Integrated optimization of tool and process parameters and energy saving prediction method in side milling

Abstract

Disclosed is an integrated optimization and energy-saving prediction method for tools and process parameters in side milling, which can meet the demands of modern manufacturing industry for high efficiency, good quality and green production.SOLUTION: Determining a joint optimization variable of a tool parameter and a process parameter in side milling, determining a joint optimization target and establishing a target function model, determining a joint optimization constraint condition, establishing a joint optimization multi-target function model, performing normalization and weighting processing on the multi-target function model, obtaining an optimization solution of the multi-target function model, and predicting an energy saving effect according to the tool parameter and the process parameter obtained by the solution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of numerical control machining, and in particular to a method for integrated optimization of tool parameters and process parameters and energy saving prediction in side milling. [Background technology]

[0002] In modern manufacturing, numerical control (NC) machining technology has become an important means of improving production efficiency and product quality. Side milling, as a common form of NC machining, is widely used in the manufacture of molds, aerospace, automobiles, and other mechanical parts. Its machining quality and efficiency not only depend on the performance of the equipment, but are also closely related to tool parameters and process parameters.

[0003] In traditional side milling, optimization of tool and process parameters generally relies on experience and trial and error, which not only requires time and effort but can also lead to resource waste and reduced production efficiency. In recent years, with the development of computer and artificial intelligence technologies, optimization methods based on numerical simulation and machine learning have gradually been applied to the manufacturing field. These methods provide theoretical guidance and technical support by establishing mathematical models and algorithms to simulate and optimize complex machining processes. However, most current research mainly employs step-by-step optimization methods, some of which focus on tool parameter optimization and others on process parameter optimization, resulting in a lack of integrated optimization methods for tool and process parameters. With the promotion and popularization of green manufacturing concepts, manufacturing companies are increasingly focusing on energy conservation, reducing pollutant emissions, and achieving sustainable development. Under these circumstances, the question of how to scientifically and integratedly optimize tool and process parameters in side milling and apply the optimization results to actual production to achieve effective energy-saving prediction and control remains an urgent issue. Summary of the Invention [Problem to be solved by the invention]

[0004] In response to the problems of the prior art, the present invention proposes a method for integrated optimization of tool parameters and process parameters in side milling and prediction of energy saving. By integratedly optimizing tool parameters and process parameters, energy saving and efficiency improvement in the side milling process can be realized, and the demands for high-efficiency, high-quality, and green production in modern manufacturing industries can be met. [Means for solving the problem]

[0005] A method for integrated optimization and energy saving prediction of tool parameters and process parameters in side milling, comprising the following steps: Step 1: Determine the integrated optimization variables of tool parameters and process parameters in side milling, where the integrated optimization variables of tool parameters include tool diameter d, tool tooth number z, and tool material M, and the integrated optimization variables of process parameters include spindle speed n and feed rate F. v and milling width a e and milling depth a p Includes:

[0006] Step 2: Integrated optimization of tool and process parameters in side milling. Target: Cutting energy consumption per unit volume E cutting , machining surface roughness R a , cutting time per unit volume T cutting Determine.

[0007] Step 3: Determine the integrated optimization constraint conditions of the tool parameters and process parameters in side milling. The integrated optimization constraint conditions of the tool parameters include a tool diameter constraint, a tool tooth number constraint, and a tool material constraint. The integrated optimization constraint conditions of the process parameters include a spindle speed constraint, a feed rate constraint, a milling width constraint, and a milling depth constraint.

[0008] Step 4: Establish an integrated optimization multi-target function model of tool parameters and process parameters in side milling.

[0009] Step 5: Normalization and weighting are performed on the multi-target function model.

[0010] Step 6: Find the optimization solution for the multi-target function model.

[0011] Step 7: Predict the energy-saving effect based on the tool parameters and process parameters obtained by finding the optimization solution in Step 6.

[0012] Preferably, in step 2, the cutting energy consumption per unit volume E cutting The function model for the optimization target of

number

[0013] In the formula, E cutting is the cutting energy consumption per unit volume, expressed in J / mm 3 and P cutting is the milling power in W, MRR is the material removal rate in mm 3 / s.

[0014] Processing surface roughness R a The function model for the optimization target of

number

[0015] In the formula, R a is the roughness of the machined surface in μm, K is the correction coefficient in the formula, and C F is the cutting force coefficient, the value of which depends on the workpiece material, α1 is the exponential term of the spindle speed n, and f Zis the feed rate per tooth, β1 is the exponential term of the feed rate per tooth, and γ1 is the milling width a e is the exponential term of the milling depth a p is the exponential term of the tool diameter d, ζ is the exponential term of the tool tooth number z, is the cutting force coefficient, and η is the cutting force coefficient C F is the exponential term of and is obtained by fitting experimental data.

[0016] Cutting time per unit volume T cutting The function model for the optimization target of

number

[0017] In the formula, T cutting is the cutting time per unit volume, in s / mm 3 is.

[0018] Preferably, the milling power P cutting The function model is a polynomial regression model

number

[0019] In the formula, A is the coefficient term of the spindle speed n, α2 is the exponent term of the spindle speed n, and B is the feed per tooth f Z is the coefficient term of the feed per tooth, β2 is the exponent term of the feed per tooth, C is the coefficient term of the milling width, γ2 is the milling width a e is the exponential term of the milling depth a p is the coefficient term of the milling depth a p where E is the coefficient of the tool diameter d, ε2 is the exponential term of the tool diameter d, F is the coefficient of the number of tool teeth z, ζ2 is the exponential term of the number of tool teeth z, and G is the cutting force coefficient C F η2 is the cutting force coefficient C Fand H is the constant term in the equation. A, B, C, D, E, F, G, α2, β2, γ2, δ2, ε2, ζ2, and η2 are obtained by fitting the experimental data.

[0020] Preferably, the tool diameter constraint is:

number

[0021] In the formula, d1, d2, , d n are optional tool diameters provided by the tool manufacturer.

[0022] Tool tooth number constraints are

number

[0023] In the formula, z1, z2, , z n are the optional tool tooth numbers provided by the tool manufacturer.

[0024] Tool material constraints are

number

[0025] In the formula, C F1 ,C F2 ,···,C Fn are the cutting force coefficients corresponding to the optional tool materials provided by the tool manufacturer.

[0026] The spindle speed constraint is

number

[0027] In the formula, n min , n max are the minimum and maximum spindle speeds recommended by the tool manufacturer, respectively.

[0028] The feed rate constraint is

number

[0029] In the formula, F v_min , F v_man are the minimum and maximum feed rates recommended by the tool manufacturer, respectively.

[0030] The milling width constraint is

number

[0031] In the formula, a e_min , a e_max are the minimum and maximum milling widths recommended by the tool manufacturer, respectively.

[0032] The milling depth constraint is

number

[0033] In the formula, a p_min , a p_max are the minimum and maximum milling depths recommended by the tool manufacturer, respectively.

[0034] Preferably, the integrated optimization multi-target function model of the tool parameters and process parameters in side milling in step 4 is:

number

[0035] Preferably, the normalization method of the multi-target function model is to map the target function value into the interval (0, 1), and the specific normalization process is as follows:

number

[0036] In the formula, f * i represents the normalized function expression, and f i represents the i-th function value, and f i_max and f i_min and represent the maximum and minimum values ​​of the i-th function in the interval, respectively.

[0037] The weighting method for the multi-target function model is to set different weighting coefficients according to the importance of the target functions. The weighted function model is as follows:

number

[0038] In the formula, c1, c2, and c3 are the weight coefficients of the three optimization target functions, respectively.

[0039] Preferably, the method for solving the multi-target function model in step 6 employs a black hole-sequential ant colony algorithm.

[0040] Preferably, step 7 is specifically as follows: the tool parameters and process parameter values ​​(d, z, M, n, F) obtained by finding the optimization solution in step 6 are used to calculate the tool parameters and process parameter values ​​(d, z, M, n, F) v ,a e ,a p ) is the cutting energy consumption per unit volume E cutting Function model, machined surface roughness R a Function model, cutting time per unit volume T cutting By substituting it into a function model and comparing it with empirical processing plans, it is possible to predict the energy-saving effect. [Effects of the Invention]

[0041] The present invention has the following beneficial effects:

[0042] In conventional side milling, optimization research on tool parameters and process parameters mainly uses stepwise optimization methods, and research on integrated optimization of both is lacking. The present invention uses an integrated optimization method that performs global parameter optimization of tool parameters and process parameters as a whole, so that the optimization results have more practical significance. Furthermore, compared with stepwise optimization, the integrated optimization of tool parameters and process parameters can more comprehensively explore the potential optimization space, achieve more favorable machining solutions, further promote energy conservation and pollutant emission reduction, and better meet the requirements of green manufacturing. [Brief explanation of the drawings]

[0043] [Figure 1] Figure 1 is a schematic diagram of side milling. [Figure 2] FIG. 2 is a flow chart of the method of the present invention. [Figure 3] Figure 3 is the flowchart of finding the optimization solution based on the multi-target function model of the black hole-sequential ant colony algorithm. DETAILED DESCRIPTION OF THE INVENTION

[0044] Specific embodiments of the present application will be described in more detail below in conjunction with the drawings and examples. Example 1: This example uses the side milling of a rectangular workpiece on a VMC650L high-speed vertical machining center as an example. The tool parameters and process parameters during machining are integrated and optimized, and energy saving prediction is performed based on the optimization results. The schematic diagram of the side milling process is shown in Figure 1, which includes a tool 1 and a workpiece 2.

[0045] During the machining process, different tool types can be selected according to different combinations of tool material, diameter, number of teeth and other parameters. The specific detailed parameter information is shown in Table 1.

[0046] [Table 1]

[0047] 1 and 2, this embodiment will be introduced in detail, which is a method for integrated optimization of tool parameters and process parameters and energy saving prediction in side milling, including the following steps: Step 1: Determine the integrated optimization variables of tool parameters and process parameters in side milling, where the integrated optimization variables of tool parameters include tool diameter d, tool tooth number z, and tool material M, and the integrated optimization variables of process parameters include spindle speed n and feed rate F. v and milling width a e and milling depth a p Includes:

[0048] Step 2: Integrated optimization of tool and process parameters in side milling. Target: Cutting energy consumption per unit volume E cutting , machining surface roughness R a , cutting time per unit volume T cutting Determine.

[0049] Cutting energy consumption per unit volume E cutting The function model for the optimization target of

number

[0050] In the formula, E cutting is the cutting energy consumption per unit volume, expressed in J / mm 3 and P cutting is the milling power in W, MRR is the material removal rate in mm 3 / s.

[0051] Milling Power P cutting The function model is a polynomial regression model

number

[0052] In the formula, A is the coefficient term of the spindle speed n, α2 is the exponent term of the spindle speed n, and B is the feed per tooth f Z is the coefficient term of the feed per tooth, β2 is the exponent term of the feed per tooth, C is the coefficient term of the milling width, γ2 is the milling width a e is the exponential term of the milling depth a p is the coefficient term of the milling depth a p where E is the coefficient of the tool diameter d, ε2 is the exponential term of the tool diameter d, F is the coefficient of the number of tool teeth z, ζ2 is the exponential term of the number of tool teeth z, and G is the cutting force coefficient C F η2 is the cutting force coefficient C F and H is the constant term in the equation. A, B, C, D, E, F, G, α2, β2, γ2, δ2, ε2, ζ2, and η2 are obtained by fitting the experimental data.

[0053] Processing surface roughness R a The function model for the optimization target of

number

[0054] In the formula, R a is the roughness of the machined surface in μm, K is the correction coefficient in the formula, and C F is the cutting force coefficient, the value of which depends on the workpiece material, α1 is the exponential term of the spindle speed n, and f Z is the feed rate per tooth, β1 is the exponential term of the feed rate per tooth, and γ1 is the milling width a e is the exponential term of the milling depth a p is the exponential term of the tool diameter d, ζ is the exponential term of the tool tooth number z, is the cutting force coefficient, and η is the cutting force coefficient C Fis the exponential term of and is obtained by fitting experimental data.

[0055] Cutting time per unit volume T cutting The function model for the optimization target of

number

[0056] In the formula, T cutting is the cutting time per unit volume, in s / mm 3 is.

[0057] To obtain the coefficients in each target function model, this embodiment uses Taguchi orthogonal table L27(3 7 ) to design the experiment, the experimental data necessary for fitting the coefficients can be obtained, and the experimental results are shown in Table 2.

[0058] Based on the experimental results shown in Table 2, nonlinear polynomial fitting was performed, and the milling power P obtained by fitting was calculated. cutting The function model is

number

[0059] Therefore, the cutting energy consumption per unit volume E cutting The optimization target function model is further

number

[0060] Surface roughness R obtained by fitting a The optimization target function model is

number

[0061] [Table 2] JPEG2026012010000025.jpg243169

[0062] Step 3: Determine the integrated optimization constraint conditions of the tool parameters and process parameters in side milling. The integrated optimization constraint conditions of the tool parameters include a tool diameter constraint, a tool tooth number constraint, and a tool material constraint. The integrated optimization constraint conditions of the process parameters include a spindle speed constraint, a feed rate constraint, a milling width constraint, and a milling depth constraint.

[0063] The tool diameter constraint is

number

[0064] In the formula, d1, d2, , d n are optional tool diameters provided by the tool manufacturer.

[0065] Tool tooth number constraints are

number

[0066] In the formula, z1, z2, , z n are the optional tool tooth numbers provided by the tool manufacturer.

[0067] Tool material constraints are

number

[0068] In the formula, C F1 ,C F2 ,···,C Fn are the cutting force coefficients corresponding to the optional tool materials provided by the tool manufacturer.

[0069] The spindle speed constraint is

number

[0070] In the formula, n min , n max are the minimum and maximum spindle speeds recommended by the tool manufacturer, respectively.

[0071] The feed rate constraint is

number

[0072] In the formula, F v_min , F v_man are the minimum and maximum feed rates recommended by the tool manufacturer, respectively.

[0073] The milling width constraint is

number

[0074] In the formula, a e_min , a e_max are the minimum and maximum milling widths recommended by the tool manufacturer, respectively.

[0075] The milling depth constraint is

number

[0076] In the formula, a p_min , a p_max are the minimum and maximum milling depths recommended by the tool manufacturer, respectively.

[0077] Here, the cutting force coefficient C Fis obtained based on the "Cutting Volume Manual". The cutting force coefficients corresponding to the three types of tool materials, namely, hard alloy, cobalt-containing high-speed steel, and high-speed steel, are 1.07, 1.12, and 1.13, respectively. The milling width a e The value of is generally less than one-tenth of the tool diameter, and the milling depth a p Since the value is generally 1.5 times or less of the tool diameter, the specific constraint condition in this embodiment is

number

[0078] Step 4: Establish an integrated optimization multi-target function model of tool parameters and process parameters in side milling.

[0079] In this embodiment, the integrated optimization multi-target function model of tool parameters and process parameters in side milling is as follows:

number

[0080] Step 5: Normalization and weighting are performed on the multi-target function model.

[0081] Here, the normalization method of the multi-target function model is to map the target function values ​​within the interval (0, 1). In this way, the dimensional influence of different target functions can be removed and they can be compared under the same scale. The specific normalization process is expressed by the following equation:

number

[0082] In the formula, f * i represents the normalized function expression, and f i represents the i-th function value, and f i_max and f i_minand represent the maximum and minimum values ​​of the i-th function in the interval, respectively.

[0083] The weighting processing method for the multi-target function model is to set different weighting coefficients according to the importance of the target function. The larger the weighting coefficient, the more emphasized a target is. In this embodiment, the weight of cutting energy consumption per unit volume is 1 / 3, the weight of machined surface roughness is 1 / 3, and the weight of cutting time per unit volume is 1 / 3.

[0084] Therefore, in this embodiment, the weighted function model is

number

[0085] Step 6: Find the solution for the optimization of the multi-target function model. In this embodiment, the method for finding the solution for the multi-target function model uses the black hole-continuous ant colony algorithm. The flowchart for finding the solution for the optimization of the multi-target function model based on the black hole-continuous ant colony algorithm is shown in Figure 3. The optimization program is written using Python 3.6, the computer uses the Windows 10 operating system, the processor is a quad-core Intel Core CPU, and the memory is 8GB.

[0086] In this embodiment, the tool parameters and process parameters obtained by finding the optimization solution are: (d=14, z=4, M=hard alloy, n=6000, F v =740, a e =1.0, a p =15).

[0087] Step 7: Predict the energy-saving effect based on the tool parameters and process parameters obtained by finding the optimization solution in Step 6.

[0088] In this example, the tool parameters and process parameter values ​​obtained by finding the optimization solution in step 6 are used as the cutting energy consumption per unit volume E cutting Function model, machined surface roughness R a Function model, cutting time per unit volume T cutting By substituting the function model and comparing it with empirical processing plans, the energy-saving effect can be predicted, as shown in Table 3, for example.

[0089] [Table 3]

[0090] As can be seen from Table 3, the optimized plan reduces the cutting energy consumption per unit volume by 22.140 J / mm compared with the empirical plan. 3 to 7.367J / mm 3 reduced to 14.773J / mm 3 As can be seen from the table, the surface roughness was reduced from 1.601 μm to 0.217 μm, and the cutting time per unit volume was reduced to 0.0171 s / mm 3 to 0.0054s / mm 3 This has reduced the cost to 100%, realizing the demand for green, high-quality and highly efficient production. The above is merely a preferred embodiment of the present invention, and it should be noted that those skilled in the art may make some further improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered to fall within the protection scope of the present invention. [Explanation of symbols]

[0091] 1 Tool, 2 Workpiece

Claims

1. A method for integrated optimization and energy saving prediction of tool and process parameters in side milling, comprising the steps of: Step 1: Determine the integrated optimization variables of tool parameters and process parameters in side milling; where the integrated optimization variables of the tool parameters include the tool diameter d, the tool tooth number z, and the tool material M; The integrated optimization variables of the process parameters are the spindle speed n and the feed rate F ｖ and milling width a ｅ and milling depth a ｐ and Step 2: Integrated optimization of tool and process parameters in side milling. Target: Cutting energy consumption per unit volume E cutting , processed surface roughness R ａ , cutting time per unit volume T cutting Determine Step 3: Determine integrated optimization constraint conditions of tool parameters and process parameters in side milling, where the integrated optimization constraint conditions of tool parameters include a tool diameter constraint, a tool tooth number constraint, and a tool material constraint, and the integrated optimization constraint conditions of process parameters include a spindle speed constraint, a feed rate constraint, a milling width constraint, and a milling depth constraint; Step 4: Establish an integrated optimization multi-target function model of tool parameters and process parameters in side milling; Step 5: Normalize and weight the multi-target function model; Step 6: Find the optimization solution for the multi-target function model; Step 7: Predicting the energy-saving effect based on the tool parameters and process parameters obtained by finding the optimization solution in Step 6; In step 2, the cutting energy consumption per unit volume E cutting The function model for the optimization target of [Equation 1] In the formula, E cutting is the cutting energy consumption per unit volume, in J / mm ３ and P cutting is the milling power in W, MRR is the material removal rate in mm ３ / s, Processing surface roughness R ａ The function model for the optimization target of [Equation 2] and in the formula, R ａ is the roughness of the processed surface in μm, K is the correction coefficient in the formula, and C Ｆ is the cutting force coefficient, the value of which depends on the workpiece material, α1 is the exponential term of the spindle speed n, and f Ｚ is the feed rate per tooth, β1 is the exponential term of the feed rate per tooth, and γ1 is the milling width a ｅ is the exponential term of the milling depth a ｐ is the exponential term of the tool diameter d, ζ is the exponential term of the number of tool teeth z, is the cutting force coefficient, and η is the cutting force coefficient C Ｆ is the exponential term of Cutting time per unit volume T cutting The function model for the optimization target of [Equation 3] and in the formula, T cutting is the cutting time per unit volume, in s / mm ３ and Milling Power P cutting The functional model is a polynomial regression model [Equation 4] In the formula, A is a coefficient term of the spindle rotation speed n, α2 is an exponent term of the spindle rotation speed n, and B is the feed amount f per tooth. Ｚ is a coefficient term of the feed rate per tooth, C is a coefficient term of the milling width, and γ is a coefficient term of the milling width a ｅ is the exponential term of the milling depth a ｐ is the coefficient term of the milling depth a ｐ where E is a coefficient term of the tool diameter d, ε2 is an exponential term of the tool diameter d, F is a coefficient term of the number of tool teeth z, ζ2 is an exponential term of the number of tool teeth z, and G is the cutting force coefficient C Ｆ η2 is the coefficient term of the cutting force coefficient C Ｆ is the exponential term of the equation, and H is the constant term of the equation. The tool diameter constraint is [Equation 5] In the formula, d １ ,d ２ ,・・・,d ｎ are optional tool diameters provided by tool manufacturers, and Tool tooth number constraints are [Equation 6] In the formula, z １ ,z ２ ,・・・,z ｎ are the optional tool tooth numbers provided by tool manufacturers, Tool material constraints are [Equation 7] and in the formula, C Ｆ１ ,C Ｆ２ ,・・・,C Ｆｎ are the cutting force coefficients corresponding to the optional tool materials provided by the tool manufacturer, respectively, The spindle speed constraint is [Equation 8] In the formula, n ｍｉｎ , n ｍａｘ are the minimum and maximum spindle speeds recommended by the tool manufacturer, respectively, and The feed rate constraint is [Equation 9] and in the formula, F ｖ＿ｍｉｎ , F ｖ＿ｍａｎ are the minimum and maximum feed rates recommended by the tool manufacturer, respectively, and The milling width constraint is [Equation 10] In the formula, a ｅ＿ｍｉｎ , a ｅ＿ｍａｘ are the minimum and maximum milling widths recommended by the tool manufacturer, respectively, and The milling depth constraint is [0011] In the formula, a ｐ＿ｍｉｎ , a ｐ＿ｍａｘ are the minimum and maximum milling depths recommended by the tool manufacturer, respectively, and In step 4, the integrated optimization multi-target function model of tool parameters and process parameters in side milling is [0012] The normalization method of the multi-target function model is to map the target function value within the interval (0, 1). The specific normalization process is expressed by the following formula: [0013] In the formula, f * i represents the normalized function expression, and f ｉ represents the i-th function value, and f ｉ＿ｍａｘ and f ｉ＿ｍｉｎ and represent the maximum and minimum values ​​of the i-th function in the interval, respectively. The weighting processing method for the multi-target function model is to set different weighting coefficients according to the importance of the target functions, and the weighted function model is as follows: [0014] and in the formula, c １ , c ２ , c ３ The integrated optimization and energy-saving prediction method for tool and process parameters in side milling is characterized in that:

2. The method for integrated optimization and energy-saving prediction of tool and process parameters in side milling as claimed in claim 1, characterized in that the method for solving the multi-target function model in step 6 adopts the black hole-sequential ant colony algorithm.

3. Step 7 is specifically as follows: The tool parameters and process parameter values ​​(d, z, M, n, F) obtained by finding the optimization solution in step 6 are ｖ , a ｅ , a ｐ ) is the cutting energy consumption per unit volume E cutting Function model, machining surface roughness R ａ Function model, cutting time per unit volume T cutting The method for integrated optimization and energy-saving prediction of tool and process parameters in side milling as claimed in claim 1, characterized in that the energy-saving effect can be predicted by substituting the function model and comparing it with empirical machining plans.

Images