Casting type thin-wall cabin cutting machining parameter optimization method

Abstract

The invention relates to a method for optimizing cutting machining parameters of a cast thin-wall cabin, which solves the problems of low machining efficiency and unstable machining precision of the current cast thin-wall cabin in numerical control machining, and comprises the following steps of: 1, classifying machining features of a cast thin wall, and determining feature allowance based on a three-dimensional scanning result of a casting; step 2, aiming at the divided machining features, based on machining deformation test data, establishing a feature machining deformation prediction model by using a least square support vector machine; and step 3, for the divided machining features, establishing a cutting machining parameter optimization model of the features, and performing optimization solution by using a genetic algorithm to realize cutting machining parameter optimization. According to the method, cutting machining parameter optimization selection is carried out on different machining characteristics, meanwhile, the influence of cutting machining parameters on machining deformation is considered when the cutting machining parameters are optimized, the machining efficiency and precision are considered, and the optimized cutting machining parameters have more guiding significance for actual production.

Description

Technical Field

[0001] The present invention relates to a method for optimizing cutting parameters of a casting thin-wall cabin body, which involves problems such as machining feature classification, prediction algorithm and optimization algorithm, and belongs to the technical field of numerical control machining. Background Art

[0002] Casting thin-walled parts are mostly produced by aluminum alloy and titanium alloy casting, which have complex structures, large sizes and many thin-walled features. Due to the inconsistency of cast products, the entire thin-walled parts machining has the disadvantages of long processing time and unstable product quality, resulting in poor product accuracy consistency and low processing efficiency.

[0003] Therefore, the present invention proposes a method for optimizing the cutting processing parameters of cast thin-walled cabin bodies. This method classifies the processing characteristics and determines the casting allowance of thin-walled cabin body casting blanks, and performs a targeted cutting processing parameter optimization method. At the same time, it considers the influence of cutting processing parameters on processing deformation, which has certain guiding significance for actual production and processing. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the cutting parameters of a casting-type cabin body in order to solve the problems of low machining efficiency and unstable machining accuracy in CNC machining of the current casting thin-wall cabin body.

[0005] The present invention is achieved by the following technical solutions:

[0006] A method for optimizing cutting parameters of a casting thin-walled cabin body comprises the following steps:

[0007] Step 1: Classify the machining features of casting thin walls and clarify the feature allowance based on the 3D scanning results of the castings;

[0008] Step 2: Based on the divided processing features and processing deformation test data, a feature processing deformation prediction model is constructed using a least squares support vector machine;

[0009] Step 3: For the divided processing features, establish a cutting processing parameter optimization model for the features, use genetic algorithms to optimize and solve, and achieve cutting processing parameter optimization.

[0010] In step 2, for the acquired machining deformation test data, a support vector machine is used to construct a mapping relationship between machining deformations of different machining features and cutting parameters, thereby obtaining a machining deformation prediction model of different machining features.

[0011] In step 3, the cutting parameter optimization model includes: optimization variables, optimization objectives and constraints.

[0012] Furthermore, spindle speed, feed per tooth, cutting depth and cutting width are selected as optimization variables; cutting efficiency is selected as optimization target; machining deformation, machine tool power, spindle speed, feed per tooth, cutting depth and cutting width are selected as constraints.

[0013] Furthermore, the machining deformation constraints are constructed using SVM, and the remaining constraints are selected according to the CNC machine tool and actual machining conditions.

[0014] Compared with the problem of unreasonable selection of cutting parameters in traditional casting thin-wall processing, the present invention optimizes the selection of cutting parameters for different processing features based on the processing feature division. At the same time, the influence of cutting parameters on processing deformation is considered when optimizing cutting parameters, taking into account both processing efficiency and precision. The optimized cutting parameters are more instructive for actual production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the present invention;

[0016] Figure 2 It is a schematic diagram of the genetic algorithm optimization solution of the present invention. DETAILED DESCRIPTION

[0017] The method of the present invention first classifies the machining features of casting thin walls, and based on the three-dimensional digital scanning results of the casting blank, clarifies the margin state of each machining feature; takes machining efficiency as the optimization goal, optimizes the cutting parameters of each machining feature; while optimizing the cutting parameters, the Support Vector Machine (SVM) is used to establish the mapping relationship between the cutting parameters and the machining deformation for each divided machining feature based on the cutting test data, and uses it as the constraint condition for the optimization of the characteristic cutting parameters. After adopting this method, the machining features of casting thin walls are optimized for the cutting parameters, and the influence of the cutting parameters on the machining deformation is considered at the same time, which can effectively improve the current problems of low machining efficiency and unstable machining accuracy of casting thin walls.

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Figure 1 This is a flow chart of a casting thin-wall cutting process parameter optimization method of the present invention. Figure 1 As shown, the following steps are included:

[0020] Step 1: Classify the machining features of casting thin walls and clarify the feature allowance based on the 3D scanning results of the castings;

[0021] According to the characteristics of large size, complex structure and multiple thin-wall features of castings, they can be mainly divided into common features such as special-shaped curved surfaces, deep cavities, grooves, ribs and holes. Different feature processing methods have different focuses. Therefore, before processing, the processing features of the casting thin walls to be processed are classified, and the allowance of each processing feature is clarified through the comparison of the 3D scanning data of the casting.

[0022] Step 2: Based on the divided processing features and processing deformation test data, a feature processing deformation prediction model is constructed using a support vector machine;

[0023] In the process of casting thin-wall processing, machining deformation is inevitable, which is also one of the important factors restricting the processing quality of products. The present invention designs cutting tests with different machining characteristics according to the different machining characteristics of casting thin-walls, and obtains the corresponding data of cutting parameters and machining deformation.

[0024] According to the acquired machining deformation test data, the present invention adopts support vector machine to construct the mapping relationship between machining deformation of different machining features and cutting parameters (spindle speed, feed per tooth, cutting depth, cutting width), so as to obtain machining deformation prediction models of different machining features.

[0025] Step 3: For the divided processing features, establish a cutting processing parameter optimization model for the features, use genetic algorithms to optimize and solve, and achieve cutting processing parameter optimization.

[0026] The cutting parameter optimization model in the present invention includes: optimization variables, optimization targets and constraints. According to the characteristics of thin-walled casting processing, the present invention selects spindle speed, feed per tooth, cutting depth and cutting width as optimization variables; selects cutting efficiency as optimization target; selects processing deformation, machine tool power, spindle speed, feed per tooth, cutting depth and cutting width as constraints. Except for the processing deformation constraint condition constructed by SVM, the remaining constraints can be selected according to the CNC machine tool and actual processing conditions.

[0027] After the cutting parameter optimization model based on various machining features is constructed, the genetic algorithm is selected as the optimization algorithm for solving the optimization model of different machining features. The solution process of the genetic algorithm is as follows: Figure 2 shown.

[0028] It should be understood that the present invention is not limited to the aforementioned implementation examples. The above embodiments are only for illustrating the specific implementation methods of the present invention. All equivalent or simply transformed technical solutions based on the ideas described in the present invention are within the protection scope of the present invention.

Claims

1. A method for optimizing cutting parameters of a casting thin-walled cabin, characterized by: The following steps are involved: Step 1: Classify the machining features of casting thin walls and clarify the feature allowance based on the 3D scanning results of the castings; Step 2: Based on the divided processing features and processing deformation test data, a feature processing deformation prediction model is constructed using a least squares support vector machine; Step 3: For the divided processing features, establish a cutting processing parameter optimization model for the features, use genetic algorithms to optimize and solve, and achieve cutting processing parameter optimization.

2. The method for optimizing cutting parameters of a casting thin-walled cabin according to claim 1 is characterized by: In step 2, for the acquired machining deformation test data, a support vector machine is used to construct a mapping relationship between machining deformations of different machining features and cutting parameters, thereby obtaining a machining deformation prediction model of different machining features.

3. The method for optimizing cutting parameters of a casting thin-walled cabin according to claim 1 is characterized by: In step 3, the cutting parameter optimization model includes: optimization variables, optimization objectives and constraints.

4. The method for optimizing cutting parameters of a casting thin-walled cabin according to claim 3 is characterized by: Select spindle speed, feed per tooth, cutting depth and cutting width as optimization variables; select cutting efficiency as optimization target; select machining deformation, machine tool power, spindle speed, feed per tooth, cutting depth and cutting width as constraints.

5. The method for optimizing cutting parameters of a casting thin-walled cabin according to claim 4 is characterized by: The machining deformation constraints are constructed using SVM, and the remaining constraints are selected according to the CNC machine tools and actual machining conditions.

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