Flat end mill parameter optimization and machining performance prediction method considering plurality of machining stages

Abstract

To disclose a flat end mill parameter optimization and machining performance prediction method considering a plurality of machining stages.SOLUTION: A flat end mill parameter optimization and machining performance prediction method considering a plurality of machining stages includes: identifying a plane to be machined by referring to a component drawing, analyzing size feature parameters of the plane to be machined, and determining model numbers of a machine tool and a cutting tool; setting optimization variables, selecting an optimization target, determining constraint conditions, and establishing a multi-target optimization mathematical model for flat end mill process parameters in comprehensive consideration of a plurality of machining-stage problems of a rough cutting rotary milling and a finish cutting rotary milling; and finding the optimal solution for the optimization model on the basis of a multi-target optimization algorithm, obtaining an optimal process parameter combination and a machining performance prediction value, and finally guiding a flat end mill machining process on the basis of the machining process parameter combination obtained by finding the solution.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the technical field of planar end mill parameter optimization, and particularly to a method for optimizing planar end mill parameters and predicting machining performance considering multiple machining stages.

Background Art

[0002] The planar end mill machining process is one of the most commonly seen machining methods and is widely used in the manufacturing process of parts and products. The success of the application of a planar end mill often depends on the optimization of process parameters to ensure high-quality machining, optimal production efficiency, and relatively low energy consumption.

[0003] Conventional optimization of planar end mill machining process parameters generally relies on empirical methods and tests. This may often limit the optimization range of process parameters and cause the inability to achieve optimal machining performance, and also waste materials and human resources. At present, research on the optimization of planar end mill machining process parameters often focuses on a single machining stage, that is, the rough milling stage or the finish milling stage. However, in actual production machining, the process often needs to combine multiple machining stages with each other, and the optimization of process parameters for multiple machining stages affects each other before and after and is a dynamically changing process. Therefore, the optimization results of process parameters for a single machining stage often lack applicability, and it is very difficult to directly use the optimization results in actual production.

Summary of the Invention

Problems to be Solved by the Invention

[0004] In view of the problems existing in the prior art, the present invention provides a method for optimizing planar end mill parameters and predicting machining performance considering multiple machining stages. This method comprehensively considers multiple machining stages, establishes an accurate multi-target mathematical model, and obtains solutions through an optimization algorithm, thereby accurately and efficiently determining optimal process parameters, realizing pre-prediction of machining performance, and further guiding the planar end mill machining process.

Means for Solving the Problems

[0005] The present invention adopts the following technical solutions.

[0006] The method for optimizing planar end mill parameters and predicting machining performance considering multiple machining stages includes the following steps.

[0007] Step 1: A preparatory operation. Refer to the part drawing, identify the plane to be machined, analyze the size characteristic parameters of the plane to be machined, determine the machining machine model number and cutting tool model number, and obtain the machining process.

[0008] JPEG2025093837000002.jpg30170

[0009] Step 3: Select optimization targets such as machining efficiency, machining energy consumption, and machining quality.

[0010] Step 4: Determine the constraint conditions.

[0011] Step 5: Establish a multi-target optimization mathematical model for planar end mill process parameters.

[0012] Step 6: Obtain machining process parameter combinations and machining performance prediction values based on the multi-target optimization mathematical model of planar end mill process parameters in Step 5.

[0013] Step 7: Guide the actual planar end mill machining process based on the machining process parameter combinations and machining performance prediction values obtained in Step 6.

[0014] Preferably, Step 3 specifically includes the following.

[0015] The target function model of machining efficiency is JPEG2025093837000003.jpg11170

[0016] JPEG2025093837000004.jpg76170

[0017] The target function model of machining energy consumption is JPEG2025093837000005.jpg10170

[0018] JPEG2025093837000006.jpg29170JPEG2025093837000007.jpg39170JPEG2025093837000008.jpg17170

[0019] The target function model of machining quality is JPEG2025093837000009.jpg9170

[0020] JPEG2025093837000010.jpg66170

[0021] Preferably, the constraint conditions in Step 4 include spindle speed constraint, feed per revolution constraint, feed rate constraint, end mill depth of cut constraint, end mill width of cut constraint, rated power constraint of the machine tool spindle, and cutting tool life constraint.

[0022] JPEG2025093837000011.jpg9170

[0023] JPEG2025093837000012.jpg35170

[0024] JPEG2025093837000013.jpg9170

[0025] JPEG2025093837000014.jpg8170JPEG2025093837000015.jpg25170

[0026] JPEG2025093837000016.jpg9170

[0027] JPEG2025093837000017.jpg48170

[0028] JPEG2025093837000018.jpg10170

[0029] JPEG2025093837000019.jpg36170

[0030] JPEG2025093837000020.jpg9170

[0031] JPEG2025093837000021.jpg36170

[0032] JPEG2025093837000022.jpg15170

[0033] JPEG2025093837000023.jpg27170

[0034] JPEG2025093837000024.jpg8170

[0035] JPEG2025093837000025.jpg17170

[0036] JPEG2025093837000026.jpg75170

[0037] Preferably, the multi-target optimization mathematical model of the planar end mill process parameters is JPEG2025093837000027.jpg145170

[0038] Preferably, the tool life formula is a generalized Taylor tool life formula JPEG2025093837000028.jpg16170

[0039] JPEG2025093837000029.jpg36170

[0040] Preferably, step 6 is specifically as follows JPEG2025093837000030.jpg7170JPEG2025093837000031.jpg20170

[0041] When obtaining the solution of the multi-target optimization mathematical model, some optimization algorithms representative for processing multi-target optimization models such as the particle swarm algorithm, genetic algorithm, artificial cellular group algorithm, etc. may be adopted. These algorithms have the characteristics of generating a plurality of points and performing multi-directional search, and are very applicable to processing very complex multi-target optimization problems in the optimal solution search space such as process parameter optimization.

Advantages of the Invention

[0042] The beneficial effects of the present invention are as follows

[0043] Conventional optimization of face mill machining process parameters is to perform local optimization for a single rough milling process or finish milling process, and the machining cost used in the optimization process is often the machining cost defined by the enterprise. The present invention takes the rough milling stage and the finish milling stage as a whole, performs overall optimization, and further makes the applicability based on the optimization results. In addition, when dealing with the constraint conditions, the present invention considers the cutting tool life so that the optimization results have more practical significance. The present invention solves the problems of low machining efficiency, high energy consumption, and poor machining quality caused by the selection of inappropriate process parameters in the face mill machining process, and the lack of applicability of the optimization results of the single machining stage process parameters.

Brief Description of the Drawings

[0044]

Figure 1

Figure 2

Figure 3

Modes for Carrying Out the Invention

[0045] Hereinafter, the specific embodiments of the present application will be described in more detail in conjunction with the drawings and examples. JPEG2025093837000032.jpg73170JPEG2025093837000033.jpg45170

[0046] Example 1, by combining FIGS. 1 to 3, the method for optimizing planar end mill parameters and predicting machining performance considering multiple machining stages includes the following steps.

[0047] JPEG2025093837000034.jpg35170

[0048] JPEG2025093837000035.jpg36170

[0049] In this example, an XHK-714F vertical machining center and a W400F-FS coated tungsten steel tool are selected and used.

[0050] JPEG2025093837000036.jpg87170JPEG2025093837000037.jpg77170

[0051] JPEG2025093837000038.jpg29170

[0052] Step 3: Select the optimization targets of machining efficiency, machining energy consumption, and machining quality.

[0053] Specifically, it includes the following. The machining efficiency target function model is JPEG2025093837000039.jpg10170

[0054] JPEG2025093837000040.jpg76170

[0055] JPEG2025093837000041.jpg15170JPEG2025093837000042.jpg8170

[0056] JPEG2025093837000043.jpg19170

[0057] JPEG2025093837000044.jpg8170JPEG2025093837000045.jpg30170

[0058] JPEG2025093837000046.jpg38170

[0059] JPEG2025093837000047.jpg63170

[0060] JPEG2025093837000048.jpg48170JPEG2025093837000049.jpg36170JPEG2025093837000050.jpg9170

[0061] JPEG2025093837000051.jpg26170

[0062] JPEG2025093837000052.jpg9170

[0063] JPEG2025093837000053.jpg27170

[0064] JPEG2025093837000054.jpg16170

[0065] JPEG2025093837000055.jpg37170

[0066] Here, in this embodiment, the consumption time of the automatic cutter change during the auxiliary time is JPEG2025093837000056.jpg9170

[0067] JPEG2025093837000057.jpg25170

[0068] The time consumed when manual cutter change is required for the machine tool due to tool wear during the auxiliary time is JPEG2025093837000058.jpg33170 and may be calculated as

[0069] JPEG2025093837000059.jpg17170

[0070] In this embodiment, the processing efficiency target function model may be represented in more detail as JPEG2025093837000060.jpg101170.

[0071] The processing energy consumption target function model is JPEG2025093837000061.jpg18170

[0072] JPEG2025093837000062.jpg30170 JPEG2025093837000063.jpg57170

[0073] Since the energy consumption in the numerical control processing process is equal to the integral of power with respect to time, the processing energy consumption target function model may be more specifically represented as JPEG2025093837000064.jpg50170.

[0074] JPEG2025093837000065.jpg67170

[0075] In this embodiment, the power function model of each sub - processing process in the face - milling process is shown as in Table 1.

[0076] Power function model of each sub - processing process in the face - milling process JPEG2025093837000066.jpg215170

[0077] JPEG2025093837000067.jpg18170 JPEG2025093837000068.jpg8170 JPEG2025093837000069.jpg134170

[0078] Furthermore, in this embodiment, the processing energy consumption target function model may be represented in more detail as JPEG2025093837000070.jpg170170.

[0079] JPEG2025093837000071.jpg73170JPEG2025093837000072.jpg9170JPEG2025093837000073.jpg7170

[0080] The processed quality target function model is JPEG2025093837000074.jpg10170

[0081] JPEG2025093837000075.jpg67170

[0082] In this embodiment, an exponential surface roughness prediction model is adopted, and the function expression is JPEG2025093837000076.jpg10170

[0083] Step 4: Determine the constraint conditions. The constraint conditions include spindle speed constraint, feed rate constraint, feed speed constraint, milling depth constraint, milling width constraint, rated power constraint of the machine tool spindle, and cutting tool life constraint.

[0084] JPEG2025093837000077.jpg9170

[0085] JPEG2025093837000078.jpg9170

[0086] JPEG2025093837000079.jpg10170

[0087] JPEG2025093837000080.jpg10170

[0088] JPEG2025093837000081.jpg10170

[0089] JPEG2025093837000082.jpg15170

[0090] JPEG2025093837000083.jpg9170

[0091] JPEG2025093837000084.jpg20170

[0092] Regarding the plane end mill machining of multiple machining stages combining rough turning milling and finish turning milling, the rough turning milling stage and the finish turning milling stage often adopt different constraint conditions. During rough turning milling, the improvement of productivity should be the main focus. Generally, relatively large milling depths of cut and feed rates should be selected, and the cutting speed should not be set extremely high. During finish turning milling, the guarantee of the machining accuracy and surface quality of the parts should be the main focus, and relatively small milling depths of cut, feed rates and relatively high cutting speeds are often adopted. In addition, combining the technical specification parameters of the XHK-714F vertical machining center and the recommended range of cutting parameters of the W400F-FS coated tungsten steel tool, the summary of the constraint conditions in the machining process of this embodiment is shown in Table 2 as follows.

[0093] Constraint conditions JPEG2025093837000085.jpg145170

[0094] Step 5: Establish a multi-target optimization mathematical model for the plane end mill process parameters.

[0095] The multi-target optimization mathematical model for the plane end mill process parameters is JPEG2025093837000086.jpg10170

[0096] Step 6: Obtain the combination of machining process parameters and the predicted machining performance values based on the multi-target optimization mathematical model of the face end mill process parameters in Step 5. When solving the multi-target optimization mathematical model of the face end mill process parameters, some representative optimization algorithms such as the particle swarm algorithm, genetic algorithm, and artificial cellular group algorithm may be adopted to process the multi-target optimization model. These algorithms have the characteristics of generating multiple points and performing multi-directional searches, and are very applicable to dealing with very complex multi-target optimization problems in the optimal solution search space such as process parameter optimization.

[0097] JPEG2025093837000087.jpg49170

[0098] The combination of process parameters and the predicted machining performance values obtained based on the present invention, and the combination of process parameters and the predicted machining performance values based on experience are shown in Table 3.

[0099] Process parameter combinations and predicted machining performance values for the optimal solution and the empirical solution JPEG2025093837000088.jpg73170

[0100] Step 7: Guide the actual face end mill machining process based on the combination of machining process parameters and the predicted machining performance values obtained in Step 6.

[0101] JPEG2025093837000089.jpg7170JPEG2025093837000090.jpg78170The above-mentioned is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art of this technology, several more improvements and substitutions can be made without departing from the technical principle of the present invention, and these improvements and substitutions should also be regarded as falling within the protection scope of the present invention.

Description of symbols

[0102] 1 Cutting tool 2 Path 3 Plane to be machined 4 Material removed in rough milling stage 5 Material removed in finish milling stage

Claims

1. A method for optimizing parameters of a flat end mill and predicting machining performance considering multiple machining stages, comprising the steps of: Step 1: A preparation operation, which refers to a part drawing, identifies a plane to be machined, analyzes the size characteristic parameters of the plane to be machined, and determines the machining tool model and cutting tool model to obtain the machining process; Step 3: Select the optimization targets: processing efficiency, processing energy consumption, and processing quality. Specifically, this includes: The target function model for machining efficiency is The target function model for processing energy consumption is The target function model for processing quality is Step 4: Determine the constraints. The constraint conditions include a spindle speed constraint, a feed amount constraint, a feed rate constraint, a milling depth constraint, a milling width constraint, a rated power constraint of a machine tool spindle, and a cutting tool life constraint; Step 5: Establish a multi-target optimization mathematical model of the plane end mill process parameters; The multi-target optimization mathematical model of the plane end mill process parameters is Step 6: obtain the machining process parameter combination and machining performance prediction value according to the multi-target optimization mathematical model of the plane end mill process parameters of step 5; Step 7: Based on the machining process parameter combination and machining performance prediction value obtained in step 6, instruct the actual plane end mill machining process.

2. The tool life formula is the generalized Taylor tool life formula

3. Step 6 is specifically as follows:

Images