Abstract:
The gear shaving cutting parameters optimization method based on genetic algorithm for green manufacturing. Due to the irrationality of traditional gear shaving cutting parameters in machine tools, which leads to high energy consumption during production, this method aims to minimize energy consumption by selecting the spindle speed, feed rate, and other relevant parameters as optimization variables. The objective function is established, and constraints related to cutting speed, feed speed, and workpiece quality are considered. Genetic algorithm is utilized to solve the energy consumption optimization function for gear shaving processing, thus completing the optimization of gear shaving cutting parameters based on genetic algorithm. Experimental results demonstrate that the electricity consumption in gear shaving production after optimization is less than before.
1. Introduction
Machine tools are crucial manufacturing equipment in current machinery processing. To further enhance machine tool efficiency and reduce processing costs, research on cutting parameters has become a significant focus in this field. Additionally, with the increasing application of information technology, machine tool processing has gradually evolved into numerical control (NC) processing mode. During NC processing of gears, there are various options for shaving tool parameters, and different cutting parameters result in significant differences in gear precision and shaving tool lifespan. Therefore, the optimal selection of gear shaving cutting parameters directly determines the efficiency and quality of NC processing. In the process of gear workpiece and gear shaving rotation, the gear shaft and tool shaft do not operate in parallel but intersect each other. Hence, the meshing theory is proposed based on this fundamental principle. In actual operation of gear workpieces and shaving tools, there is no substantial mechanical connection between the two bearings, and they rotate freely. To further improve the rotational performance of gear shaving and extend its lifespan in NC machine tools, this paper conducts research on gear shaving cutting parameters optimization method based on genetic algorithm for green manufacturing.
2. Gear Shaving Cutting Parameters Optimization Method Design Based on Genetic Algorithm
2.1 Determination of Optimization Variables and Objective Function for Gear Shaving Cutting Parameters
Determining the objective function for gear shaving cutting parameters optimization in green manufacturing involves numerous influencing factors, including multiple optimization variables such as the NC machine tool model, gear shaving specifications, related materials, and tools connected to the gear shaving. Furthermore, in different processing technologies, the process route also significantly affects the optimization target of gear shaving cutting parameters. However, in the proposed gear shaving cutting parameters optimization method based on genetic algorithm for green manufacturing in this paper, for NC machine tools with completed production plans, parameters such as relevant materials, NC machine tool equipment, and connected tools are already defined and are not considered excessively during optimization. Therefore, this paper only focuses on optimizing some detailed parameters within the gear shaving cutting parameters, including cutting speed, feed rate per gear, vertical feed speed of gear shaving, direction of vertical feed for gear shaving, maximum cutting layer size, and tool engagement in the feed direction.
In practical NC machine tool processing, much attention is usually paid to processing efficiency, with less focus on the environmental impact of energy consumption during processing. Therefore, when determining the cutting parameter optimization objective function, this paper adopts green manufacturing as a fundamental principle, taking into account both processing requirements and efficient energy conservation. Gear shaving cutting parameter optimization refers to removing a portion of the metal from components during cutting to meet the expected geometric shape and other related technical requirements. Thus, by increasing the amount of metal removed per unit time by the gear shaving, the production efficiency of gear shaving processing can be directly enhanced, further optimizing energy consumption during processing. The objective function can be specifically expressed as:
Where W represents the objective function for gear shaving cutting parameters optimization based on genetic algorithm for green manufacturing; v represents the actual spindle speed of the gear shaving; m represents the actual feed rate per gear; γ represents the rotational speed of each small gear on the gear shaving; s_p represents the maximum size of the cutting layer perpendicular to the feed direction; and x_e represents the maximum size of the cutting layer parallel to the feed direction. Through an investigation of gear shaving in actual machinery processing enterprises, it is found that the energy consumed by gear shaving mainly includes energy consumption from the transmission device’s own no-load operation, the feed device’s own no-load operation, the energy consumption of related auxiliary devices, and gear shaving cutting energy consumption. Therefore, when using the above formula (1) for calculation, the parameters involved in the formula should be adjusted according to the actual situation to make the final optimization scheme more suitable for the needs of machinery processing enterprises.
2.2 Establishment of Constraints for Gear Shaving Cutting Parameters Based on Green Manufacturing
After defining the optimization variables and objective function for gear shaving cutting parameters, it is necessary to further establish constraints for gear shaving cutting parameters based on green manufacturing. As mentioned earlier, the optimization results of gear shaving cutting parameters are directly related to factors such as cutting speed, feed speed, and workpiece processing quality. Therefore, constraints related to cutting speed, feed speed, workpiece quality, and power should be considered when optimizing gear shaving cutting parameters.
Table 1: Constraints for Gear Shaving Cutting Parameters
| Constraint Type | Description |
|---|---|
| Cutting Speed Constraint | The spindle speed of the gear shaving should be within a certain range. |
| Feed Speed Constraint | The perpendicular cutting amount in the feed direction should not exceed the maximum allowable cutting amount of the NC machine tool. |
| Workpiece Quality Constraint | The surface roughness of the workpiece should meet the allowable range for gear shaving. |
2.3 Solution to Energy Consumption Optimization Function for Gear Shaving Processing Based on Genetic Algorithm
The optimization of gear shaving cutting parameters belongs to multi-objective optimization. In most multi-objective optimization processes, the influencing factors of the objective optimization often have conflicts. Therefore, optimizing a sub-objective may lead to changes in other already optimized sub-objectives, resulting in the final optimization result not meeting expectations. Based on this, genetic algorithm is introduced into the solution process of the multi-objective optimization function. Firstly, the advantages of the non-dominated fast sorting method in genetic algorithm are utilized to minimize the complexity of the optimization function solution process. Secondly, by comparing the crowding degrees of two different genetic structures, the need for data sharing in advance in traditional calculation methods is overcome, better ensuring the diversity of different genetic structures. Finally, continuous iteration improves the optimization performance of the algorithm and ensures that excellent genetic structures are not lost during the genetic optimization process. The above methods can improve the overall calculation efficiency and stability of solving the optimization function in this paper.
The solution process is as follows: Firstly, multiple different optimization genetic targets are randomly generated using genetic algorithm and set as the initial set of gear shaving cutting parameter optimization targets. After non-dominated sorting, multiple different optimized sub-genetic structures are generated through random selection, mutation, or crossover genetic operations. Secondly, the initial set of gear shaving cutting parameter optimization targets is merged with the optimized sub-genetic structure set to form a new set. Thirdly, non-dominated sorting is performed on the new set to find the new primary sub-genetic structure set with the most appropriate sorting value and sub-genetic structure among them in terms of crowding degree. Finally, it is judged whether each optimization target in the finally generated optimized sub-genetic structure set meets the constraints set above. If it does, the output result is the final optimization scheme. If not, the second step is repeated until the final result meets all constraints, thus completing the solution to the energy consumption optimization function for gear shaving processing based on genetic algorithm and determining the cutting parameters for green manufacturing of gear shaving.
3. Comparative Experiment
The experiment selects the YJL26615G machine tool as the experimental object and uses the designed optimization method to optimize the gear shaving cutting parameters of this machine tool. The specifications of the gear shaving of this machine tool are shown in Table 2.
Table 2: Specifications of the Gear Shaving of the Machine Tool
| Sequence Number | Content | Parameter |
|---|---|---|
| 1 | Maximum Processing Diameter | 180 mm |
| 2 | Maximum Processing Module | 5 mm |
| 3 | Maximum Gear Shaving Rotation Angle | 35.5° |
| 4 | Distance from Gear Shaving Center to Workbench Center | 45~180 mm |
| 5 | Maximum Workbench Rotation Speed | 220 r/min |
| 6 | Machine Tool Spindle Speed | 155~480 r/min |
| 7 | Gear Shaving Material | DP20 High-speed Steel |
| 8 | Number of Gear Teeth | 42 |
The machine tool is used for mechanical equipment processing with 100 pieces and a processing time of 24 hours. The cutting parameters before optimization are: actual cutting speed of the gear shaving spindle is 155 r/min, cutting layer size of the gear shaving is 4.5 mm, and perpendicular cutting amount in the feed direction is 1.8 mm/r. After optimization using the proposed method, the cutting parameters are: actual cutting speed of the gear shaving spindle is 163 r/min, cutting layer size of the gear shaving is 5.5 mm, and perpendicular cutting amount in the feed direction is 2.4 mm/r. During the experiment, the electric energy consumption before and after the optimization of the gear shaving cutting parameters of the machine tool was recorded as the experimental result, to analyze the optimization method of gear shaving cutting parameters for green manufacturing based on the genetic algorithm.
It can be concluded from the table above that after optimizing the gear shaving cutting parameters for green manufacturing based on the genetic algorithm, the electric energy consumption required for gear shaving cutting production of the machine tool is far lower than that before optimization, indicating that the proposed optimization method can effectively reduce the electric energy consumption of gear shaving cutting of the machine tool and meet the demand for green manufacturing of gear shaving cutting.
In conclusion, this paper combines the genetic algorithm with the gear shaving parameters of traditional CNC machine tools to optimize the gear shaving cutting parameters for green manufacturing. Currently, mechanical processing enterprises have a large number of CNC machine tools, which consume a significant amount of energy. If the development of CNC machine tools continues in this manner, it will seriously violate the fundamental requirements of green construction. Therefore, when optimizing the gear shaving cutting parameters, green manufacturing should be taken as the main principle to reduce energy waste through optimization, thereby introducing the concept of green manufacturing into the digital development of mechanical processing and providing a basis for optimizing cutting parameters for efficient and energy-saving mechanical processing.