one embodiment of the present invention, since the total number of inputs Num is 3, when the number of repetitions n is 1 or 2, if both the target production quantity and the good product condition are satisfied, the aluminum alloy manufacturing process can be terminated.

[0156] Meanwhile, if the target production volume is not met or the aluminum alloy does not meet the compositional constraints, i.e., the quality conditions, the process can proceed back to step S128.

[0157] Step S128 is for determining the number of repetitions (n) in which steps S106 to S122 are executed, i.e., the number of times raw materials are input, and can determine whether the number of repetitions (n) in which steps S106 to S122 are executed is greater than or equal to (total number of inputs (Num)-1). That is, in one embodiment of the present invention, since the total number of inputs (Num) is 3, it can determine whether the number of repetitions (n) in which steps S106 to S122 are executed, i.e., the number of times raw materials are input, is 2 or more.

[0158] This time, since n is set to 2, step S128 is satisfied, so we can proceed to step S134.

[0159] That is, if steps S106 through S122 are repeated (total number of inputs (Num)-1) times and the target production quantity is not met or the good product condition is not met, the process can proceed to step S134.

[0160] When n=2, that is, when the process is repeated twice, if either the target production quantity or the quality constraint is not satisfied, the budget parameter value is fixed at 1.0 to derive the most conservative solution at the end, and the third input is executed to determine the input amount of each raw material to satisfy the condition, and the determined input amount of raw material is fed into the furnace to complete the manufacture of the aluminum alloy.

[0161] To explain this in detail, in step S134, the value representing the uncertainty of the recycled scrap metal can be set to the maximum. That is, in step S134, the first budget parameter ( ) and the second budget parameter ( The value of ) can be set to 1.0 respectively.

[0162] In step S136, a robust optimization model (RO Model) is executed by considering the case where the value representing the uncertainty of recycled scrap metal is at its maximum to determine the input amount of each raw material, and by feeding the determined input amount of each raw material into the furnace, a good aluminum alloy that satisfies the composition constraints and ultimately satisfies the target production volume can be manufactured.

[0163] Experimental results

[0164] Data description

[0165] To compare the performance of the algorithm presented in this invention, data on the compositional ratios of each raw material was generated, and experiments were conducted accordingly. To create data that closely approximates reality, the distribution was estimated using Company C's process logs and the results of component spectrum analysis based on the process. The process of estimating the distribution is as follows.

[0166] 1. Specify the element to estimate the distribution

[0167] 2. Randomly generate estimated component ratios within the variation range, limited to the raw materials input in the process log. Store in the generated data list for each input raw material.

[0168] 3. Repeat step 2 multiple times to generate a large number of data lists. Calculate the total sum of the component ratio data in the process log input quantity lists by raw material.

[0169] 4. Extract only the data list with calculated results similar to the actual component analysis results.

[0170] 5. Repeat steps 2 through 5 for multiple process logs.

[0171] 6. Generate a histogram using the ingredient ratios of the extracted list.

[0172] 7. Estimate the probability distribution most similar to the shape of the generated histogram through continuity fitting.

[0173] The component ratio distribution obtained through this process can partially describe the actual distribution of the variability group. The goodness of fit between the histogram and the distribution is measured using two metrics: AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion).

[0175] Expert imitation algorithm

[0176] Currently, decision-making in Company C's manufacturing process is based on subjective judgment derived from the experience of experts. In this invention, Company C's decision-making method is compared with the proposed algorithm. To this end, an expert imitation algorithm is implemented based on the contents of a process log. Subsequently, to verify whether the expert imitation algorithm closely mimics the actual work method of experts, the production unit cost based on raw material input in the process log and the production unit cost based on the results of the expert imitation algorithm are compared using the same price data as in Table 3.

[0177]

[0178] As a result of the comparison, it can be confirmed that the expert imitation algorithm implemented in the present invention is similar to the results of the process log. Therefore, we intend to compare this algorithm with an alloy manufacturing method based on the robust optimization algorithm proposed in the present invention.

[0180] Experimental Results and Analysis

[0181] In order to solve the aluminum alloy manufacturing process of Company C, which is subject to variability, the results of an expert imitation algorithm and the algorithm proposed in this invention are compared using 10 experimental data sets containing information on the unit price of raw materials, inventory quantity, and component ratio. The results are shown in Table 4 as the production unit price of the finished product, and the extent to which the production unit price has decreased is verified by comparing it with the expert imitation algorithm.

[0182] Each algorithm compares results from a minimum number of inputs up to 3 inputs, and the expert imitation algorithm fixes the number of inputs at 2, identical to Company C's decision-making. The product used in the experiment is Company C's flagship product, ALDC12.x.

[0183]

[0184] The RO algorithm according to the present invention shows better performance than the expert imitation algorithm in other datasets, excluding the results of dataset 9, when the number of inputs is 1. In particular, when the experiment is conducted with 3 inputs, it shows the highest performance on average and a unit price reduction rate of 11.38% compared to the expert imitation algorithm.

[0185] This invention presents an algorithm that solves the variability-existing production process of recycled aluminum alloys through mathematical modeling, and analyzes the performance and process advantages of each algorithm in comparison with Company C's operating method. The RO algorithm presented in this invention enables decision-making without specialized knowledge, provided that only data on finished products and raw materials is provided. Thus, the RO algorithm according to this invention can resolve the problem of Company C's existing inefficient raw material input method. Furthermore, the RO algorithm can adjust the number of operations according to on-site conditions. This algorithm has the potential to be applied to other production process fields, provided that the range of data variability is known. Therefore, there is a need for active consideration of utilizing this algorithm not only by Company C but also by other companies. In conclusion, it can be confirmed that the algorithm presented by this invention is a useful tool for dealing with on-site problems and reducing production costs. Explanation of the symbols

[0187] 200: Device capable of performing an alloy manufacturing method based on a robust optimization algorithm 202 : Processor 204 : Memory 206: Input / Output Interface 208: Network Communication Interface

Claims

Claim 1 A method for manufacturing an alloy based on a robust optimization algorithm performed by a computing device, comprising: (a) a step of inputting initial data values ​​required for each finished alloy product; (b) a step of determining whether production of a target finished alloy product is possible by executing a robust optimization model considering the case where the value representing the uncertainty of recycled scrap metal is at its maximum; (c) a step of, if production of the target finished alloy product is possible, resetting the value representing the uncertainty of the recycled scrap metal to a predetermined value (Θ), executing a robust optimization model to verify the result value of the input amount of each raw material, and then inputting an amount of each raw material corresponding to a part of the input amount result value into a furnace; (d) a step of performing an intermediate inspection of the alloy; (e) a step of determining whether the target production quantity is satisfied. and (f) a step of determining whether the alloy meets the good product condition when the target production quantity is met, and terminating the alloy production process when the good product condition is met, and in the step (c) of feeding each raw material into the furnace corresponding to a part of the input amount result value, feeding (input amount result value × (100 × Θ)%) into the furnace, a method for manufacturing an alloy based on a robust optimization algorithm. Claim 2 A method for manufacturing an alloy based on a robust optimization algorithm according to claim 1, characterized in that when the target production volume is not met or the good product condition is not met, and when the number of repetitions is less than or equal to (total number of inputs (Num)-1), the data value is updated and steps (b) to (d) are repeated. Claim 3 A method for manufacturing an alloy based on a robust optimization algorithm according to claim 2, further comprising the step of determining the input amount of each raw material by executing a robust optimization model in the case where, when steps (b) to (d) are repeated (total number of inputs (Num)-1) times, the target production volume is not met or the good product condition is not met, and the value representing the uncertainty of recycled scrap metal is at its maximum, and inputting the determined input amount of each raw material into a furnace. Claim 4 delete Claim 5 A method for manufacturing an alloy based on a robust optimization algorithm according to claim 1, wherein the initial data values ​​include the minimum and maximum values ​​of the raw material composition ratio for each raw material, the inventory quantity of each raw material which is the quantity that can be input on-site during the production process, and the total number of inputs (Num). Claim 6 In paragraph 1, the value representing the uncertainty is the first budget parameter ( ) and the second budget parameter( Includes ) and the first budget parameter ( ) is a budget parameter for the loss rate, which represents the uncertainty related to the loss of raw materials that may occur as raw materials are lost in the production process and decrease in the total input amount, and the second budget parameter ( A method for manufacturing an alloy based on a robust optimization algorithm, characterized in that ) is a budget parameter for the raw material composition ratio, which represents the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material. Claim 7 In paragraph 6, the above-mentioned first budget parameter ( ) and the second budget parameter ( The value of ) is a real value taking a value between 0 and 1.0, and when the value representing the uncertainty of the recycled scrap metal is maximum, the first budget parameter ( ) and the second budget parameter ( A method for manufacturing an alloy based on a robust optimization algorithm, characterized in that the value of ) is 1.0, and the predetermined value (Θ) is a real value that takes a value between 0 and 1.

0. Claim 8 A method for manufacturing an alloy based on a robust optimization algorithm, characterized in that, in step (b) of determining whether the production of the target alloy finished product is possible, a robust optimization model is executed to proceed with the production process if feasible, and stop the production process if feasible. Claim 9 In claim 1, the robust optimization model determines the result value of the input amount of each raw material based on Equation 8, [Equation 8] The above raw materials include N types of raw materials from i=1 to N, Xi represents the input amount of raw material i, Ci represents the cost of raw material i, and Xi is limited by Equations 9 and 10, [Equation 9] [Mathematical Formula 10] Q represents the capacity of the blast furnace, R represents the amount of casting in the furnace based on quality inspection results, and I is a set of raw materials where I={1, 2, Representing , N}, represents the inventory quantity of raw material i, and A method for manufacturing an alloy based on a robust optimization algorithm, characterized by indicating the amount of raw material i used up to the quality inspection result. Claim 10 In Clause 9, the above-mentioned good product condition is based on Equations 11 and 12 for meeting the minimum and maximum conditions of the compositional ratios included in the finished alloy product, [Equation 11] [Mathematical Formula 12] represents the central value of the loss rate by raw material, and represents the first budget parameter for the loss rate, which signifies the uncertainty related to the loss of raw materials that may occur as a result of raw materials being lost in the production process and decreasing the total input amount. represents a second budget parameter for the raw material composition ratio, which signifies the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material, and represents the maximum variation in the loss rate of raw material i, and represents the central value of the raw material composition ratio, and j is And, J is the set of components of the finished product, J={1, 2, , M} and, represents the minimum proportion of component j in the finished product, and A method for manufacturing an alloy based on a robust optimization algorithm, characterized in that represents the maximum proportion of component j in the finished product and Y represents the target production quantity. Claim 11 In Clause 10, the target production quantity (Y) is determined by Equation 13 and Equation 14, [Equation 13] [Mathematical Formula 14] A method for manufacturing an alloy based on a robust optimization algorithm, characterized in that D represents the production requirement. Claim 12 A method for manufacturing an alloy based on a robustness optimization algorithm according to claim 1, wherein the alloy comprises one alloy selected from the group including aluminum alloy, copper alloy, iron alloy, nickel alloy, titanium alloy, tin alloy, and zinc alloy. Claim 13 The processor comprises: a memory in which a robust optimization algorithm model is stored; and a processor connected to the memory and configured to execute computer-readable commands contained in the memory, wherein the processor comprises: (a) an operation of inputting initial data values ​​required for each alloy finished product; (b) an operation of determining whether production of a target alloy finished product is possible by executing a robust optimization model, considering the case where the value representing the uncertainty of recycled scrap metal is at its maximum; (c) an operation of, if production of the target alloy finished product is possible, resetting the value representing the uncertainty of recycled scrap metal to a predetermined value (Θ), executing the robust optimization algorithm model to check the result value of the input amount of each raw material, and then inputting an amount of each raw material corresponding to a part of the input amount result value into a furnace; (d) an operation of performing an intermediate inspection of the alloy; and (e) an operation of determining whether the target production quantity is satisfied. and (f) when the target production volume is met, determine whether the alloy meets the good product condition, and if the good product condition is met, terminate the alloy production process; the operation of feeding each raw material into the furnace in the operation (c) corresponding to a part of the input amount result value is characterized by feeding (input amount result value × (100 × Θ)%) into the furnace. The alloy manufacturing apparatus based on a robust optimization algorithm. Claim 14 An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that, in the case of claim 13, when the target production volume is not met or the good product condition is not met and the number of repetitions is less than or equal to (total number of inputs (Num)-1), the data value is updated and the above operations (b) to (d) are repeated. Claim 15 An alloy manufacturing apparatus based on a robust optimization algorithm according to claim 14, further comprising the operation of determining the input amount of each raw material by executing a robust optimization model in the case where, when the above operations (b) to (d) are repeated (total number of inputs (Num)-1) times, the target production volume is not met or the good product condition is not met, and the value representing the uncertainty of recycled scrap metal is at its maximum, and inputting the determined input amount of each raw material into a furnace. Claim 16 An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that, in Clause 13, the above initial data values ​​include the minimum and maximum values ​​of the raw material composition ratio for each raw material, the inventory quantity of each raw material which is the quantity that can be input on-site during the production process, and the total number of inputs (Num). Claim 17 In Clause 13, the value representing the above uncertainty is the first budget parameter ( ) and the second budget parameter( Includes ) and the first budget parameter ( ) is a budget parameter for the loss rate, which represents the uncertainty related to the loss of raw materials that may occur as raw materials are lost in the production process and decrease in the total input amount, and the second budget parameter ( An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that ) is a budget parameter for the raw material composition ratio, which represents the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material. Claim 18 In Clause 17, the above-mentioned first budget parameter ( ) and the second budget parameter ( The value of ) is a real value taking a value between 0 and 1.0, and when the value representing the uncertainty of the recycled scrap metal is maximum, the first budget parameter ( ) and the second budget parameter ( An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that the value of ) is 1.0, and the predetermined value (Θ) is a real value that takes a value between 0 and 1.

0. Claim 19 An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that, in the operation (b) of determining whether the production of the alloy finished product to be targeted is possible in claim 13, the robust optimization model is executed to proceed with the production process if feasible, and stop the production process if feasible. Claim 20 In Clause 13, the above robust optimization model determines the result value of the input amount of each raw material based on Equation 8, [Equation 8] The above raw materials include N types of raw materials from i=1 to N, Xi represents the input amount of raw material i, Ci represents the cost of raw material i, and Xi is limited by Equations 9 and 10, [Equation 9] [Mathematical Formula 10] Q represents the capacity of the blast furnace, R represents the amount of casting in the furnace based on quality inspection results, and I is a set of raw materials where I={1, 2, Representing , N}, represents the inventory quantity of raw material i, and An alloy manufacturing device based on a robust optimization algorithm, characterized by indicating the amount of raw material i used up to the quality inspection result. Claim 21 In Clause 20, the above-mentioned good product conditions are based on Equations 11 and 12 for meeting the minimum and maximum conditions of the compositional ratios included in the finished alloy product, [Equation 11] [Mathematical Formula 12] represents the central value of the loss rate by raw material, and represents the first budget parameter for the loss rate, which signifies the uncertainty related to the loss of raw materials that may occur as a result of raw materials being lost in the production process and decreasing the total input amount. represents a second budget parameter for the raw material composition ratio, which signifies the uncertainty related to the content of the raw material's constituent components arising from the fact that the metal composition ratio cannot be accurately known when using recycled scrap metal as a raw material, and represents the maximum variation in the loss rate of raw material i, and represents the central value of the raw material composition ratio, and j is And, J is the set of components of the finished product, J={1, 2, , M} and, represents the minimum proportion of component j in the finished product, and An alloy manufacturing apparatus based on a robust optimization algorithm, characterized in that represents the maximum proportion of component j in the finished product and Y represents the target production quantity.

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