Self-adaptive layering method and system for controlling machining deformation of thin-wall blade

Abstract

The invention belongs to the related technical field of machining and manufacturing, and discloses a self-adaptive layering method and system for controlling machining deformation of a thin-wall blade, and the method comprises the steps: (1) according to a blade CAD model, a given layering machining allowance and the layering length of each layer, reversely deducing blade CAD models at different machining stages, a function of the maximum machining deformation of each layer and the length of each layer in the layered machining process is constructed; (2) on the basis of the set number of layers and the function corresponding to each layer, constructing a self-adaptive layered length optimization model by taking minimization of the maximum deformation of the whole as a target; and (3) repeatedly adopting the self-adaptive layered length optimization model to obtain an optimal length distribution strategy of each layer and a corresponding minimum overall maximum machining deformation under different layered numbers, and taking the obtained minimum overall maximum machining deformation smaller than a machining deformation threshold as a constraint. And obtaining the required minimum number of layers and the corresponding length distribution strategy of each layer. The machining quality and efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to processing and manufacturing, and more specifically, relates to an adaptive layering method and system for controlling deformation of thin-walled blades during processing. Background Art

[0002] During the machining of thin-walled blades, the blades' weak rigidity leads to deformation and vibration induced by cutting forces, which are unavoidable and detrimental factors affecting machining accuracy. The layered machining method utilizes the material to be removed as support, increasing rigidity during machining and effectively controlling deformation. This method divides the blade to be machined into several layers radially (from tip to root), performing semi-finishing and finishing on each layer, and then repeating these operations on the next layer. This approach offers superior application value in achieving high-precision machining of thin-walled blades.

[0003] Traditional layered machining methods require engineers to specify the layering strategy and the required number of layers based on experience or multiple trial cutting experiments. To avoid machining deformation errors exceeding the allowable value, more conservative machining parameters are usually selected. Traditional machining layering methods generally adopt a uniform layering strategy, which does not take into account the deformation distribution of the blade. Therefore, more layers are required to meet the same machining deformation threshold, resulting in a larger number of tool marks and longer machining time, affecting surface machining quality and machining efficiency. Therefore, it is necessary to seek an adaptive layering method to control the machining deformation of thin-walled blades in order to obtain and optimize the process parameters of layered machining, thereby achieving efficient and high-precision machining of blades. Summary of the Invention

[0004] In response to the above defects or improvement needs of the prior art, the present invention provides an adaptive layering method and system for controlling the deformation of thin-walled blade processing, which aims to solve the problems of low processing quality and processing efficiency of existing thin-walled blades.

[0005] To achieve the above object, according to one aspect of the present invention, an adaptive layering method for controlling deformation during machining of thin-walled blades is provided, the method comprising the following steps:

[0006] (1) Based on the blade CAD model, the given layered machining allowance and the layered length of each layer, the blade CAD model at different machining stages is reversed, and then the function of the maximum machining deformation of each layer and the length of each layer during the layered machining process is constructed;

[0007] (2) Based on the set number of layers and the functions corresponding to each layer, an adaptive layer length optimization model is constructed with the goal of minimizing the overall maximum deformation;

[0008] (3) With the goal of minimizing the number of layers, the adaptive layer length optimization model is repeatedly used to obtain the optimal layer length allocation strategy and the corresponding minimum overall maximum processing deformation under different layer numbers, and the minimum overall maximum processing deformation obtained is constrained to be less than the processing deformation threshold, thereby obtaining the required minimum number of layers and its corresponding layer length allocation strategy.

[0009] Furthermore, the vector composed of the lengths of each layer is l=[l1,l2,…,l n ], and obtain the function f of the maximum machining deformation of each layer as the length of each layer changes i (l),i=1,2,…,n.

[0010] Furthermore, the maximum deformation of the entire blade is the maximum processing deformation f i (l), i=1,2,…,n takes the maximum value.

[0011] Furthermore, the expression of the adaptive layer length optimization model is:

[0012]

[0013] Where L is the total length of the blade, h(n) is a function of the number of layers n; f i (l) represents the maximum machining deformation of the i-th layer obtained by analytical method or finite element method with the length of each layer l = [l i ],i=1,2,…,n.

[0014] Furthermore, the minimum number of layers n required is obtained by optimizing the maximum machining deformation h(n) of the blade as a whole within the allowable deformation threshold. * , where it is assumed that Δn is increased at the current position so that n becomes n+Δn, where the expression of step length Δn is:

[0015]

[0016] Where, represents rounding up, h(n) is a decreasing function of n, h'(n) is a negative number, and e2 is the machining deformation threshold.

[0017] Furthermore, when h(n)>e2, Δn>0, the number of layers is too small, and it is necessary to increase the number of layers to meet the machining accuracy requirements; when h(n)<e2, Δn≤0, the number of layers is too large, and the number of layers should be reduced accordingly to reduce the number of tool marks; after continuous iteration, the optimal number of layers n is finally obtained. * And its corresponding optimal layer length allocation strategy l * .

[0018] The present invention also provides an adaptive layering system for controlling deformation of thin-walled blade processing. The system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it executes the adaptive layering method for controlling deformation of thin-walled blade processing as described above.

[0019] The present invention also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the adaptive layering method for controlling deformation of thin-walled blade processing as described above.

[0020] In general, compared with the prior art, the above technical solutions conceived by the present invention provide an adaptive layering method and system for controlling deformation during machining of thin-walled blades, which has the following beneficial effects:

[0021] 1. This method substitutes the maximum deformation function of each layer obtained by simulation and analytical methods, and takes minimizing the maximum deformation of the entire blade as the goal to obtain the optimal layer length and the corresponding overall maximum processing deformation under a given number of layers. Compared with the traditional layering method, this method greatly reduces the maximum processing deformation of the entire blade; at the same time, based on the above-mentioned layer length optimization method, with the maximum processing deformation within the threshold as the constraint, the number of processing layers is minimized, the number of tool marks and the number of tool lifts required for switching between different layers are reduced, thereby improving the surface processing quality and processing efficiency.

[0022] 2. Based on the adaptive layer length optimization, the present invention proposes an adaptive layer number optimization method which searches for the minimum number of layers corresponding to the allowable processing deformation threshold by repeatedly calling the adaptive layer length optimization method, thereby reducing the number of tool lifting switches and the number of tool marks between different layers, and improving the processing surface accuracy and efficiency.

[0023] 3. The present invention obtains the mapping function between the maximum processing deformation of each layer and the layer length by adopting theoretical derivation, finite element simulation and other methods. Combined with the adaptive process parameter optimization algorithm, it solves the difficult problem that the number of layers and the length parameters of each layer in the actual production process can only be selected based on engineering experience or repeated trial production. It ensures the feasibility of the processing parameters, reduces the number of required trial productions, and improves the success rate of the processing.

[0024] 4. The adaptive layer length and layer number optimization method proposed in this invention directly determines key process parameters such as the minimum number of layers and the optimal layer length for each layer during processing. This ensures the optimality of the selected process parameters while ensuring their feasibility. This provides an important theoretical basis and reference for selecting key process parameters such as the number of layers and the length of each layer, thereby guiding subsequent CNC machining programming. Furthermore, a smaller number of layers also reduces the complexity of CNC programming, which is of great significance for improving the efficiency of the entire production cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of an adaptive layering method for controlling deformation of thin-walled blades provided by the present invention;

[0026] Figure 2 (a) and (b) are schematic diagrams of the optimization parameters and involved in the adaptive layering algorithm proposed in the present invention;

[0027] Figure 3 This is a flow chart of the adaptive layer number and layer length optimization algorithm proposed in the present invention;

[0028] Figure 4 (a) and (b) are the layer lengths and maximum deformations of each layer corresponding to the uniform layering and the optimized adaptive layer length optimization proposed in the present invention, respectively; (c) is the layer length and maximum deformation of each layer obtained by the traditional uniform layering method under the condition of satisfying the processing deformation threshold. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0030] The present invention provides an adaptive layering method for controlling deformation during thin-walled blade machining. This method provides a theoretical basis for selecting process parameters and strategies during thin-walled blade layering. While meeting a threshold for machining deformation, it reduces the number of required layers, thereby reducing the number of inter-layer tool marks and tool lift switching times during layered machining, thereby improving the efficiency and surface quality of the blade layering process. Furthermore, the present invention can optimize the process parameters for thin-walled blade layering, significantly reducing layering, the number of tool marks, and machining time while meeting a machining error threshold, thereby improving the machining quality and efficiency of thin-walled blades.

[0031] See also Figure 1 , the adaptive layering method mainly includes the following steps:

[0032] S1, based on the blade CAD model, the given layered machining allowance and the layered length of each layer, the blade CAD model at different machining stages is reversed, and then the function of the maximum machining deformation of each layer and the length of each layer during the layered machining process is constructed.

[0033] According to the CAD model of the blade after processing and the processing allowance, combined with the layer length l of each layer i , i=1,2,…,n, reversely deduce the blade CAD model corresponding to different stages in the layered processing process. The vector composed of the length of each layer is l=[l1,l2,…,l n ]. Then, combined with the blade CAD model corresponding to different processing stages in the layered processing process, analytical methods, finite element methods or machine learning methods are used to obtain the function f of the maximum processing deformation of each layer as the length of each layer changes i (l),i=1,2,…,n.

[0034] S2, based on the set number of layers and the corresponding functions of each layer, an adaptive layer length optimization model is constructed with the goal of minimizing the overall maximum deformation.

[0035] Given the number of layers n, use the function f of the maximum machining deformation of each layer obtained in the previous step as a function of the length of each layer i (l), i = 1, 2, ..., n, the goal is to minimize the maximum machining deformation of the whole blade, where the maximum deformation of the whole blade is the maximum machining deformation of each layer f i (l), i=1,2,…,n, take the maximum value, take the sum of the lengths of each layer as the total length (fixed value) as the constraint, take the length of each layer l=[l i ], i=1,2,…,n are the optimization variables, and the adaptive layer length optimization model is as follows:

[0036]

[0037] Where L is the total length of the blade, and h(n) is a function of the number of layers n. i (l) represents the maximum machining deformation of the i-th layer obtained by analytical method or finite element method with the length of each layer l = [l i ], i=1,2,…,n. Substitute and solve the above problem to obtain the optimal layer length allocation strategy l when the number of layers is n * =[l1 * ,l2 * ,…,l n * ] and its corresponding maximum deformation h(n) on the optimized overall blade.

[0038] S3, with the goal of minimizing the number of layers, repeatedly uses the adaptive layer length optimization model to obtain the optimal layer length distribution strategy and the corresponding minimum overall maximum processing deformation under different layer numbers, and takes the obtained minimum overall maximum processing deformation as a constraint that is less than the processing deformation threshold, thereby obtaining the required minimum number of layers and its corresponding layer length distribution strategy.

[0039] In order to reduce the number of tool marks, improve machining efficiency and reduce the number of tool lifts and switches between different layers during delamination, the minimum number of layers n required is obtained by optimizing the maximum machining deformation h(n) of the blade as a whole after optimization obtained in the previous step within the allowable deformation threshold. * , where it is assumed that Δn is increased at the current position so that n becomes n+Δn, where the expression of step length Δn is:

[0040]

[0041] Where, Indicates rounding up. h(n) is a decreasing function of n, h'(n) is a negative number, and e2 is the processing deformation threshold. When h(n)>e2, Δn>0, the number of layers is too small, and it is necessary to increase the number of layers to meet the processing accuracy requirements; when h(n)<e2, Δn≤0, the number of layers is too large, and the number of layers can be reduced to reduce the number of tool marks. After continuous iteration, the optimal number of layers n is finally obtained. * And its corresponding optimal layer length allocation strategy l * .

[0042] In the adaptive layering method, on the one hand, the adaptive layer length optimization method, that is, substituting the maximum deformation function of each layer obtained by simulation and analytical methods, and taking minimizing the maximum deformation of the entire blade as the goal, obtains the optimal layer length and the corresponding overall maximum processing deformation under a given number of layers. Compared with the traditional layering method, this method greatly reduces the maximum processing deformation of the entire blade; on the other hand, the adaptive layer number optimization method, that is, on the basis of the above-mentioned layer length optimization method, takes the maximum processing deformation within the threshold as the constraint, minimizes the number of processing layers, reduces the number of tool marks and the number of tool lifts required for switching between different layers, thereby improving the surface processing quality and efficiency.

[0043] The present invention also provides an adaptive layering system for controlling deformation of thin-walled blade processing. The system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it executes the adaptive layering method for controlling deformation of thin-walled blade processing as described above.

[0044] The present invention also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the adaptive layering method for controlling deformation of thin-walled blade processing as described above.

[0045] The present invention is further described in detail below with reference to specific embodiments.

[0046] See also Figure 2 、 Figure 3 and Figure 4 The adaptive layering method for controlling deformation of thin-walled blades provided in an embodiment of the present invention realizes efficient and high-precision machining of thin-walled blades, and mainly includes the following steps:

[0047] Step 1, such as Figure 2 As shown in (a), according to the blade CAD model and machining allowance, it includes machining allowance a2 and finishing allowance a1, and combined with the given layer length l of each layer i , i=1,2,…,n, reverse the blade CAD model at different stages in the layered processing process. The vector composed of the length of each layer is l=[l1,l2,…,l n ].like Figure 2 As shown in (b), combined with the blade CAD models corresponding to different processing stages, analytical methods, finite element methods or machine learning methods are used to obtain the function f of the maximum processing deformation of each layer as a function of the length of each layer. i (l), i = 1, 2, ..., n. Considering that the blade is a thin-walled cantilever structure, the stiffness of the top is weaker than that of the bottom, so the maximum deformation of each layer generally occurs at the top of each layer.

[0048] Step 2: Based on the blade model and machining allowance, and given the number of layers, an optimization model is constructed to minimize the maximum machining deformation of each layer, and the optimal layering strategy under the current number of layers is obtained, that is, the optimal layer length l = [l1, l2, ..., l n ].

[0049] Specifically, combined Figure 3 , substitute the obtained maximum processing deformation function of each layer, given the number of layers n, and aim to minimize the overall maximum processing deformation, where the overall maximum deformation is the maximum processing deformation of each layer f i (l), i=1,2,…,n, take the maximum value, and take the length of each layer l i , i=1,2,…,n is the total length (fixed value) as a constraint, and the optimization model is constructed. The expression is:

[0050]

[0051] Where L is the total length of the blade, which is 100%, and h(n) is a function of the number of layers n, representing the minimum overall maximum machining deformation corresponding to a given number of layers n. The original problem is a minmax problem, and an additional variable is introduced represents the maximum deformation of the whole and increases by ξ-f i ≥0,i=1,2,…,n, and then transform the original problem into the problem of minimizing the overall maximum deformation ξ.

[0052] Since there are nonlinear terms in the constraints and objectives, the embodiment of the present invention adopts the sequential linear approximate programming method to transform the above problem into a series of feasible solutions (ξ k ,l k ) of the optimal perturbation (Δξ, Δl), and obtain the feasible solution (ξ k+1 ,l k+1 )=(ξ k ,l k )+(Δξ,Δl). When the feasible solution satisfies |ξ k+1 / ξ k -1|≤1e-4, the current feasible solution is the optimal solution, that is, l * =l k+1 , Otherwise, continue the above cycle until the above conditions are met. Finally, the optimal layer length l under the current given layer number n is obtained. * and the corresponding minimum overall maximum machining deformation h(n).

[0053] When the given number of layers is 4, such as Figure 4 (a) in the figure adopts the uniform layering method, and the corresponding maximum overall deformation is 0.10936 mm. The optimal layer length l is obtained by adaptive layering optimization. * and the maximum deformation of each layer as Figure 4 As shown in (b), the optimal layer length l * The length distribution is l1<l2<…<l n , the corresponding overall maximum deformation is 0.08032mm. Compared with the existing uniform layering method, the maximum deformation f of each layer obtained in the embodiment of the present invention is i The distribution of is more uniform, and the corresponding maximum deformation of the whole is smaller. Figure 2In (b), due to the weak stiffness at the blade tip, large deformation will occur in the first few layers of processing, so a smaller layer length is required. When processing the last few layers, the stiffness near the blade root is better and the corresponding deformation is also smaller, so a larger layer length can be selected. In summary, the adaptive layer length optimization algorithm can adaptively distribute the layer lengths of each layer according to the stiffness distribution of the blade. Under the same number of layers, the overall maximum deformation is smaller than that obtained by the uniform layering method.

[0054] Step 3: Given the machining deformation threshold e2, the adaptive layer length optimization method under the given number of layers in step 2 is nested to solve the required minimum number of layers and the corresponding optimal layer length distribution strategy.

[0055] Specifically, the adaptive layer length optimization algorithm of step 2 is nested to obtain the optimal layer length of each layer and the corresponding minimum overall maximum deformation h(n) under any given layer number n. Given the initial layer number n0, the current minimum overall maximum processing deformation h(n0) is obtained. If it is still greater than the deformation threshold e2, that is, h(n0)>e2, it indicates that the current set total number of layers is not large enough n0 and needs to be increased; similarly, if h(n0) is smaller than the deformation threshold e2, that is, h(n0)<e2, it indicates that the total number of layers n0 may be too large and needs to be reduced. The Newton iteration algorithm is used to calculate the optimal number of layers n * , where the iteration step length Δn is calculated as follows:

[0056]

[0057] Where, Indicates rounding up. h(n) is a decreasing function of the number of layers n, that is, h'(n) is a negative number. When h(n)>e2, Δn>0, the number of layers is too small, and it is necessary to increase the number of layers to meet the machining accuracy requirements; when h(n)<e2, Δn≤0, the number of layers is too large, and the number of layers can be reduced to reduce the number of tool marks. Among them, h'(n) can be approximated by finite differences. For example Figure 4As shown in (b), when the given processing deformation threshold e2 is 0.0808mm, the optimal number of layers obtained by the adaptive layered processing parameter optimization method proposed in the embodiment of the present invention is only 4 layers, and the corresponding global maximum deformation is 0.08032mm. Under the same number of layers, the number of layers required to achieve the same processing deformation threshold requirement using the traditional uniform layering method is 15 layers. Compared with the traditional method and the actual production process, the number of layers is generally obtained by combining engineering experience or a large number of trial cutting tests. The adaptive layered processing method proposed in the present invention can not only quickly obtain the available number of layers, reduce the number of trial cuttings and dependence on engineering experience, but also greatly optimize and reduce the number of layers processed, thereby reducing the number of tool marks at the layering and the number of tool lifts required for switching between different layers, improving processing efficiency, precision and surface quality. At the same time, the optimal layer length distribution under this number of processing layers is also obtained to guide subsequent data processing programming.

[0058] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An adaptive layering method for controlling deformation of thin-walled blades, characterized in that: The method comprises the following steps: (1) Based on the blade CAD model, the given layered machining allowance and the layered length of each layer, the blade CAD model at different machining stages is reversed, and then the function of the maximum machining deformation of each layer and the length of each layer during the layered machining process is constructed; (2) Based on the set number of layers and the obtained function, an adaptive layer length optimization model is constructed with the goal of minimizing the overall maximum deformation; (3) With the goal of minimizing the number of layers, the adaptive layer length optimization model is repeatedly used to obtain the optimal layer length allocation strategy and the corresponding minimum overall maximum processing deformation under different layer numbers, and the minimum overall maximum processing deformation obtained is constrained to be less than the processing deformation threshold, thereby obtaining the required minimum number of layers and its corresponding layer length allocation strategy.

2. The adaptive layering method for controlling deformation during machining of thin-walled blades according to claim 1, characterized in that: The vector composed of the lengths of each layer is l=[l1,l2,…,l n ], and obtain the function f of the maximum machining deformation of each layer as the length of each layer changes i (l),i=1,2,…,n.

3. The adaptive layering method for controlling deformation during machining of thin-walled blades according to claim 2, wherein: The maximum deformation of the entire blade is the maximum processing deformation f in each layer. i (l), i=1,2,…,n takes the maximum value.

4. The adaptive layering method for controlling deformation during machining of thin-walled blades according to claim 2, wherein: The expression of the adaptive layer length optimization model is: Where L is the total length of the blade, h(n) is a function of the number of layers n; f i (l) represents the maximum machining deformation of the i-th layer obtained by analytical method or finite element method with the length of each layer l = [l i ],i=1,2,…,n.

5. The adaptive layering method for controlling deformation during machining of thin-walled blades according to claim 1, wherein: The optimization is performed to obtain the minimum number of layers n, with the maximum machining deformation h(n) of the blade as a constraint within the allowable deformation threshold. * , where it is assumed that Δn is increased at the current position so that n becomes n+Δn, where the expression of step length Δn is: Where, represents rounding up, h(n) is a decreasing function of n, h'(n) is a negative number, and e2 is the machining deformation threshold.

6. The adaptive layering method for controlling deformation during machining of thin-walled blades according to claim 5, characterized in that: When h(n)>e2, Δn>0, the number of layers is too small, and it is necessary to increase the number of layers to meet the machining accuracy requirements; when h(n)<e2, Δn≤0, the number of layers is too large, and the number of layers should be reduced accordingly to reduce the number of tool marks; after continuous iteration, the optimal number of layers n is finally obtained. * And its corresponding optimal layer length allocation strategy l * .

7. An adaptive layering system for controlling deformation during machining of thin-walled blades, characterized by: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the adaptive layering method for controlling machining deformation of thin-walled blades according to any one of claims 1 to 6 is executed.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor, the machine-executable instructions prompt the processor to implement the adaptive layering method for controlling machining deformation of thin-walled blades according to any one of claims 1 to 6.

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