Variable cutting depth layering method for rough machining of channel parts

Abstract

The invention provides a variable cutting depth layering method for rough machining of channel parts. The method comprises the following steps: firstly, establishing a layered milling optimization model taking channel layered rough machining efficiency as an optimization target; then, the bending strength of the tool, the allowable cutting force under the constraint of the deformation amount and main machining parameters serve as constraint conditions of the optimization model; and finally, solving the optimization model by adopting a genetic algorithm under constraint to obtain a group of variable layering depth and feeding speed combinations which enable the total machining time to be shortest, and the variable layering depth and feeding speed combinations are used for layering milling rough machining of channel parts.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of numerical control milling manufacturing, and particularly relates to a variable-depth layering method for rough machining of a channel type part. BACKGROUND

[0002] Channel type parts such as blisks and impellers are widely used in turbomachinery such as aero-engines and gas turbines, and are usually obtained by numerical control milling. The channel type parts have complex structures and large material removal, and rough machining accounts for a large proportion of the total working hours. Therefore, ensuring safe and efficient rough machining can greatly improve the overall productivity of the channel type parts.

[0003] When a ball end mill is used to rough machine a channel type part, the flow passage is usually divided into multiple layers with equal axial depth for layer-by-layer milling. In this process, in order to ensure the accessibility of the tool for rough machining, the overhang length of the tool needs to be increased synchronously with the layer depth, and the stiffness of the tool is also weakened. However, the cutting force of each layer does not change with the stiffness of the tool and is not matched with the stiffness of the tool. Therefore, in actual machining, the tool is prone to large deformation or even failure. Therefore, only conservative cutting parameters can be used, which limits the improvement of the rough machining efficiency. At present, only the document "Li Zhaoyu, Hu Pengcheng, Xie Fubao, et al. A variable-depth multi-layer five-axis trochoidal milling method for machining deep freeform 3D slots [J]. Robotics and Computer-Integrated Manufacturing, 2021, 68: 102093." optimizes the layering of multi-layer trochoidal milling rough machining. In the document, the tool stress and tool deformation are used as constraints, and an optimization model of machining efficiency is established. The optimal multi-layer layer depth distribution and the step distance combination of the trochoidal tool path are obtained by optimization, and layering machining is performed. However, this method does not optimize the feed speed and other parameters, and therefore does not fully improve the machining efficiency. Moreover, this method is only suitable for parameter optimization when a trochoidal tool path is used for milling, and is not suitable for other tool paths. SUMMARY

[0004] The purpose of the present application is to provide a variable-depth layering method for rough machining of a channel type part to overcome the shortcomings of the prior art. To solve the above problems, first, a layering milling optimization model is established with the rough machining efficiency of the channel layering as the optimization objective. Then, a constraint condition considering the change rule of the mechanical properties of the tool material is constructed. Finally, the genetic algorithm is used to solve the optimization model under the constraint to obtain a set of layering depth and feed speed combinations that minimize the total machining time for layering milling rough machining of the channel type part.

[0005] The technical scheme of the present application is:

[0006] 1. A variable depth of cut layering method for rough machining of a channel type part, comprising the following steps:

[0007] Step 1: Establish a variable depth of cut layering milling optimization model: taking the channel layering rough machining efficiency as the optimization objective, a variable depth of cut layering milling optimization model composed of the total cutting time of each layer and the tool changing time is established;

[0008] Step 2: Analyze the constraint conditions: considering the change rule of the mechanical properties of the tool material, the bending strength and deformation of the tool and the main machining parameters are taken as the constraint conditions of the optimization model;

[0009] Step 3: Solve the optimization model: under the constraint conditions, the genetic algorithm is used to solve the optimization model, and a set of layering depths and feed speed combinations that make the total machining time of the channel layering shortest are obtained.

[0010] 2. The variable depth of cut layering method for rough machining of a channel type part according to claim 1, wherein the specific implementation method of step 1 is:

[0011] 1.1 Layering rough machining of a channel, the total machining time is composed of the total cutting time T Machine , the tool changing time T Change and the tool feeding and retracting time T Transition , that is,

[0012] T = T Machine + T change + T Transition

[0013] Among them, the tool feeding and retracting time is related to the geometric characteristics of the channel, and the optimization algorithm cannot usually optimize it, which is not considered here. The single tool changing time t c is fixed, and T Change can be calculated by simply summing. Therefore, the main optimization object is the total cutting time of each tool.

[0014] 1.2 The cutting time T Machine,i of the i-th tool is equal to the ratio of the tool cutting volume V M,i to the material removal rate MRR i . If the layering depth is set to be equal to the axial cutting depth of the tool, the actual cutting volume of each tool is a positive correlation function of the axial cutting depth, and if the axial cutting depth of the i-th tool is represented as a p，i , then: V M,i = V M,i (a p，i ). At this time, when the channel is divided into n layers and machined by n tools with different overhang lengths, then

[0015]

[0016] 1.3 The cutting force F of the i-th tool c,i The material removal rate MRR i There is a relationship as follows:

[0017] F c,i = K M MRR i / πd i n i

[0018] In the formula, K M is a cutting coefficient related to cutting conditions and materials, etc.; d i , n i are the diameter of the i-th tool and the spindle speed during machining, respectively.

[0019] In order to simplify the mathematical model, the tool diameter and the spindle speed are set as constant values, so

[0020] F c，i = K c MRR i

[0021] In the formula, K c is a correlation coefficient of the cutting force and MRR.

[0022] If a constant radial depth of cut and spindle speed are used, according to the cutting force exponential model, for the i-th tool, its cutting force F c,i is a positive function of the axial depth of cut a p,i and the feed speed v f,i , then:

[0023] F c,i = F c，i (a p,i , v f,i )

[0024] 1.4 The target function T can be represented by combining the above formulas

[0025]

[0026] 3. The variable depth of cut layering method for rough machining of a channel type part according to claim 1, wherein the specific implementation method of step 2 is:

[0027] 2.1 In the machining process, the machine tool spindle and non-thin-walled parts can be regarded as rigid bodies, so the flat-bottomed end mill with weak rigidity in the process system can be simplified as a cantilever beam model. The cutting force Fci generated by the ith tool will produce a bending deformation force on the tool. In order to ensure safe and efficient rough machining, the bending and deformation should be kept within a controllable range, so the bending strength and deformation of the tool are taken as constraints. Assuming that the cutting force Fci is applied to the tool tip in the vertical direction of the tool axis, the allowable cutting force Fci of the ith tool under the constraints of the bending strength and the maximum allowable deformation of the material is defined as c，i The total vertical tool axis direction acts on the tool tip. For the ith tool, the allowable cutting force Fci of the tool under the constraints of the bending strength and the maximum allowable deformation of the material is defined as A_S，i And F A_D,i , which are respectively expressed as

[0028]

[0029] In the formula, d 0,i is the equivalent tool diameter (mm) of the ith tool, generally 0.70-0.85 times the tool diameter; σ max is the maximum bending strength of the tool material (MPa); C Strength and C stiffness are the safety factors of the bending strength and the allowable deformation, respectively, which are greater than 1; E is the elastic modulus of the tool material (MPa); δ max is the maximum allowable deformation of the tool (mm) in rough machining; and l i is the overhanging length of the ith tool, which is set as the sum of the tool handle-workpiece safety distance l0 and m (m≤n) layers of axial cutting depth, i.e. Where l0 is set as a positive constant value.

[0030] The allowable cutting force Fci of the ith tool is double-constrained by the bending strength and the maximum allowable deformation of the material, i.e. AIlow,i F Allow,i = min{F A_S,i , F A_D，i}

[0031] 2.2 At the same time, the maximum allowable cutting depth a p_max of the tool should be less than the channel depth H, the sum of the axial cutting depth of each layer of tool is equal to the channel depth H; and in order to be more in line with the change rule of the mechanical properties of the tool material, the axial cutting depth a p，i and the feed speed v f,i of the tool should decrease with the increase of the overhanging length of the tool.

[0032] 2.3 In summary, the constraint conditions can be expressed as follows

[0033] F c，i ≤ F allow,i

[0034] 0 < a p，i+1 < a p，i < a p_max 0 < v f，i+1 < v f,i < v f_max

[0035] 4. The variable depth of cut layering method for rough machining of a channel part according to claim 1, wherein the step 3 is implemented as follows:

[0036] 3.1 The optimization model aims to divide the channel into n layers with variable depths, each layer matching a tool capable of machining the layer without interference, and under the constraints, a set of layer depths and feed speed combinations is solved to minimize the total machining time of the flow passage, in which the diameter of the tool, the spindle speed, the radial depth of cut and other parameters are constant values. When the parameters other than l i , a p,i and v f,i are fixed values, the model is essentially a global optimization problem with the layering depth a p,i and the feed speed v f,i as optimization variables.

[0037] 3.2 Genetic algorithm can effectively solve the global optimization problem. The combination of layering depth and feed speed is taken as an individual in the algorithm, where the Kth individual Layer K is composed of n a p,i and v f,i that satisfy the constraints, i.e. Layer K = [a p,1 , v f,1 , a p，2 , v f,2 ,..., a p，n , v f，n ]. Then the problem of minimizing the time is converted into a problem of maximizing the fitness, and the fitness function is established: Finally, the genetic algorithm is used to solve the optimization model, and a set of layering depth and feed speed combinations that minimize the total machining time of the channel is obtained, which is used for layering milling of the flow passage of the blade disc.

[0038] The beneficial effects of the present application are as follows:

[0039] The present application takes the layering rough machining efficiency of the channel as the optimization target, and obtains the optimal layering depth and feed speed combination by optimizing the layering depth and feed speed combination of each layer of the channel, which can fully exert the cutting capacity of the tool and significantly improve the layering rough machining efficiency of the channel part. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1The algorithm flow chart of the present application;

[0041] Figure 2 The blade disc model used for testing;

[0042] Figure 3 The layered results of the blade disc flow passage;

[0043] Figure 4 The simulation layered rough machining of the blade disc flow passage. DETAILED DESCRIPTION

[0044] A variable depth-of-cut layering method for rough machining of a channel type part, the flow chart is shown in Figure 1 , and the specific implementation steps are described as follows:

[0045] Referring to Figure 2 , the blisk is a typical channel type part, and the effectiveness of the method is verified by using it. The hub diameter of the blisk is 152 mm, the flow passage top surface is open and the depth is 50.5 mm. In order to leave enough semi-finish and finish machining allowance for the flow passage bottom surface, and to avoid overcutting caused by the interference between the flat bottom cutter bottom and the flow passage, 2.5 mm allowance is reserved on the flow passage bottom surface, so that the actual rough machining depth of the flow passage is about 48 mm. It is intended to divide it into three layers for machining based on the layering algorithm.

[0046] First, the variable depth-of-cut layering milling optimization model

[0047]

[0048] Then, the allowable cutting force F A_S,i of the i-th cutter under the constraints of the material bending strength and the maximum allowable deformation is defined A_D，i , which are respectively expressed as

[0049]

[0050] The allowable cutting force F Allow,i of the i-th cutter is subject to the dual constraints of the material bending strength and the maximum allowable deformation, that is,

[0051] F Allow,i =min{F A_S，i , F A_D，i}

[0052] And the main parameters in the formula are set, the cutter is a flat bottom end mill with a diameter of 12 mm, the elastic modulus of the cutter material is about 206 GPa, the maximum bending strength σ max is about 3000 MPa; the equivalent cutter diameter is 0.7 times the cutter diameter; the cutter-shank-to-workpiece safety distance l0is 12.5 mm; the maximum axial depth of cut a p_max35mm (blade length). The material of the test part is 6061 aluminum alloy which is commonly used in aviation industry.

[0053] Meanwhile, the axial depth of cut a p，i is added as a constraint condition. f,i

[0054] Then, the constraint condition is expressed as follows by combining the part parameters

[0055] F c,i ≤F allow,i

[0056] 0<a p,i+1 <a p,i <35 0<v f，i+1 <v f，i <800

[0057] Finally, the established optimization model is solved by genetic algorithm. In which, the tool is always kept cutting, and the default layer depth is equal to the axial depth of cut, and the average radial depth of cut is 1.5mm. The Kth individual Layer K in the algorithm is defined as: Layer K = [a p,1 , v f,1 , a p，2 , v f,2 , a p,3 , v f，3 ] and the fitness function is established: The solving steps of the algorithm are as follows:

[0058] Step1: generate an initial population N0= {Layer 1 , Layer 2 , …, Layer 50} consisting of 50 individuals;

[0059] Step2: calculate the fitness of each individual based on the fitness function;

[0060] Step3: select individuals from the population N0 by roulette wheel method;

[0061] Step4: perform crossover and mutation operations;

[0062] Step5: repeat Step2-Step4 to generate new individuals and calculate the corresponding fitness, keep elite individuals, eliminate low fitness individuals to update the population;

[0063] Step6: repeat Step2-Step5, stop iteration when the iteration number is greater than the set number of generations;

[0064] ​Step7: Select the individual with the highest fitness from the last generation as the best combination of depth of layer and feed rate, and the algorithm ends.

[0065] The best combination of depth of layer and feed rate is calculated by the algorithm, and it is compared with the traditional method as shown in Table 1. The results of depth of layer are shown in Figure 3 It can be seen that as the number of layers increases, the overhang length of the tool increases, which in turn reduces the depth of layer and feed rate

[0066] Table 1 Comparison of layering methods

[0067]

[0068]

[0069] To verify the advantages of the proposed optimization layering method, the theoretical machining efficiency is compared with the fixed layering method while keeping other machining parameters and the number of layers unchanged as shown in Table 2. It can be seen that the proposed method significantly improves the theoretical machining efficiency.

[0070] Table 2 Comparison of theoretical machining efficiency

[0071]

[0072] Since cycloid milling is a common machining strategy for channel type parts, in order to further verify the effectiveness of the layering method, the same cycloid tool path is used in each layer divided by the two methods, and the two flow channels of the blade disc are simulated and compared in VERICUT. The efficiency is compared as shown in Table 3. It can be seen that when the proposed method is applied to cycloid milling of flow channels, the machining efficiency is significantly improved.

[0073] Table 3 Comparison of efficiency of simulation machining

[0074]

[0075] In addition, for other channel type parts using other tool paths for layering machining, this method is also applicable.

Claims

1. A variable cutting depth layering method for rough machining of channel parts, characterized in that: The following steps are involved: Step 1: Establish a variable depth of cut layered milling optimization model: Taking the channel layered roughing efficiency as the optimization target, establish a variable depth of cut layered milling optimization model consisting of the total cutting time and tool change time of each layer; Step 2: Analyze the constraints: Consider the changing laws of the mechanical properties of the tool material, and use the tool's bending strength and deformation as well as the main processing parameters as the constraints of the optimization model; Step 3: Solve the optimization model: Under the constraints, use the genetic algorithm to solve the optimization model and obtain a set of layer depth and feed speed combinations that minimize the total processing time of channel layering.

2. A variable cutting depth layering method for rough machining of channel parts according to claim 1, characterized in that: The specific implementation method of step 1 is: 1.1 Layered roughing channel, the total machining time is determined by the total cutting time T of each tool Machine , tool change time T Change and feed and retract time T Transition Composition, that is T=T Machine +T Change +T Transition Among them, the tool feed and retraction time is related to the geometric characteristics of the channel, and the optimization algorithm usually cannot optimize it, so it is not considered here. c Fixed, T can be calculated by simply summing Change Therefore, the main optimization object is the total cutting time of each tool. 1.2 Cutting time T for tool i Machine,i , which is equal to the tool cutting volume V M,i and material removal rate MRR i If the layer depth is set equal to the axial cutting depth of the tool, the actual cutting volume of each tool is a positive correlation function of the axial cutting depth. If the axial cutting depth of the i-th tool is expressed as a p，i , then: V M，i V M，i (a p，i ). At this time, when the channel is divided into n layers and processed with n tools of different overhang lengths, 1.3 Cutting force F of tool i c，i Its material removal rate MRR i The following relationship exists: F c，i =K M MRR i / πd i n i Where K M It is the cutting coefficient related to cutting conditions and materials; d i 、n i are the diameter of the i-th tool and the spindle speed during processing respectively. In order to simplify the mathematical model, the tool diameter and spindle speed are set to constant values, so F c，i =K c MRR i Where K c is the correlation coefficient between cutting force and MRR. If a constant radial cutting depth and spindle speed are used, according to the cutting force index model, for the i-th tool, its cutting force F c，i is the axial depth of cut a p，i and feed speed v f，i The positive correlation function is: F c,i =F c，i (a p，i ,v f，i ) 1.4 Combining the above equations, the objective function T can be expressed as 3. The variable cutting depth layering method for rough machining of channel parts according to claim 1 is characterized in that: The specific implementation method of step 2 is: 2.1 In machining, the machine tool spindle and non-thin-walled parts can usually be regarded as rigid bodies. In the process system, the flat-bottomed end mill with weaker rigidity can be simplified as a cantilever beam model. The cutting force F generated by the i-th tool c，i The tool will bend and deform due to the force. To ensure safe and efficient roughing, the bending and deformation should be kept within a controllable range. Therefore, the bending strength and deformation of the tool are used as constraints. Assume that the cutting force F c,i All vertical tool axis directions act on the tool tip. For the i-th tool, define the allowable cutting force F of the tool under the constraints of the material bending strength and maximum allowable deformation. A_S,i and F A_D，i , and express them as Where, d 0,i is the equivalent tool diameter of the i-th tool (mm), generally 0.70 to 0.85 times the tool diameter; σ max is the maximum bending strength of the tool material (MPa); C Strength with C stiffness are the safety factors of bending strength and allowable deformation greater than 1 respectively; E is the elastic modulus of the tool material (MPa); δ max is the maximum allowable deformation of the tool during rough machining (mm); l i is the overhang length of the i-th tool. In order to meet the accessibility of machining, it is set to the sum of the tool holder-workpiece safety distance l0 and the axial cutting depth of the m (m≤n) layer, that is, Here, l0 is set to a positive constant value. The allowable cutting force F of the i-th tool is Allow,i It is subject to the dual constraints of material bending strength and maximum allowable deformation, namely F Allow,i =min{F A_S,i ,F A_D，i } 2.2 At the same time, the maximum cutting depth a allowed by the tool p_max It should be less than the channel depth H. The sum of the axial cutting depths of each layer of tool is equal to the channel depth H. In order to better conform to the change law of the mechanical properties of the tool material, the axial cutting depth of the tool a p,i With feed speed v f，i Both should decrease as the tool overhang increases. 2.3 In summary, the constraints can be expressed as follows 4. The variable cutting depth layering method for rough machining of channel parts according to claim 1 is characterized in that: The specific implementation method of step 3 is: 3.1 The optimization model aims to divide the channel into n layers of variable depth, and match each layer with a tool that can process the layer without interference. Under the constraints, a set of layer depth and feed rate combinations that minimize the total processing time of the channel is solved. In this process, the tool diameter, spindle speed, radial cutting depth and other parameters are constant. i 、a p，i and v f,i When all other parameters except for the above are fixed, the model is essentially based on the depth of each layer a. p，i and its feed speed v f,i It is a global optimization problem with optimization variables. 3.2 Genetic algorithm can effectively solve the global optimization problem. The optimization variable layer depth and its feed rate combination are taken as individuals in the algorithm, where the Kth individual Layer K There are n a that satisfy the constraints p,i With v f，i Composition, namely: Layer K =[a p，1 , v f,1 , a p，2 , v f,2 ,...,a p，n ,v f，n ]. Then the time minimization problem is transformed into a fitness maximization problem, and the fitness function is established: Finally, a genetic algorithm is used to solve the optimization model and a set of layer depth and feed rate combinations that minimize the total channel processing time are obtained for layered milling of the blade disk flow channel.

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