Batching system for casting and batching method
By combining cloud computing and intelligent optimization algorithms with real-time data detection, the problem of traditional casting batching relying on manual experience has been solved, enabling precise multi-terminal collaborative batching and improving production efficiency and product consistency.
Patent Information
- Application Number
- CN202511746755.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-02-27
AI Technical Summary
Traditional casting batching methods rely on manual experience, resulting in low efficiency, high cost, poor composition control accuracy, and a lack of real-time detection and dynamic adjustment, which affects production stability and product consistency.
By employing cloud computing and intelligent optimization algorithms, combined with real-time data detection and dynamic adjustment, the system enables multi-terminal collaboration through clients, cloud servers, display terminals, and detection modules to perform precise ingredient calculations and real-time adjustments.
It improved the accuracy of ingredient mixing and production stability, reduced raw material costs and resource consumption, and enhanced production transparency and decision support capabilities.
Smart Images
Figure CN121576789A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of casting material preparation technology, and in particular to a casting material preparation system and method. Background Technology
[0002] Casting is an indispensable basic process in the manufacturing industry. By melting metal raw materials into liquid and pouring them into a mold, parts with specific shapes and properties are formed. As a key link in casting production, batching directly affects product quality, cost and production efficiency. The batching process requires precise calculation of the amount of various raw materials to be added according to the product process formula to ensure that the chemical composition and physical properties of the molten metal meet the design requirements.
[0003] Traditional ingredient mixing methods rely heavily on the experience of operators, who estimate and add ingredients by manually consulting formula cards and inventory records. This method is not only inefficient but also susceptible to human factors, resulting in low ingredient accuracy, poor cost control, and even product defects and quality fluctuations.
[0004] With the advancement of industrial automation technology, some foundry enterprises have introduced computer-aided batching systems, which use simple algorithms to calculate raw materials and control feeding. These systems have improved the calculation speed to some extent, but their functions are relatively simple and lack the ability to coordinate multiple terminals and share data in real time.
[0005] In actual smelting processes, due to fluctuations in raw material yield, changes in furnace conditions, and operational errors, the initial batching results often deviate from actual requirements. However, traditional systems lack real-time detection and dynamic adjustment mechanisms, making it impossible to correct these deviations in a timely manner, which affects production stability and product consistency.
[0006] Furthermore, ingredient batching calculations involve multi-objective optimization problems, requiring a balance between element content constraints, inventory limitations, and cost control. Parameters such as element yield and overall increase / decrease rate of in-furnace replenishment are affected by multiple factors, including smelting temperature, furnace lining condition, and raw material characteristics, making them difficult to accurately describe using a fixed model. Existing algorithms often ignore these dynamic factors or use empirical values for simplification, leading to the accumulation of calculation errors.
[0007] Furthermore, the process formulation and inventory management rely on offline records, resulting in issues such as version confusion and delayed updates, and a lack of centralized and timely data management. Summary of the Invention
[0008] To address the aforementioned technical problems, this invention provides a batching system and method for casting. By utilizing cloud computing, multi-terminal collaboration, and intelligent optimization algorithms, combined with real-time data detection and dynamic adjustment, it solves the problems of low efficiency, high cost, poor composition control accuracy, and lack of coordination in production data caused by the reliance on manual experience in traditional casting batching. Specifically, this is achieved through the following technical solutions.
[0009] This invention provides a batching system for casting, including a client, a cloud server, a display terminal, and a detection module; The cloud server is connected to the client and the display terminal via signals. The detection module is connected to the client signal; The client is used to call data and algorithms from the cloud server, obtain on-site data through the detection module, calculate the optimal ingredient ratio, and is also used for parameter input and result query and display. The cloud server is used to store the optimal ingredient mixing result algorithm, raw material inventory data, product process formula data, and optimal ingredient mixing result; The display terminal is used to display the optimal result of the ingredient mixing; The detection module is used to collect on-site data, including the current liquid volume in the furnace and the current composition and content of the liquid in the furnace.
[0010] Preferably, the client includes a computer web client, a mini-program, a mobile application, or a desktop application, and the client can be installed and used on an office computer, a mobile phone, or a computer in front of the stove.
[0011] Preferably, the display terminal includes a central control room display screen, a furnace front batching display screen, or a furnace rear process display screen.
[0012] Preferably, the detection module includes a component analysis instrument and a weight sensor; The component analysis instrument is located near the smelting furnace and is used to detect the sample components during the smelting process, and transmits the detection results to the client. The weight sensor is installed on the outside of the smelting furnace to detect the weight of the smelting furnace in real time. The weight inside the furnace is obtained by subtracting the weight of the furnace body from the weight of the smelting furnace body, and the result of the weight inside the furnace is transmitted to the client.
[0013] Preferably, the raw material inventory data stored in the cloud server is updated in real time according to the consumption and replenishment of raw materials, and the product process formula data is updated in real time according to the adjustment of process formula parameters.
[0014] The present invention also provides a casting material batching method, based on the above-mentioned casting material batching system, comprising the following steps: Step 1: Data Collection Collect on-site data and retrieve raw material inventory data and product process formula data from the cloud server; Step 2: Ingredient Calculation The client calls the optimal ingredient mixing result algorithm in the cloud server to calculate the initial ingredient mixing result; Step 3: Output Results The client transmits the initial batching results to the cloud server for storage and then to the display terminal for display. Step 4: Ingredient Adjustment Before exiting the oven, the client executes steps 1 and 2 again to obtain the secondary batching results, which are then transmitted to the cloud server for storage and to the display terminal for display. Step 5: Storage and Display After the product is produced, the cloud server will combine the initial and secondary batching results to calculate the optimal batching result, which will then be transmitted to the cloud server for storage and finally to the display terminal for display.
[0015] Preferably, the algorithm for the optimal ingredient ratio result is as follows:
[0016] in, This indicates the weight of the i-th raw material to be added. , Indicates the accuracy of adding the i-th type of raw material. Indicates the amount of raw materials added. Z， and ; This represents the penalty term in the objective function. , This indicates the maximum total weight penalty variable added inside the furnace. This indicates the penalty coefficient for the maximum total weight added inside the furnace; This represents the lower limit of the target content of the j-th element before the furnace. This represents the upper limit of the target content of the j-th element at the furnace. This represents the unit price of the i-th raw material. This represents the content of the j-th element in the i-th raw material. This represents the maximum additional weight of the i-th raw material; This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element; This represents the actual weight of the j-th element before furnace operation. , This represents the actual content of the j-th element before furnace operation. This indicates the liquid volume in the furnace before adding fuel. This indicates the maximum total weight to be added inside the furnace.
[0017] Preferably, in step 2, the calculated initial batching results include:
[0018] in, This represents the weight of the i-th raw material to be added, and Cost represents the theoretical total cost of adding it in the furnace. This represents the unit price of the i-th raw material. This indicates the theoretical total weight added into the furnace. This represents the theoretical weight of the j-th element before furnace replenishment. This represents the content of the j-th element in the i-th raw material. This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element. This represents the actual weight of the j-th element before furnace operation. This represents the theoretical content of the j-th element before furnace replenishment. This indicates the liquid volume in the furnace before adding fuel. This indicates the total weight of the added material.
[0019] Preferably, the output items and output format of the secondary batching results and the optimal batching results are the same as those of the primary batching results.
[0020] After adopting the above technical solution, the beneficial effects of the present invention are: 1. Supports client installation and use on various mobile terminals, including office computers, mobile phones and furnace computers, to meet the operational needs of different participants. Through the signal connection between the cloud server and the display terminal, the batching results are synchronized in real time to the central control room, furnace batching and furnace process displays, etc., to guide operators to accurately add materials. At the same time, it facilitates remote monitoring of production status by technicians and managers, improving overall collaborative efficiency and response speed.
[0021] 2. The ingredient calculation algorithm aims at both the lowest cost and the least amount of replenishment. It introduces a penalty term to calibrate losses and errors, and ensures calculation accuracy through constraints such as element content and inventory limits. This algorithm effectively reduces raw material costs and resource consumption while ensuring product quality, thereby improving production economy and environmental friendliness.
[0022] 3. The system can perform secondary batching calculations based on composition deviations during the smelting process and output the raw material adjustment amount. This dynamic optimization mechanism overcomes operational errors and deviations in empirical parameters, ensuring that the final product composition is closer to the target value, improving batching accuracy and production stability, and reducing manual intervention and operational errors.
[0023] 4. The cloud server centrally stores data such as raw material inventory, process formula and batching results, and updates them in real time according to permissions. Batching results are stored by time and batch, and historical data can be queried and tracked, providing a reliable basis for production management, quality analysis and process improvement, and enhancing production transparency and decision support capabilities. Attached Figure Description
[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 This is a schematic diagram of the material preparation system for casting. Figure 2 This is the interface for entering the process formula for new products on the client side. Figure 3 This is the interface for displaying the ingredient calculation results on the client side. Detailed Implementation
[0026] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.
[0027] An embodiment of the present invention provides a batching system for casting, see below. Figure 1 The casting batching system includes a client, a cloud server, a display terminal, and a detection module. The cloud server is connected to the client and the display terminal via signals, and the detection module is connected to the client via signals.
[0028] The client includes a web interface for computers, a mini-program, a mobile application, a desktop application, and other applications that can input and output data and run the batching algorithm. This ensures that the client can be installed and used on various mobile terminals such as office computers, mobile phones, and furnace computers, meeting the usage needs of multiple participants.
[0029] The aforementioned client is used to retrieve data and algorithms from the cloud server. It can also acquire on-site data through a detection module and calculate the optimal ingredient ratio based on this data. (See [link to relevant documentation]). Figure 2 , Figure 3 The client application also allows users to input new product process formulas and query and display ingredient calculation results.
[0030] The cloud server stores the optimal ingredient ratio algorithm, raw material inventory data, product process formula data, and optimal ingredient ratio results.
[0031] The raw material inventory data is updated in real time based on the consumption and replenishment of raw materials. The raw material consumption data is the optimal batching result calculated by the client during the production process, and the replenishment data is the data of newly entered raw materials entered through the client.
[0032] The product process formula data is updated in real time according to the adjustment of the process formula parameters. Operators can adjust the existing process formula parameters or enter the process formula of new products according to their permissions.
[0033] In addition, technicians can adjust and optimize the optimal ingredient mixing algorithm in real time according to their permissions.
[0034] The calculated optimal batching result is transmitted to the cloud server and stored sequentially according to time and batch number, facilitating data retrieval and tracking.
[0035] The display terminal includes display devices such as the central control room display screen, the furnace front batching display screen, and the furnace rear process display screen, which can display the final calculation results.
[0036] The display terminal is set up in a location on-site where the batching calculation results need to be referenced to guide the specific operations of the operators. The display terminal is also set up in a location that is convenient for technicians and managers to observe, so that technicians and managers can know the on-site status.
[0037] Of course, managers and technicians can also query data through the client to know the on-site status.
[0038] The detection module includes component analysis instruments and weight sensors. The component analysis instruments include carbon and silicon analyzers, spectrometers, and other analytical instruments capable of detecting the components inside the furnace.
[0039] The component analysis instrument is set up near the smelting furnace to detect the composition and content of samples during the smelting process, and transmits the detection results to the client of the computer in front of the furnace to provide raw data for calculating the optimal batching result.
[0040] The weight sensor is installed on the outside of the smelting furnace to detect the weight of the smelting furnace in real time. The weight inside the furnace is obtained by subtracting the weight of the furnace body from the weight of the furnace body. The weight inside the furnace is then transmitted to the client of the computer in front of the furnace to provide raw data for calculating the optimal batching result.
[0041] Another embodiment of the present invention provides a batching method for casting, which calculates the optimal batching result based on the above-described batching system. The batching method includes the following steps: Step 1: Data Collection The operator starts the computer client in front of the furnace to collect on-site data and retrieves raw material inventory data and product process formula data from the cloud server.
[0042] The on-site data includes the current liquid volume in the furnace, the current composition and content in the furnace. The current liquid volume in the furnace is obtained by subtracting the weight of the smelting furnace from the weight sensor of the detection module, and the current composition and content in the furnace are obtained by the composition analysis instrument of the detection module.
[0043] Raw material inventory data and product process formula data are accessed through the cloud server. For the process formula of a new product, the operator inputs the ingredients and content of the new product through the client of the computer in front of the furnace, names the process formula of the product, and then transmits it to the cloud server for storage.
[0044] Step 2: Ingredient Calculation Based on the data collected in step 1, the client computer at the furnace front calls the optimal batching result algorithm in the cloud server to calculate the initial batching result.
[0045] The algorithm for achieving the optimal ingredient ratio is as follows:
[0046] in, This indicates the weight of the i-th raw material to be added. , Indicates the accuracy of adding the i-th type of raw material. Indicates the amount of raw materials added. Z， and ； This represents the penalty term in the objective function. , This indicates the maximum total weight penalty variable added inside the furnace. This indicates the penalty coefficient for the maximum total weight added inside the furnace. and As an empirical coefficient, its specific value is derived through experiments or practical experience.
[0047] Equation (1) in the above system of equations represents the lowest cost of adding raw materials, and equation (2) represents the minimum amount of raw materials added. Both equations (1) and (2) include a penalty term in the objective function. This takes into account the loss and error of raw materials during the addition process, thereby ensuring the accuracy of the calculation.
[0048] This represents the lower limit of the target content of the j-th element before the furnace. This represents the upper limit of the target content of the j-th element at the furnace. and The specific values are obtained from the product process formula data.
[0049] This represents the unit price of the i-th raw material. This represents the content of the j-th element in the i-th raw material. This represents the maximum replenishment weight of the i-th raw material, i.e., the inventory level. , and The specific values are obtained from the raw material inventory data.
[0050] This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element. and As an empirical coefficient, its specific value is derived through experiments or practical experience.
[0051] Equation (3) of the above system of equations is obtained by processing the newly added raw materials. and The calibration ensures the accuracy of the component content calculation after adding raw materials.
[0052] This represents the actual weight of the j-th element before furnace operation. , This represents the actual content of the j-th element before furnace operation. This indicates the liquid volume in the furnace before adding fuel. This indicates the maximum total weight to be added inside the furnace. and The specific values were obtained from on-site data.
[0053] Equation (4) of the above system of equations, using parameters The total amount of newly added raw materials is calibrated to ensure that the total amount of raw materials added after calibration does not exceed the maximum total weight to be added into the furnace. This is to avoid waste of raw materials caused by spillage.
[0054] Equation (5) of the above set of equations limits the amount of each type of raw material to ensure that the ingredient calculation is completed based on the existing inventory.
[0055] By solving the above system of equations, the initial batching results are obtained, as shown below:
[0056] in, This represents the weight of the i-th raw material to be added, and Cost represents the theoretical total cost of adding it in the furnace. This represents the unit price of the i-th raw material. This indicates the theoretical total weight added into the furnace. This represents the theoretical weight of the j-th element before furnace replenishment. This represents the content of the j-th element in the i-th raw material. This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element. This represents the actual weight of the j-th element before furnace operation. This represents the theoretical content of the j-th element before furnace replenishment. This indicates the liquid volume in the furnace before adding fuel. This indicates the total weight of the added material.
[0057] In the initial batching results obtained above, equation (6) represents the amount of each raw material added, equation (7) represents the cost of all added raw materials, equation (8) represents the weight of all added raw materials, and equations (9) and (10) represent the theoretical weight and content of each element, respectively.
[0058] Step 3: Output Results The client computer at the furnace transmits the initial batching result calculated in step 2 to the cloud server for storage. The cloud server then transmits the initial batching result to the display terminal for display. The operator adds each raw material according to the data on the display terminal and begins smelting.
[0059] When the operator feeds materials according to the data on the display terminal, he only needs to feed each material one by one according to the amount of each material added in step 2 (6), which reduces the difficulty of operation and reduces the probability of operation error.
[0060] Step 4: Ingredient Adjustment During the smelting process, the operator uses the client to perform a second calculation based on the existing smelting process, according to the calculation method in step 2, to obtain the secondary batching results. The client on the furnace computer transmits the secondary batching results to the cloud server for storage, and then the cloud server transmits the secondary batching results to the display terminal for display. The operator then completes the replenishment of each raw material based on the data on the display terminal.
[0061] Due to on-site operational errors and deviations in empirical parameters, the actual composition and content in the smelting furnace still deviate from the composition and content of the target product after the operator feeds the materials according to the initial batching results. Therefore, during the smelting process, a second calculation is performed using the calculation method in step 2 to fine-tune the composition and content of the current smelting process, so as to ensure that the composition and content of the final product are more accurate.
[0062] The output items and output format of the secondary batching results are the same as those of the primary batching results.
[0063] The raw material types and weight data in the secondary batching results obtained above include not only the materials required for adjusting the composition in the furnace during the smelting process, but also the information on auxiliary materials to be added after smelting and before casting. This information can directly guide operators to complete the auxiliary material addition operation before casting.
[0064] Step 5: Storage and Display After smelting is completed, the cloud server will combine the stored initial batching results and secondary batching results to calculate the optimal batching result, store the optimal batching result in the cloud server, and transmit it to the display terminal for display.
[0065] Since the display terminal includes multiple screens, and the initial and secondary batching results calculated before the smelting is completed are not the final batching results for that furnace, when the cloud server displays the data of the initial and secondary batching results through the display terminal, it is only necessary to transmit the above data to the client of the furnace computer or to the display terminal that is easy for on-site operators to observe.
[0066] After the smelting is completed, the optimal batching result obtained by combining the initial batching result and the secondary batching result is the final batching result for that furnace. The optimal batching result is stored in the cloud server along with the time data, and then transmitted to each display terminal for display, so that technicians and managers can know the on-site status.
[0067] The output items and output format of the optimal batching result are the same as those of the initial batching result and the secondary batching result.
[0068] The above-mentioned comprehensive calculation process involves integrating the data from the initial batching results and the secondary batching results to output data such as the type and weight of the feed, total cost, total weight, weight of each element, and content of each element for that batch. The data on the type and weight of the feed, total cost, and total weight are obtained by summing the initial batching results and the secondary batching results. After the feed is added, the data on the weight and content of each element are the same as those in the secondary batching results.
[0069] The calculation process involves simple data accumulation or summarization, and will not be elaborated here.
[0070] Of course, technicians and managers can also use the client to query the final batching results data stored on the cloud server by time to meet management needs.
[0071] The embodiments described above are not exhaustive, nor do they limit the invention to any specific embodiments. Clearly, many modifications and variations can be made based on the above description. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to effectively utilize the invention and its modifications. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of protection of the invention.
Claims
1. A batching system for casting, characterized in that, Includes client, cloud server, display terminal and detection module; The cloud server is connected to the client and the display terminal via signals. The detection module is connected to the client signal; The client is used to call data and algorithms from the cloud server, obtain on-site data through the detection module, calculate the optimal ingredient ratio, and is also used for parameter input and result query and display. The cloud server is used to store the optimal ingredient mixing result algorithm, raw material inventory data, product process formula data, and optimal ingredient mixing result; The display terminal is used to display the optimal result of the ingredient mixing; The detection module is used to collect on-site data, including the current liquid volume in the furnace and the current composition and content of the liquid in the furnace.
2. The casting batching system as described in claim 1, characterized in that, The client includes a computer web interface, a mini-program, a mobile application, or a desktop application, and the client can be installed and used on an office computer, a mobile phone, or a computer in front of the stove.
3. The casting batching system as described in claim 1, characterized in that, The display terminal includes a central control room display screen, a furnace front batching display screen, or a furnace rear process display screen.
4. The casting batching system as described in claim 1, characterized in that, The detection module includes a component analysis instrument and a weight sensor; The component analysis instrument is located near the smelting furnace and is used to detect the sample components during the smelting process, and transmits the detection results to the client. The weight sensor is installed on the outside of the smelting furnace to detect the weight of the smelting furnace in real time. The weight inside the furnace is obtained by subtracting the weight of the furnace body from the weight of the smelting furnace body, and the result of the weight inside the furnace is transmitted to the client.
5. The casting batching system as described in claim 1, characterized in that, The raw material inventory data stored in the cloud server is updated in real time according to the consumption and replenishment of raw materials, and the product process formula data is updated in real time according to the adjustment of process formula parameters.
6. A casting batching method, based on the casting batching system as described in claim 1, characterized in that, Includes the following steps: Step 1: Data Collection Collect on-site data and retrieve raw material inventory data and product process formula data from the cloud server; Step 2: Ingredient Calculation The client calls the optimal ingredient mixing result algorithm in the cloud server to calculate the initial ingredient mixing result; Step 3: Output Results The client transmits the initial batching results to the cloud server for storage and then to the display terminal for display. Step 4: Ingredient Adjustment Before exiting the oven, the client executes steps 1 and 2 again to obtain the secondary batching results, which are then transmitted to the cloud server for storage and to the display terminal for display. Step 5: Storage and Display After the product is produced, the cloud server will combine the initial and secondary batching results to calculate the optimal batching result, which will then be transmitted to the cloud server for storage and finally to the display terminal for display.
7. The casting batching method as described in claim 6, characterized in that, In step 2, the algorithm for achieving the optimal ingredient ratio is as follows: in, This indicates the weight of the i-th raw material to be added. , Indicates the accuracy of adding the i-th type of raw material. Indicates the amount of raw materials added. Z， and ； This represents the penalty term in the objective function. , This indicates the maximum total weight penalty variable added inside the furnace. This indicates the penalty coefficient for the maximum total weight added inside the furnace; This represents the lower limit of the target content of the j-th element before the furnace. This represents the upper limit of the target content of the j-th element at the furnace. This represents the unit price of the i-th raw material. This represents the content of the j-th element in the i-th raw material. This represents the maximum additional weight of the i-th raw material; This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element; This represents the actual weight of the j-th element before furnace operation. , This represents the actual content of the j-th element before furnace operation. This indicates the liquid volume in the furnace before adding fuel. This indicates the maximum total weight to be added inside the furnace.
8. The casting batching method as described in claim 6, characterized in that, In step 2, the initial batching results calculated include: in, This represents the weight of the i-th raw material to be added, and Cost represents the theoretical total cost of adding it in the furnace. This represents the unit price of the i-th raw material. This indicates the theoretical total weight added into the furnace. This represents the theoretical weight of the j-th element before furnace replenishment. This represents the content of the j-th element in the i-th raw material. This represents the yield of the j-th element from the i-th raw material. This represents the overall increase or decrease rate of the in-furnace replenishment of the j-th element. This represents the actual weight of the j-th element before furnace operation. This represents the theoretical content of the j-th element before furnace replenishment. This indicates the liquid volume in the furnace before adding fuel. This indicates the total weight of the added material.
9. The casting batching method as described in claim 6, characterized in that, The output items and output format of the secondary batching results and the optimal batching results are the same as those of the initial batching results.