Energy consumption prediction and adaptive parameter adjustment method for laser remanufacturing of waste rolls

Abstract

This invention belongs to the field of laser remanufacturing technology for rolling mill rolls. It discloses a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolling mill rolls. The method utilizes a laser remanufacturing expert system to determine the process flow and parameters for laser remanufacturing of waste rolling mill rolls, predicting the energy consumption during the remanufacturing process. A control system and an energy consumption monitoring system are used to perform remanufacturing repair and energy consumption monitoring. An energy consumption analysis system analyzes and compares the energy consumption and adaptively adjusts the parameters. This invention determines the laser remanufacturing repair process scheme based on the wear state of the waste rolling mill rolls and optimizes the process parameters by analyzing actual processing energy consumption and standard energy consumption. This allows for the rapid and accurate recommendation of optimal process parameter combinations for the remanufacturing process of waste rolling mill rolls under different wear states. It solves the problems of complex and difficult-to-determine process schemes and unpredictable energy consumption in the laser remanufacturing process of waste rolling mill rolls, improving the accuracy of energy consumption prediction and the efficiency of selecting optimal process parameters in the remanufacturing process.

Description

Technical Field

[0001] This invention belongs to the field of laser remanufacturing technology for rolls, and particularly relates to a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls. Background Technology

[0002] Currently, rolling mill rolls, as the main working components and tools on rolling mills that induce continuous plastic deformation of metal, experience fatigue wear on their surfaces under repeated alternating stress during actual rolling operations, thus affecting their use. Due to the large quantity of rolling mill rolls used, high production costs, and the fact that worn rolls are not internally damaged, laser remanufacturing repair of worn-out rolls is particularly important to save costs and avoid resource waste. Laser remanufacturing technology is an advanced repair technology in the remanufacturing field. This technology combines laser processing technology with remanufactured products, using scrap mechanical parts as remanufacturing blanks and employing laser forming technologies such as laser cladding to repair the parts and restore their shape, size, and performance. Because the remanufacturing process is complex and involves many steps, it consumes a significant amount of energy throughout the process.

[0003] Existing research on energy consumption prediction in laser remanufacturing processes mostly focuses on the energy consumption of individual processing steps, such as turning, grinding, and laser cladding. There is a lack of energy consumption prediction, monitoring, and adaptive adjustment for the entire laser remanufacturing process. Therefore, given the complexity of the processes involved in laser remanufacturing of waste rolls, the coupling relationships between energy levels in each process, and the difficulty in obtaining accurate energy consumption predictions from individual sub-processes, there is an urgent need to design a new method for energy consumption prediction and adaptive parameter adjustment in the laser remanufacturing of waste rolls to overcome the shortcomings of existing technologies.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) Existing research on energy consumption prediction in laser remanufacturing is relatively limited, and most studies only focus on energy consumption prediction and parameter optimization for a specific process in remanufacturing, such as additive manufacturing or subtractive manufacturing. Although it is possible to predict the energy consumption of a specific process and propose an optimized process plan, complete laser remanufacturing includes both additive and subtractive manufacturing processes, and these processes influence each other. Therefore, energy consumption prediction and parameter optimization for a single processing process cannot accurately predict the energy consumption of the entire laser remanufacturing process.

[0006] (2) The existing laser remanufacturing process for waste rolls is complex, with energy coupling between processes and complex processing parameters. When remanufacturing and repairing waste rolls, a lot of time is required to develop the process flow and determine the process parameters. It may also require multiple trials to modify and improve the process plan, which greatly reduces production efficiency. Therefore, a more efficient and accurate method is needed to determine the process plan and predict energy consumption. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls.

[0008] This invention uses an expert system to formulate preliminary process flow and parameters for waste rolls in different wear states and predict energy consumption. Then, the energy consumption data of the remanufacturing process is fed back to the expert system, which optimizes the process plan and records it in the system database. When processing waste rolls in the same wear state in the future, the expert system can quickly formulate the optimal combination of process parameters and predict energy consumption.

[0009] This invention is implemented as follows: a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls, comprising: determining the process flow and process parameters of laser remanufacturing of waste rolls through a laser remanufacturing expert system, and predicting the energy consumption of the remanufacturing process; performing remanufacturing repair and energy consumption monitoring on parts through a control system and an energy consumption monitoring system; and analyzing and comparing energy consumption and adaptively adjusting parameters through an energy consumption analysis system.

[0010] Furthermore, the method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls includes the following steps:

[0011] Step 1: For the laser remanufacturing process expert system database of large-volume worn waste rolls, the rolls are classified into wear levels, and a laser remanufacturing process processing scheme database and energy consumption prediction model are established. The database recommends appropriate processing flow and process parameters by analyzing the wear information of the parts. The energy consumption prediction model is a model for the expert system to predict the processing time of each process and the energy consumption required for the entire repair process based on the specified process flow and parameters.

[0012] Step 2: The control system outputs control signals based on the remanufacturing process plan, process parameters, and processing time of each process provided by the expert system. It adjusts the processing parameters of the turning machine tool and laser cladding equipment and performs laser remanufacturing repair on the parts. The route and time for the hoisting device to transfer the parts are set so that the remanufacturing process of the parts conforms to the remanufacturing process processing plan established by the expert system.

[0013] Step 3: The energy consumption monitoring system uses the multi-channel power sensors and time sensors of the data acquisition module to collect the working power and time of each system of the turning equipment, laser cladding equipment, and the lifting device, outputs the collected signals to the signal conversion interface, and uploads them to the energy consumption analysis system through the wireless network transmission module;

[0014] Step 4: The energy consumption analysis system uses the signal processing module to process the signals input by the energy consumption monitoring system to obtain the actual processing energy consumption; the energy consumption analysis module applies weights to the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value, and the analysis module compares the actual processing energy consumption with the standard energy consumption value to determine the optimal process plan.

[0015] Furthermore, the database in Step 1 includes a process flow analysis module and a process parameter analysis module.

[0016] The process flow analysis module contains the rules for classifying the wear grades of waste rolls. All batches of waste rolls are classified according to the wear area. When the wear area 0 < A c , c1 , d , c1 ,

[0017] , d , , c , ≤ A1, the part is slightly worn. When A1 < A a ≤ A2, the part is moderately worn. When A a > A2, the part is severely worn. Here, A a is the wear area, and A1 and A2 are constants; the wear grade of the part is refined according to the wear depth. Use to represent the slightly worn characteristic grade in the i-th batch of rolls, where i = 1, 2, 3…n; use to represent the moderately worn characteristic grade in the i-th batch of rolls. When the part is severely worn, it is directly scrapped; after the process flow analysis module determines the wear grade of the part, it formulates the remanufacturing process flow of the part: when the part belongs to slight wear, the processing flow is to directly perform laser cladding on the part, and after the cladding is completed, perform turning machine processing on the part to make the part reach the target size; when the part is moderately worn, then perform turning processing on the part to remove part of the worn area, then perform laser cladding repair on the part, and finally perform secondary turning to make the part reach the target size.

[0017] The process parameter analysis module for the laser remanufacturing process contains the functional relationship between the wear size data and the processing process parameters. When the part is moderately worn, the remanufacturing process is turning processing - laser cladding repair - secondary turning processing to form. The cutting depth D c1 of the first turning processing is equal to the maximum wear depth m d of the part, D c1 = m d + d c , where d cAdditional empirical dimensions added for cutting, total cutting width W and maximum wear width w d Equal, W = w d Determine the depth of cut 'a' based on the cutting depth. p1 Feed rate f1 and cutting speed v c1 The total cladding height H = D during laser cladding process c1 +h e , where h e The additional empirical dimensions for cladding are: the total width of the cladding layer is W, and the height of a single cladding layer is determined based on the total cladding height H. cl The laser power P is determined based on the height of the single-pass cladding layer. lc Scanning speed v s and powder delivery volume S p The depth of cut D during the second turning process after cladding. c2 =HD c1 The cutting width is also W. The depth of cut a during the turning process after cladding is determined based on the cutting depth. p2 Feed rate f2 and cutting speed v c2 When the part has slight wear, the remanufacturing process involves laser cladding repair followed by secondary turning. The total cladding height H during the laser cladding process is [m]. d +h e Total width W = w d The laser power P is determined based on the height of the single-pass cladding layer. lc Scanning speed v s and powder delivery volume S p ; Depth of cut D during turning after cladding c2 =h e The cutting width is W. The depth of cut a during the turning process after cladding is determined based on the cutting depth. p2 Feed rate f2 and cutting speed v c2 .

[0018] Furthermore, the energy consumption prediction model in step one includes the energy consumption of the turning process, laser cladding equipment, and hoisting equipment. After determining the remanufacturing process scheme and process parameters in the database, the model is used to predict the working time of each processing step and the total energy consumption of the remanufacturing process; the processing parameters and processing time of each step are output to the control system, and the predicted energy consumption is input to the energy consumption analysis system. The specific process of establishing the energy consumption prediction model includes:

[0019] (1) Energy consumption E during turning process T The expression for the computation function is:

[0020]

[0021] In the formula, E n Main spindle no-load energy consumption, Ec For cutting energy consumption, E a1 Energy consumption for auxiliary system control units, lighting, and cutting fluid systems; P n Main spindle no-load power, t n Spindle idle time, P c For cutting power, t c P is the cutting time. a1 For auxiliary system power, t cs This refers to the standby time of the turning machine tool. Among these, the spindle no-load power P... s With spindle speed n c The relationship between them is:

[0022] P n =a*n c 2 +b*n c +c;

[0023] In the formula, a, b, and c are all constants. The machine tool power under different scanning speeds is measured using a power analyzer, and the data is substituted into the spindle no-load power P. s With spindle speed n c From the relationship expression, an overdetermined system of equations concerning machine tool power and scanning speed is obtained, and a, b, and c are solved. Cutting power P c The expression for the computation function is:

[0024] P c =F c v c = (9.81C) F a p x f y v c z k F )*v c ;

[0025] In the formula, F c C is the cutting force; F k is a coefficient related to the machining material and tool material. F , where are correction coefficients under different cutting conditions; x, y, and z are the depth of cut, feed rate, and cutting speed exponents, which are constants.

[0026] Among them, the cutting time t c The expression for the computation function is:

[0027]

[0028] (2) Laser cladding energy consumption is divided into laser system energy consumption E l Energy consumption of water cooling system E hEnergy consumption E of laser cladding machine tool m Energy consumption E of powder feeding and protective gas system pg Energy consumption of auxiliary systems E a2 Five parts, total energy consumption E of laser cladding process LC The expression for the computation function is:

[0029] E LC =E l +E h +E m +E P +E pg +E a2 ;

[0030] The energy consumption of the laser system includes the energy consumption E during the cladding process. lc Interlayer stop light energy consumption E d and standby power consumption E ls Its calculation function expression is:

[0031]

[0032] In the formula, n is the number of cladding layers, m i Let D be the number of the i-th cladding layer, and D be the workpiece size. c This is the depth of cut for the first cut. If the first turning is not performed, then D... c =0; H cl The height of a single cladding layer; P in P is the input power of the laser. ls The standby power of the laser; t d The interlayer stop time, t ls This refers to the laser standby time. The number of cladding layers is determined by the width w of a single cladding layer. cl The lateral overlap rate μ of the cladding layer is determined; the width of a single cladding layer is determined by the laser spot diameter, which is a constant. Therefore, the number of cladding layers m is calculated as follows:

[0033]

[0034] Energy consumption of water cooling system E h Energy consumption E of water cooling system hc With standby power consumption E hs Two parts, then:

[0035]

[0036] In the formula, P hc P represents the operating power of the water cooling system. lc P is the laser power. in For the laser input power, t lc Where ρ is the cladding time, c is the cooling water density, and ρ is the cladding time.p v is the specific heat capacity of cooling water. h Where P is the cooling water flow rate, ΔT is the cooling water temperature difference, and P is the cooling water flow rate. hs For the standby power of the water-cooled system, t ls This refers to the laser's standby time.

[0037] Energy consumption E of laser cladding machine tool m The expression for the calculation function is:

[0038]

[0039] In the formula, a1, b1, and c1 are undetermined coefficients, and their values ​​are determined using the same method as those for the spindle power and speed of a turning machine tool; when the scanning speed v s When the power is 0, the machine tool performs horizontal reciprocating motion, and the machine tool power equals the standby power, i.e., c1; v m The horizontal movement speed of the machine tool spindle when the workpiece returns to the initial cladding position.

[0040] The powder feeding and protective gas systems operate simultaneously, with the operating time equal to the cladding process time t. lc Similarly, the standby time is equal to the laser standby time t. ls and the time of light stoppage between laser cladding layers t d The sum of the energy consumption E of the powder feeding and protection system pg The calculation formula is:

[0041] E pg =P pg *t lc +P pgs *(t ls +t d );

[0042] In the formula, P pg For the working power of the powder feeding and protective gas system, P pgs This refers to the standby power of the powder feeding and protective gas systems. The laser cladding auxiliary system, including the lighting system and integrated control system, assists in the laser cladding process, with a working power P. a2 The working time is a constant, encompassing the entire laser cladding process. The energy consumption E of the laser cladding auxiliary system is [not specified]. a2 The calculation formula is:

[0043]

[0044] (3) Energy consumption of hoisting device E o The energy consumption of the hoisting device is determined by the working time and the total number of operations (n). The calculation function expression is as follows:

[0045]

[0046] In the formula, i represents the number of times the device is run, i = 1, 2…n; E oi Let t be the energy consumption during the i-th load operation. oi Let P be the runtime of the i-th run. o P is the operating power of the hoisting device under load. k The unloaded operating power of the hoisting device is denoted as n. When the part is slightly worn, the hoisting device operates 2 times, n=2; when the part is moderately worn, the hoisting device operates 3 times, n=3.

[0047] By combining process path, process parameters and energy consumption prediction model, the processing time and predicted energy consumption of each process in the laser remanufacturing of waste rolls are determined.

[0048] Furthermore, in step four, the analysis module compares the actual processing energy consumption with the standard energy consumption value. When the actual processing energy consumption does not exceed the standard energy consumption value, the processing scheme used is the optimal process scheme, and the expert system records the process scheme as the optimal combination of process parameters. When processing rolls of the same wear level again, the expert system directly recommends the optimal process scheme. If the actual energy consumption is greater than the standard energy consumption, the process parameters are optimized using the energy consumption optimization model and the system's preset optimization algorithm, and the optimized parameters are fed back to the expert system's database. The database replaces the previous scheme with the new process scheme. When processing rolls of the same wear level again, the expert system selects the optimal process scheme from the database.

[0049] Standard energy consumption value E 标准 =ω*E 预测 +(1-ω)*E 实际 , where ω is the weighting coefficient, determined by the analytic hierarchy process, expert evaluation method, or fuzzy evaluation method.

[0050] Another objective of this invention is to provide an energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls, which applies the aforementioned method for energy consumption prediction and parameter adaptive adjustment in the laser remanufacturing process of waste rolls. The energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls includes: a laser remanufacturing expert system, a control system, an energy consumption monitoring system, and an energy consumption analysis system.

[0051] Among them, the laser remanufacturing expert system is used to classify the wear level of waste rolls and establish a database of laser remanufacturing process schemes and energy consumption prediction models.

[0052] The control system is used to adjust the processing parameters of turning machine tools and laser cladding equipment, and to perform laser remanufacturing repair on parts. It also sets the route and time for the hoisting device to transport parts.

[0053] The energy consumption monitoring system includes a data acquisition module and a wireless network transmission module. The data acquisition module includes a multi-channel power sensor, a time sensor, and a signal conversion interface module. The multi-channel power sensor and time sensor are used to collect the operating power and time of each system of the turning equipment, laser cladding equipment, and hoisting device. The collected signals are output to the signal conversion interface and uploaded to the energy consumption analysis system.

[0054] The energy consumption analysis system includes a signal processing module, an energy consumption analysis module, an energy consumption optimization model, and an optimization algorithm. The signal processing module processes the signals input from the monitoring device to obtain the actual processing energy consumption. The energy consumption analysis module applies weights to the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value. The analysis module compares the actual processing energy consumption with the standard energy consumption value. If the actual energy consumption is greater than the standard energy consumption, the process parameters are optimized using the energy consumption optimization model and the system's preset optimization algorithm.

[0055] Furthermore, the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls also includes: installing data acquisition modules in the spindle system, feed system, auxiliary system of the turning machine tool, laser cladding laser system, water cooling system, laser cladding special machine tool system, powder feeding and protective gas system, auxiliary system and hoisting device.

[0056] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, causing the processor to perform the steps of the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls.

[0057] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls.

[0058] Another objective of this invention is to provide an information data processing terminal for realizing the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls.

[0059] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0060] First, this invention establishes a laser remanufacturing expert system for the remanufacturing process of large-scale waste rolls in different wear states. The expert system includes specific wear level classification rules, a process scheme database, and an energy consumption prediction model. Based on the different wear states of the waste rolls, the expert system can quickly formulate the remanufacturing process flow and parameters. After determining the process scheme, it predicts the remanufacturing energy consumption and uses real-time monitoring of the processing to determine whether the actual energy consumption exceeds the standard energy consumption. If it does not meet the standard, the energy consumption optimization model and algorithm in the expert system optimize the process parameters to obtain the optimal remanufacturing process scheme. This invention solves the problems of complex and difficult-to-determine process schemes and unpredictable energy consumption in the laser remanufacturing process of waste rolls. When repairing waste rolls in different wear states, it can quickly determine the remanufacturing process flow and parameters, and when the actual energy consumption does not meet the standard, it can use an adaptive parameter adjustment method to optimize the process parameters, improving the accuracy of energy consumption prediction and the efficiency of selecting optimal process parameters in the remanufacturing process.

[0061] Secondly, the energy consumption prediction and adaptive parameter adjustment method for remanufacturing waste rolls proposed in this invention can quickly determine the optimal combination of remanufacturing process schemes for different rolls when processing large batches of waste rolls with different wear conditions. Especially when processing waste rolls with similar wear conditions, the process parameters can be further optimized based on the original schemes in the database. Using the method proposed in this invention, the number of trial and error times and time in the laser remanufacturing process can be greatly reduced when processing large batches of waste rolls with different wear conditions, thereby reducing labor, energy consumption, and time costs and improving the production efficiency of the waste roll laser remanufacturing production line.

[0062] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0063] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: By adopting the technical solution of the present invention, enterprises can quickly estimate the energy consumption, time and labor cost required to process a batch of waste rolls, and further optimize the remanufacturing energy consumption, reduce processing energy consumption and improve production efficiency.

[0064] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally: previous studies on remanufacturing energy consumption have mostly focused on single additive or subtractive manufacturing processes, lacking research on energy consumption prediction and parameter optimization for the entire laser remanufacturing process. This invention takes the entire laser remanufacturing process of large batches of waste rolls with different wear conditions as the research object, and proposes a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls. By adopting the technical solution in this invention, the energy consumption of the laser remanufacturing process of waste rolls can be predicted quickly and accurately. Furthermore, based on the predicted energy consumption, the parameters are optimized to obtain the optimal combination of processing parameters for laser remanufacturing of waste rolls. Attached Figure Description

[0065] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention 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.

[0066] Figure 1 This is a flowchart of the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls provided in this embodiment of the invention;

[0067] Figure 2 This is a schematic diagram of the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls provided in this embodiment of the invention;

[0068] Figure 3 This is a flowchart of the adaptive adjustment of process parameters in the laser remanufacturing process provided in this embodiment of the invention;

[0069] Figure 4 This is a flowchart of expert system formulation, process parameters and predicted energy consumption provided in the embodiments of the present invention;

[0070] Figure 5 This is a schematic diagram illustrating the process parameters determination and energy consumption prediction for the laser remanufacturing of moderately worn rolls provided in this embodiment of the invention.

[0071] Figure 6 This is a schematic diagram of the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls provided in this embodiment of the invention;

[0072] Figure 7 This is an information interaction diagram of the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls provided in this embodiment of the invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0074] To address the problems existing in the prior art, this invention provides a method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls. The invention will be described in detail below with reference to the accompanying drawings.

[0075] like Figure 1 As shown, the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls provided in this embodiment of the invention includes the following steps:

[0076] S101, using a laser remanufacturing expert system to determine the laser remanufacturing process flow and process parameters of waste rolls, and predict the energy consumption of the remanufacturing process;

[0077] S102, using a control system and an energy consumption monitoring system to remanufacture and repair parts and monitor energy consumption;

[0078] S103 utilizes an energy consumption analysis system to analyze and compare energy consumption and adaptively adjust parameters.

[0079] As a preferred embodiment, such as Figure 2 As shown, the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls provided in this embodiment of the invention specifically includes the following steps:

[0080] S1: An expert system database for the laser remanufacturing process of large-volume worn scrap rolls is established. The rolls are classified by wear level, and a database of laser remanufacturing process plans and an energy consumption prediction model are created. The database recommends suitable processing flows and parameters by analyzing part wear information. The energy consumption prediction model is a model used by the expert system to predict the processing time of each process and the energy consumption required for the entire repair process based on the specified process flow and parameters. The database and energy consumption prediction model are established as follows:

[0081] S1.1: The database mainly includes a process flow analysis module and a process parameter analysis module.

[0082] S1.1.1: The process flow analysis module includes the wear level classification rules for scrap rolls: all batches of scrap rolls are classified according to the wear area. When the wear area is 0... a When A1 is less than or equal to A1, the part is slightly worn. a When A2 is ≤A2, the part is considered to have moderate wear. a >At A2, the part is severely worn, where A a Let A1 and A2 be the area of ​​the wear region, and A1 and A2 be constants. The wear level of the part is then refined based on the wear depth, i.e., using... This represents the slight wear characteristic level in the i-th batch of rolls, where i = 1, 2, 3…n, denoted by . This indicates the moderate wear characteristic level in the i-th batch of rolls. When a part is severely worn, it is directly scrapped. After determining the wear level of the part, the process flow analysis module further formulates the remanufacturing process flow for the part: when When the part has only slight wear, the processing flow is to directly perform laser cladding on the part, and then perform machining on a turning machine to bring the part to the target size; when ​​When a part is moderately worn, it is necessary to first turn the part to remove part of the worn area, then laser cladding repair the part, and finally turn it a second time to make the part reach the target size.

[0083] S1.1.2: The laser remanufacturing process parameter analysis module includes the functional relationship between wear dimension data and processing parameters. Specifically, when a part is moderately worn, its remanufacturing process is turning – laser cladding repair – secondary turning to shape, where the cutting depth D of the first turning operation... c1 Maximum wear depth of part m d Equal, that is, D c1 =m d +d c d c Additional empirical dimensions added for cutting, total cutting width W and maximum wear width w d They are equal, that is, W = w d Determine the depth of cut 'a' based on the cutting depth. p1 Feed rate f1 and cutting speed v c1 The total cladding height H = D during laser cladding process c1 +h e , where h e The additional empirical dimensions for cladding are: the total width of the cladding layer is W, and the height of a single cladding layer is determined based on the total cladding height H. cl The laser power P is determined based on the height of the single cladding layer. lc Scanning speed v s and powder delivery volume S p The depth of cut D during the second turning process after cladding. c2 =HD c1 The cutting width is also W. The depth of cut a during the turning process after cladding is determined based on the cutting depth. p2 Feed rate f2 and cutting speed v c2 .

[0084] When a part has only minor wear, its remanufacturing process involves laser cladding repair followed by secondary turning. The total cladding height H during the laser cladding process is [m]. d +h e Total width W = w d Similarly, the laser power P is determined based on the height of a single cladding layer. lc Scanning speed v s and powder delivery volume S p The depth of cut D during the turning process after cladding. c2 =h e The cutting width is W. The depth of cut a during the turning process after cladding is determined based on the cutting depth. p2 Feed rate f2 and cutting speed v c2.

[0085] As a preferred embodiment, the energy consumption prediction and adaptive parameter adjustment method for the laser remanufacturing process of waste rolls provided by this invention includes the following steps:

[0086] Step 1: Establish an expert system for the laser remanufacturing process of large-scale wear-related failures of scrap rolls. This expert system includes a processing scheme database, an energy consumption prediction model, an energy consumption analysis module, and a multi-objective optimization model and algorithm. The processing scheme database contains wear level classification rules for scrap rolls. After obtaining part wear information from the expert system, the database analyzes the wear information and, in conjunction with the process flow analysis module and parameter analysis module in the database, formulates appropriate processing flow and parameters. After determining the processing flow and parameters in the database, the data is provided to the energy consumption prediction model to predict the processing time of each process and the energy consumption required for the entire repair process.

[0087] Step two: The expert system provides the predicted remanufacturing process plan and the processing time of each process to the control system; the control system outputs control signals based on the process parameter data provided by the expert system to control the operation of the turning machine tool and the laser cladding equipment and adjust the process parameters; based on the process flow and processing time of each process provided by the expert system, the control system outputs control signals to regulate the route and time of the hoisting device to transfer the parts so that the laser remanufacturing process of the parts conforms to the remanufacturing process plan formulated by the expert system.

[0088] Step 3: The energy consumption monitoring system uses the multi-channel power sensor and time sensor of the data acquisition module to collect the working power and time of each system of the turning equipment, laser cladding equipment and hoisting device, outputs the collected signals to the signal conversion interface, and uploads them to the energy consumption analysis system through the wireless network transmission module.

[0089] Step four: The energy consumption analysis module in the expert system processes the signal input from the energy consumption monitoring system and calculates the actual processing energy consumption. Then, by applying weights to the actual processing energy consumption and the predicted energy consumption and calculating the standard energy consumption value, the analysis module compares the actual processing energy consumption with the standard energy consumption value to determine the optimal laser remanufacturing process scheme.

[0090] The adaptive adjustment process flow of laser remanufacturing process parameters provided in this embodiment of the invention is as follows: Figure 3As shown, after acquiring roll wear information, the expert system classifies the rolls into wear levels based on the wear area, and determines the process flow and process parameters based on the wear level and wear data. Further, the expert system provides the process plan to the energy consumption prediction model, which predicts energy consumption based on the process flow and parameters, and provides the predicted energy consumption to the energy consumption analysis system. The control system converts the received process plan into control signals to regulate the processing equipment and hoisting devices. During actual processing, the energy consumption monitoring system monitors the real-time energy consumption of the production line and feeds it back to the expert system. The expert system's energy consumption analysis module compares the actual energy consumption with the standard energy consumption. If the actual energy consumption does not meet the standard, the process parameters are optimized using the energy consumption optimization model and algorithm, and the optimization results are fed back to the database for recording. If the actual energy consumption meets the standard energy consumption, the optimal process parameters are directly output.

[0091] The energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls provided in this invention uses an expert system to formulate preliminary process flow and parameters for waste rolls in different wear states and predict energy consumption. Then, the energy consumption data of the remanufacturing process monitored is fed back to the expert system, which optimizes the process plan and records it in the system database. When processing waste rolls in the same wear state in the future, the expert system can quickly formulate the optimal combination of process parameters and predict energy consumption.

[0092] Expert system-based development of process parameters and energy consumption prediction processes, such as... Figure 4 As shown, the process flow analysis module in the expert system determines the wear level of the parts based on the roll wear information. If the parts are slightly worn, they are directly hoisted to the laser cladding equipment for laser cladding and turning; if the parts are moderately worn, they need to be hoisted to the turning equipment for laser cladding and secondary turning; if the parts are severely worn, they are scrapped. After determining the process flow, the process parameter analysis module determines the process parameter scheme required for the remanufacturing process based on the wear size data, and provides the process parameter scheme to the energy consumption prediction model, ultimately obtaining the complete laser remanufacturing process flow, parameter scheme, predicted energy consumption, and predicted processing time for each process.

[0093] S1.2: The energy consumption prediction model is mainly divided into energy consumption for the turning process, laser cladding equipment, and hoisting equipment. After determining the remanufacturing process scheme and process parameters in the database, the model is used to predict the working time of each processing step and the total energy consumption of the remanufacturing process. The processing parameters and processing time of each step are output to the control system, and the predicted energy consumption is input to the energy consumption analysis system. The specific method for establishing the energy consumption prediction model is as follows:

[0094] S1.2.1: Energy consumption E during turning process T The expression for the computation function is:

[0095]

[0096] In the formula, E n Main spindle no-load energy consumption, E c For cutting energy consumption, E a1 Energy consumption of auxiliary systems, such as controllers and coolant pumps; P n Main spindle no-load power, t n Spindle idle time, P c For cutting power, t c P is the cutting time. a1 For auxiliary system power, t cs This refers to the standby time of the turning machine tool. The spindle no-load power P is included. s With spindle speed n c The relationship between them is:

[0097] P n =a*n c 2 +b*n c +c(2)

[0098] In the formula, a, b, and c are constants. The machine tool power is measured using a power analyzer at different scanning speeds, and the data is substituted into the above formula to obtain an overdetermined set of equations concerning machine tool power and scanning speed. Then, a, b, and c are calculated. Cutting power P c The expression for the computation function is:

[0099] P c =F c v c = (9.81C) F a p x f y v c z k F )*v c (3)

[0100] In the formula, F c For cutting force, C F k is a coefficient related to the machining material and the tool material. F Here, x, y, and z are correction factors under different cutting conditions, representing the depth of cut, feed rate, and cutting speed, respectively, and are fixed values. Cutting time t c The expression for the computation function is:

[0101]

[0102] S1.2.2: Laser cladding energy consumption is divided into laser system energy consumption E l Energy consumption of water cooling system E hEnergy consumption E of laser cladding machine tool m Energy consumption E of powder feeding and protective gas system pg Energy consumption of auxiliary systems E a2 Five parts, therefore the total energy consumption E of the laser cladding process LC The expression for the computation function is:

[0103] E LC =E l +E h +E m +E P +E pg +E a2 (5)

[0104] The energy consumption of the laser system mainly includes the energy consumption E during the cladding process. lc Interlayer stop light energy consumption E d and standby power consumption E ls Its calculation function expression is:

[0105]

[0106] In the formula, n is the number of cladding layers, m i Let D be the number of the i-th cladding layer, and D be the workpiece size. c H represents the depth of cut. cl P represents the height of a single cladding layer. in P is the input power of the laser. ls t represents the standby power of the laser, which is a constant. d t represents the interlayer stop time. ls This refers to the laser standby time. The number of cladding layers is determined by the width w of a single cladding layer. cl The lateral overlap rate μ of the cladding layer is determined, and the width of a single cladding layer is determined by the laser spot diameter, which is a fixed value. Therefore, the calculation method for the number of cladding layers m is as follows:

[0107]

[0108] Energy consumption of water cooling system E h Energy consumption E of water cooling system hc With standby power consumption E hs Two parts, namely:

[0109]

[0110] In the formula, P hc P represents the operating power of the water-cooling system. lc For laser power, t lc Where ρ is the cladding time, c is the cooling water density, and ρ is the cladding time. p v is the specific heat capacity of cooling water. hWhere P is the cooling water flow rate, ΔT is the cooling water temperature difference, and P is the cooling water flow rate. hs For the standby power of the water-cooled system, t ls This refers to the laser's standby time.

[0111] Energy consumption E of laser cladding machine tool m The expression for the calculation function is:

[0112]

[0113] In the formula, a1, b1, and c1 are undetermined coefficients, and their values ​​are determined using the same method as for the spindle power and speed of a turning machine tool. When the scanning speed v... s When the value is 0, the machine tool is performing horizontal reciprocating motion. At this time, the machine tool power is the standby power, which is c1,v. m The horizontal movement speed of the machine tool spindle when the workpiece returns to the initial cladding position.

[0114] The powder feeding and protective gas systems operate simultaneously, and their operating time is equal to the cladding process time t. lc Similarly, the standby time is equal to the laser standby time t. ls and the time of light stoppage between laser cladding layers t d The sum of these, therefore the energy consumption E of the powder feeding and protection system. pg The calculation formula is:

[0115] E pg =P pg *t lc +P pgs *(t d +t ls (10)

[0116] In the formula, P pg For the working power of the powder feeding and protective gas system, P pgs This refers to the standby power of the powder feeding and protective gas system. The laser cladding auxiliary system includes systems that assist in laser cladding operations, such as the lighting system and integrated control system, with a power P... a2 The working time is a fixed value, and it refers to the entire laser cladding process. Auxiliary system energy consumption E a2 The calculation formula is:

[0117]

[0118] S1.2.3: Energy consumption E of hoisting device o The energy consumption of the hoisting device is mainly determined by the working time and the total number of operations (n) of the hoisting equipment. Therefore, the calculation function expression for the energy consumption of the hoisting device is:

[0119]

[0120] In the formula, i (i = 1, 2…n) represents the number of times the device is run, and Eoi Let t be the energy consumption during the i-th load operation. oi Let P be the runtime of the i-th run. o P is the operating power of the hoisting device under load. k The unloaded operating power of the hoisting device is denoted as n. When the part is slightly worn, the hoisting device needs to run 2 times, and n = 3. When the part is moderately worn, the hoisting device needs to run 3 times, and n = 3.

[0121] Combining the process path, process parameters, and energy consumption prediction model in the above steps, the processing time and predicted energy consumption of each process in the laser remanufacturing of waste rolls are determined. The process parameter determination and energy consumption prediction process for the laser remanufacturing of moderately worn rolls provided in this embodiment of the invention are as follows: Figure 5 As shown. In the first turning process, the cutting depth and width are determined based on the wear depth and width, further determining the depth of cut, feed rate, and cutting speed. Based on the first cutting depth and width, the total cladding height and cladding width of the laser cladding are determined, further determining the number of cladding layers and the height of a single cladding layer, thus obtaining the laser power, scanning speed, and powder feed rate required for the laser cladding process. In the second turning process, the difference between the total laser cladding height and the part forming size is used as the cutting depth, and the cladding width is used as the turning width, further determining the depth of cut, feed rate, and cutting speed for the second turning process. After all process parameters for the three machining processes are determined, the expert system's energy consumption prediction model predicts the energy consumption of the laser remanufacturing process based on the process parameters.

[0122] S2: The control system outputs control signals based on the remanufacturing process flow, process parameters and processing time of each process provided by the expert system. It then adjusts the processing parameters of the turning machine tool and laser cladding equipment to perform laser remanufacturing repair on the parts and sets the route and time for the hoisting device to transfer the parts, so that the remanufacturing process of the parts conforms to the remanufacturing processing plan given by the expert system.

[0123] S3: The energy consumption monitoring device includes a data acquisition module and a wireless network transmission module. Data acquisition modules are installed in the spindle system, feed system, auxiliary system, laser cladding laser system, water cooling system, laser cladding dedicated machine tool system, powder feeding and protective gas system, auxiliary system, and hoisting device of the turning machine tool. The data acquisition module includes a multi-channel power sensor, a time sensor, and a signal conversion interface module. The multi-channel power sensor and time sensor collect the operating power and time of each system in the turning equipment, laser cladding equipment, and hoisting device. The collected signals are then output to the signal conversion interface and uploaded to the energy consumption analysis system via the signal conversion interface.

[0124] S4: The energy consumption analysis system mainly includes a signal processing module, an energy consumption analysis module, an energy consumption optimization model, and an optimization algorithm. The signal processing module processes the signals input from the monitoring device to obtain the actual processing energy consumption. The energy consumption analysis module weights the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value. Then, the analysis module compares the actual processing energy consumption with the standard energy consumption value. When the actual processing energy consumption does not exceed the standard energy consumption value, it indicates that the processing technology is optimal. In this case, the expert system only needs to record this technology as the optimal combination of process parameters. When processing rolls with the same wear level again, the expert system can directly recommend the optimal technology. Conversely, if the actual energy consumption is greater than the standard energy consumption, the energy consumption optimization model and the system's preset optimization algorithm are used to optimize the process parameters. The optimized parameters are then fed back to the expert system's database. The database replaces the previous optimization scheme with the new one. When processing rolls with the same wear level again, the expert system can directly select the optimal technology from the database. The standard energy consumption value E in the above process... 标准 =ω*E 预测 +(1-ω)*E 实际 , where ω is the weighting coefficient, which can be determined by the analytic hierarchy process, expert evaluation method, or fuzzy evaluation method.

[0125] like Figure 6 As shown in the embodiment of the present invention, the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls mainly includes: a laser remanufacturing expert system, a control system, and an energy consumption monitoring system. The expert system includes a process scheme database (process flow analysis module and process parameter analysis module), an energy consumption prediction model, an energy consumption analysis module, and a multi-objective optimization model and algorithm. The control system mainly controls the hoisting and processing equipment by receiving the process flow and process parameter schemes from the expert system. The energy consumption monitoring system includes a data acquisition module and a wireless network transmission module, whose main function is to collect real-time processing energy consumption data from each device and feed the data back to the energy consumption analysis module of the expert system.

[0126] Among them, the laser remanufacturing expert system is used to classify the wear level of waste rolls and establish a database of laser remanufacturing process schemes and energy consumption prediction models.

[0127] The control system is used to adjust the processing parameters of turning machine tools and laser cladding equipment, and to perform laser remanufacturing repair on parts. It also sets the route and time for the hoisting device to transport parts.

[0128] The energy consumption monitoring system includes a data acquisition module and a wireless network transmission module. The data acquisition module is installed in the spindle system, feed system, auxiliary system, laser cladding laser system, water cooling system, laser cladding dedicated machine tool system, powder feeding and protective gas system, auxiliary system, and hoisting device of the turning machine tool. The data acquisition module includes a multi-channel power sensor, a time sensor, and a signal conversion interface module. The multi-channel power sensor and time sensor are used to collect the operating power and time of each system of the turning equipment, laser cladding equipment, and hoisting device, and output the collected signals to the signal conversion interface and upload them to the energy consumption analysis system.

[0129] The energy consumption analysis system includes a signal processing module, an energy consumption analysis module, an energy consumption optimization model, and an optimization algorithm. The signal processing module processes the signals input from the monitoring device to obtain the actual processing energy consumption. The energy consumption analysis module applies weights to the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value. The analysis module compares the actual processing energy consumption with the standard energy consumption value. If the actual energy consumption is greater than the standard energy consumption, the process parameters are optimized using the energy consumption optimization model and the system's preset optimization algorithm.

[0130] The information interaction diagram of the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls provided in this embodiment of the invention is shown below. Figure 7 As shown in the diagram, the specific implementation steps are as follows: the energy consumption prediction and parameter adjustment system sends wear level classification instructions to the expert system, which then returns the roll wear level. The expert system further provides the waste roll wear level data to the control system. The control system, under the instructions of processing parameters, route, and time, completes the laser remanufacturing process and feeds back the processing parameters, route, and time data to the energy consumption prediction and parameter adjustment system. The energy consumption monitoring system collects the working power and time of the entire processing process and provides the data to the energy consumption analysis system. Simultaneously, it feeds back the monitored working power and time data of each system to the energy consumption prediction and parameter adjustment system. Finally, the energy consumption analysis system analyzes the system working power and time, and feeds back the energy consumption analysis results and optimal process scheme of the waste roll laser remanufacturing process to the energy consumption prediction and parameter adjustment system.

[0131] This invention can be applied to the remanufacturing and repair process of large batches of waste rolls in different wear states, such as in the actual processing of the first batch of rolls (roll serial number: During remanufacturing and repair, the first roll in this batch is identified through an expert system. The wear level is determined, and the processing flow and parameters are formulated. The roll is then processed, and energy consumption is monitored in real time and fed back to an expert system. The expert system determines whether the actual processing energy consumption exceeds the standard value. If it does not exceed the standard value, the process parameters for processing the first roll are the optimal parameters. If the actual energy consumption exceeds the standard value, the process parameters need to be optimized, and the expert system records the optimized process parameter scheme. When subsequent processing encounters... When dealing with rolls of the same wear level, the expert system can directly output the optimized combination of process parameters. By repeating the above method, once a certain amount of scrap rolls has been processed, the expert system can complete the formulation and recording of laser remanufacturing process plans for scrap rolls of various wear levels. When encountering scrap rolls of the same wear level in subsequent processing, the expert system can quickly and accurately formulate its processing plan and predict processing energy consumption.

[0132] In a preferred embodiment, the expert system provided by this invention mainly includes a wear characteristic level classification rule for parts, a process scheme database, and an energy consumption prediction model. The wear characteristic level classification rule is: 0~0.8mm. 2 Slight wear, 0.8–2.4 mm 2 Moderate wear, greater than 2.4mm. 2 It is severely worn.

[0133] With a diameter D = 80 mm and a wear area A a =1.5mm, wear depth m d Taking a 4mm thick scrap roll as an example, laser remanufacturing repair is performed. The process database in the expert system determines its wear level as moderate wear based on the wear area. Therefore, the selected processing flow is turning, laser cladding, and secondary turning. The depth of cut for the first turning is determined based on the wear depth. The selection schemes for feed rate and cutting speed are shown in Table 1.

[0134] Table 1. Selection Scheme for Feed Rate and Cutting Speed

[0135]

[0136] Based on the above process, the depth of cut 'a' in the turning process is determined. p1 Feed rate f1 and cutting speed v c1 .

[0137] The method for determining laser cladding equipment parameters is as follows: Determine the required number of laser cladding layers based on the total laser cladding height; determine the single-pass cladding layer height based on the number of cladding layers; and determine the laser cladding process parameters based on the single-pass cladding layer height. It is known that the cladding layer height is mainly affected by laser power, scanning speed, and powder feed rate, and these effects are relatively minor. Therefore, the range of single-pass cladding layer heights can be determined experimentally, and the maximum value is taken. and minimum value This allows us to calculate the minimum number of cladding layers, n, during the cladding process. min With the maximum value n max They are respectively:

[0138]

[0139]

[0140] In the formula, λ is the longitudinal overlap rate, which is generally chosen to be 15%. Further, the required number of cladding layers is determined based on the maximum and minimum values ​​of the desired number of layers. The value of n is rounded up to the nearest integer. The cladding layer height is further determined by the total height of the cladding layer and the number of cladding layers:

[0141]

[0142] Meanwhile, the relationship between the height of a single cladding layer and the process parameters is known to be:

[0143] H cl =u(P lc α v s βS p γ )+v (16)

[0144] In the formulas, u, v, α, β, and γ are all constants. The height of the cladding layer and parameter data were obtained through multiple experiments, and the specific values ​​were obtained through fitting. The laser power P in the laser cladding process was determined by formulas (15) and (16). lc Scanning speed v s and powder delivery volume S p .

[0145] After laser cladding is completed, the cutting depth D of the second turning is determined based on the height after cladding. c2 =HD c1 To further determine the depth of cut Further determine the feed rate f2 and cutting speed v for the second turning operation based on Table 1. c2 .

[0146] As a preferred embodiment, the energy consumption optimization model provided in this invention aims to minimize energy consumption and the flatness of the machined surface. A multi-objective optimization model is established to optimize the process parameters involved in the laser remanufacturing process. The process parameters to be optimized include: the depth of cut 'a' in the turning process. p Feed rate f, cutting speed v c Laser power P lc Scanning speed v s and powder delivery volume S pThe multi-objective optimization model is optimized by analyzing other commonly used multi-objective optimization algorithms such as particle swarm optimization, genetic algorithm, or NSGA-II algorithm preset in the system. Since this invention only involves the application of such optimization algorithms and does not require improvement of the above algorithms, the implementation of the above algorithms will not be described in detail.

[0147] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolling mill rolls, characterized in that, The method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls includes the following steps: Step 1: For the laser remanufacturing process expert system database of large-volume worn waste rolls, the rolls are classified into wear levels, and a laser remanufacturing process processing scheme database and energy consumption prediction model are established. The database recommends appropriate processing flow and process parameters by analyzing the wear information of the parts. The energy consumption prediction model is a model for the expert system to predict the processing time of each process and the energy consumption required for the entire repair process based on the specified process flow and parameters. Step 2: The control system outputs control signals based on the remanufacturing process plan, process parameters, and processing time of each process provided by the expert system. It adjusts the processing parameters of the turning machine tool and laser cladding equipment and performs laser remanufacturing repair on the parts. The route and time for the hoisting device to transfer the parts are set so that the remanufacturing process of the parts conforms to the remanufacturing process processing plan established by the expert system. Step 3: The energy consumption monitoring system uses the multi-channel power sensor and time sensor of the data acquisition module to collect the working power and time of each system of the turning equipment, laser cladding equipment and hoisting device, outputs the collected signals to the signal conversion interface, and uploads them to the energy consumption analysis system through the wireless network transmission module. Step four: The energy consumption analysis system uses the signal processing module to process the signal input from the energy consumption monitoring system to obtain the actual processing energy consumption; the energy consumption analysis module applies weights to the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value; the analysis module compares the actual processing energy consumption with the standard energy consumption value to determine the optimal process scheme. The database in Step 1 includes a process flow analysis module and a process parameter analysis module. The process flow analysis module contains the wear level classification rules for scrap rolls, classifying all batches of scrap rolls according to the wear area. The parts were slightly worn at that time. The parts are moderately worn at that time. The parts were severely worn, among which The area of ​​the wear zone. , It is a constant; the wear level of the part is refined according to the wear depth, using... This indicates the minor wear characteristic level in the i-th batch of rolls, where ;use This indicates the moderate wear characteristic level in the i-th batch of rolls. When a part is severely worn, it is directly scrapped. After determining the wear level of the part, the process flow analysis module formulates the remanufacturing process flow for the part: when the part... When the part has only slight wear, the processing procedure involves directly laser cladding the part, followed by machining on a turning machine to achieve the target dimensions; when the part... When the part is moderately worn, it is machined to remove part of the worn area, then laser cladding is performed to repair the part, and finally a second turning is performed to make the part reach the target size. The energy consumption prediction model in step one includes the energy consumption of the turning process, laser cladding equipment, and hoisting equipment; after determining the remanufacturing process scheme and process parameters in the database, the model is used to predict the working time of each processing process and the total energy consumption of the remanufacturing process; the processing parameters and processing time of each process are output to the control system, and the predicted energy consumption is input to the energy consumption analysis system. In step four, the analysis module compares the actual processing energy consumption with the standard energy consumption value. When the actual processing energy consumption does not exceed the standard energy consumption value, the processing scheme is the optimal process scheme, and the expert system records the process scheme as the optimal combination of process parameters. When processing rolls with the same wear level again, the expert system directly recommends the optimal process scheme. If the actual energy consumption is greater than the standard energy consumption, the energy consumption optimization model and the system's preset optimization algorithm are used to optimize the process parameters, and the optimized parameters are fed back to the expert system's database. The database replaces the previous scheme with the new process scheme. When processing rolls with the same wear level again, the expert system selects the optimal process scheme from the database. Standard energy consumption value ,in The weighting coefficients are determined using the analytic hierarchy process, expert evaluation method, or fuzzy evaluation method.

2. The method for energy consumption prediction and adaptive parameter adjustment in the laser remanufacturing process of waste rolls as described in claim 1, characterized in that, The laser remanufacturing process parameter analysis module includes the functional relationship between wear dimension data and machining process parameters. When the part is moderately worn, the remanufacturing process is turning - laser cladding repair - secondary turning to shape, and the cutting depth of the first turning is... Maximum wear depth of parts equal, ,in Additional empirical dimensions added for cutting, total cutting width With maximum wear width equal, Determine the depth of cut based on the cutting depth. Feed rate and cutting speed Total cladding height during laser cladding process ,in The additional empirical dimensions added for cladding, the total width of the cladding layer is The height of a single cladding layer is determined based on the total cladding height. Laser power determined based on the height of a single cladding layer Scanning speed and powder delivery volume Depth of cut in the second turning process after cladding The cutting width is also The depth of cut is determined based on the cutting depth during the turning process after cladding. Feed rate and cutting speed When the part has slight wear, the remanufacturing process involves laser cladding repair followed by secondary turning to shape the part. The total cladding height during the laser cladding process is... Total width Laser power is determined based on the height of a single cladding layer. Scanning speed and powder delivery volume Depth of cut during turning after cladding The cutting width is The depth of cut is determined based on the cutting depth during the turning process after cladding. Feed rate and cutting speed .

3. The method for energy consumption prediction and adaptive parameter adjustment in the laser remanufacturing process of waste rolls as described in claim 1, characterized in that, The process of establishing the energy consumption prediction model in step one includes: (1) Energy consumption during turning process The expression for the computation function is: ； In the formula, Main spindle no-load energy consumption, For cutting energy consumption, Energy consumption for auxiliary system controllers and cutting fluid pump systems; Main spindle no-load power, Spindle idle time, For cutting power, For cutting time, To assist system power, This refers to the standby time of the turning machine tool; among which, the spindle no-load power... With spindle speed The relationship between them is: ； In the formula, a, b, and c are all constants. The machine tool power is measured by a power analyzer under different scanning speeds, and the data is substituted into the spindle no-load power. With spindle speed From the relational expression, an overdetermined system of equations concerning machine tool power and scanning speed is obtained, and the following equations are solved. , , Among them, cutting power The expression for the computation function is: ； In the formula, For cutting force; These are coefficients related to the machining materials and tool materials; These are correction factors for different cutting conditions; The depth of cut, feed rate, and cutting speed exponent are fixed values; among them, the cutting time... The expression for the computation function is: ； (2) Laser cladding energy consumption is divided into laser system energy consumption. Energy consumption of water cooling system Energy consumption of laser cladding machine tools Energy consumption of powder feeding and protective gas systems and auxiliary system energy consumption Five parts, total energy consumption of laser cladding process The expression for the computation function is: ； The energy consumption of the laser system includes the energy consumption of the cladding process. Inter-layer light-stop energy consumption and standby power consumption The expression for the calculation function is: ； In the formula, This refers to the number of cladding layers. For the first Number of cladding layers For workpiece dimensions, For cutting depth, This refers to the height of a single cladding layer; Input power to the laser, This represents the standby power of the laser, which is a constant. Interlayer stop time, This refers to the laser standby time; where the number of cladding layers is determined by the width of a single cladding layer. Lateral overlap rate of cladding layer The width of a single cladding layer is determined by the laser spot diameter, which is a fixed value. Therefore, the number of cladding layers... The calculation method is as follows: ； Energy consumption of water cooling system Energy consumption is divided into water cooling system operation. Standby power consumption Two parts, then: ； In the formula, For the operating power of the water cooling system, For laser power, Input power to the laser, For cladding time, For the density of cooling water, The specific heat capacity of cooling water, For cooling water flow rate, For the cooling water temperature difference, This refers to the standby power of the water-cooling system. This refers to the laser's standby time. Energy consumption of laser cladding machine tools The expression for the calculation function is: ； In the formula, , , These are undetermined coefficients, and the method for determining their values ​​is the same as that for the spindle power and speed of a turning machine tool; when the scanning speed... When the power is 0, the machine tool performs horizontal reciprocating motion, and the machine tool power is the standby power. ; The horizontal traverse speed of the machine tool spindle when the workpiece returns to the initial cladding position; The powder feeding and protective gas systems operate simultaneously, with the operating time equal to the cladding process time. Similarly, the standby time is equal to the laser's standby time. and laser cladding interlayer light stop time The sum of the energy consumption of the powder feeding and protection system The calculation formula is: ； In the formula, For the working power of the powder feeding and protective gas system, The standby power is for the powder feeding and protective gas systems; the laser cladding auxiliary system includes a lighting system and an integrated control system to assist in laser cladding operations, with power... The working time is a constant, referring to the entire laser cladding process; among which, the energy consumption of the laser cladding auxiliary system is... The calculation formula is: ； (3) Energy consumption of hoisting device Working time and total number of operations of hoisting equipment The energy consumption calculation function expression for the hoisting device is determined to be: ； In the formula, For the number of times the device is run, ; For the first Energy consumption during secondary load operation For the first Run time The load operating power of the hoisting device, This represents the no-load operating power of the hoisting device; when the parts are only slightly worn, the hoisting device will operate twice. When the parts are moderately worn, the hoisting device operates 3 times. ; By combining process path, process parameters and energy consumption prediction model, the processing time and predicted energy consumption of each process in the laser remanufacturing of waste rolls are determined.

4. A system for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls, applying the energy consumption prediction and parameter adaptive adjustment method described in any one of claims 1 to 3, characterized in that, The energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls includes: a laser remanufacturing expert system, a control system, an energy consumption monitoring system, and an energy consumption analysis system; Among them, the laser remanufacturing expert system is used to classify the wear level of waste rolls and establish a database of laser remanufacturing process schemes and energy consumption prediction models. The control system is used to adjust the processing parameters of turning machine tools and laser cladding equipment, and to perform laser remanufacturing repair on parts. It also sets the route and time for the hoisting device to transport parts. The energy consumption monitoring system includes a data acquisition module and a wireless network transmission module. The data acquisition module includes a multi-channel power sensor, a time sensor, and a signal conversion interface module. The multi-channel power sensor and time sensor are used to collect the operating power and time of each system of the turning equipment, laser cladding equipment, and hoisting device. The collected signals are output to the signal conversion interface and uploaded to the energy consumption analysis system. The energy consumption analysis system includes a signal processing module, an energy consumption analysis module, an energy consumption optimization model, and an optimization algorithm. The signal processing module processes the signals input from the monitoring device to obtain the actual processing energy consumption. The energy consumption analysis module applies weights to the actual processing energy consumption and the predicted energy consumption and calculates the standard energy consumption value. The analysis module compares the actual processing energy consumption with the standard energy consumption value. If the actual energy consumption is greater than the standard energy consumption, the process parameters are optimized using the energy consumption optimization model and the system's preset optimization algorithm.

5. The energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls as described in claim 4, characterized in that, The energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls also includes: installing data acquisition modules in the spindle system, feed system, auxiliary system, laser cladding laser system, water cooling system, laser cladding special machine tool system, powder feeding and protective gas system, auxiliary system and hoisting device of the turning machine tool.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the energy consumption prediction and parameter adaptive adjustment method for the laser remanufacturing process of waste rolls as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the steps of the method for predicting energy consumption and adaptively adjusting parameters in the laser remanufacturing process of waste rolls as described in any one of claims 1 to 3.

8. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the energy consumption prediction and parameter adaptive adjustment system for the laser remanufacturing process of waste rolls as described in any one of claims 4 to 5.

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