The impact and opportunities that artificial intelligence brings to industries are happening, and its application in CNC machining and the way it is realized is gaining attention.
CNC machine tool programming is an important part of the manufacturing industry, which can utilize computer technology to achieve accurate processing of machine tools.
To address the shortcomings in traditional CNC machine tool programming, artificial intelligence should be introduced.
This will help reduce labor costs and shorten programming time. Additionally, AI can automatically generate CNC machine tool programs.
Artificial intelligence can simulate the human brain, expand a variety of theories and technologies, and can achieve better application results in CNC machining program optimization.
After the user gives the instruction, artificial intelligence will understand the needs of the instruction. It will then generate the corresponding CNC program.
The program can also be adjusted according to the actual situation. This greatly improves the efficiency of CNC machining production.
Advantages of the application of artificial intelligence in CNC machining program optimization
(1) Improve CNC machining accuracy.
Artificial intelligence based on deep learning and big data analysis can further optimize the CNC machining process and improve the accuracy of various parameters.
For example, when executing the cutting program, artificial intelligence can monitor various parameters in real time. These parameters may include vibration, temperature, and others. AI can also predict the degree of wear on the tool.
Furthermore, based on the actual situation of the cutting process, the system can adjust and optimize the process. This helps improve the overall quality of the cutting process.
(2) Improve the efficiency of CNC machining.
The application of artificial intelligence can effectively analyze large amounts of data in CNC machining.
Based on this analysis, it can identify the best combination of parameters. AI can also control these parameters to accelerate the speed of CNC machining.
The CNC machining program optimized by artificial intelligence will automatically identify tool interference situations.
It will also reduce tool wear. By avoiding the impact of frequent tool changes, the program helps improve the efficiency of CNC machining.
(3) Improve the level of automation.
The CNC machining program optimized by artificial intelligence has a higher level of automation.
The program can automatically run to complete the processing task without manual intervention from the operator.
This reduces the pressure on the operator’s work. As a result, the production line becomes more efficient.
Additionally, there is less reliance on the operator’s technical ability, which helps improve the stability of the CNC machining line.
(4) Processing complex tasks.
In the field of CNC machining program optimization, artificial intelligence can efficiently handle the machining process of complex parts, thus improving the precision of products.
The technology can also construct high-precision models to further guarantee the accuracy of the machining process.
(5) Strengthen the fault warning function.
Optimized by artificial intelligence, the CNC machining program has a good fault warning function.
Artificial intelligence can fully control the operation of CNC machine tools. Through real-time supervision, it gathers first-hand information.
AI then analyzes and transmits the work data. This enables timely detection of problems, which can be addressed by the relevant personnel. By doing so, machine tool failures are prevented, helping avoid delays in production work.
(6) Improve application flexibility.
Artificial intelligence can be adapted to different processing environments. It can also meet the implementation of various types of instructions.
This makes its application more flexible. Additionally, artificial intelligence can continue learning.
Artificial intelligence can respond to new processing needs by re-learning to further adjust and optimize the CNC machining program.
Continuous learning method of the CNC machining program based on artificial intelligence
Continuous learning refers to the introduction of the concept of task sequence based on traditional machine learning in order to achieve continuous learning, knowledge migration, deep learning, and multi-task learning.
Continuous learning can refine and consolidate the knowledge that has been learned and apply it to respond to new tasks and update the knowledge base.
Continuous learning is characterized by two main points:
One is based on the deep utilization of knowledge acquired in past tasks to cope with the problem of insufficient samples and improve the learning effect;
The second step is to strengthen the ability of knowledge migration. This helps shorten the training time of the model as much as possible.
It also ensures the continuity of learning and reduces the cost of learning. These improvements are necessary to adapt to the needs of different environments.
The framework for continuous learning of CNC machining programs based on artificial intelligence is as follows:
(1) Task selection.
The key to the speed of human learning is the systematic organization of knowledge learning.
Previously learned knowledge provides a solid foundation for subsequent learning, thus realizing the cumulative effect of knowledge.
The continuous learning carried out in optimizing the CNC machining program should also draw on this approach.
The previous method of randomly selecting tasks from the task set should be abandoned to avoid learning other tasks that are not related to the existing knowledge system;
We can integrate sequential tasks by first picking relatively simple tasks to learn, and then gradually increasing the complexity of the tasks to ensure a gradual learning process.
(2) Knowledge accumulation.
Knowledge is a learning bridge connecting tasks, and its form of existence is different in different models.
When implementing continuous learning, it is important to clarify the form in which knowledge exists.
The appropriate form of knowledge should be selected based on the key characteristics of the task.
Additionally, care must be taken to avoid forgetting previously learned knowledge when updating the learning model.
The traditional machine learning model is to train all the data at once and does not pay attention to the time factor, which is not conducive to the updating and accumulation of knowledge.
Continuous learning emphasizes the accumulation of knowledge, both to learn new knowledge and not to forget the knowledge already learned.
The continuous learning model based on artificial intelligence can store the knowledge it has learned. It also has the ability to acquire new knowledge.
This capability helps further improve the optimization level of CNC machining programs and prevents the risk of catastrophic forgetting.
Knowledge accumulation emphasizes the long-term and short-term nature of memory, which needs to be combined with old task samples and new tasks when learning new model knowledge.
(3) Knowledge refinement.
Artificial intelligence continuous learning is characterized by learning incremental tasks.
To improve learning efficiency and address the issue of insufficient sample data for knowledge representation, knowledge should be refined.
This refinement helps preserve the knowledge learned by the model from each task.
There are two common types of knowledge refinement, one is knowledge distillation, and the other is automatic coding.
(4) Knowledge migration.
In continuous learning, knowledge migration is bi-directional. The knowledge learned from old tasks can be used when learning new knowledge.
At the same time, after learning new knowledge, it can also be applied to complete old tasks.
Forward migration of knowledge in continuous learning scenarios is relatively easy, although reverse migration can be realized based on saved task data, but the migration effect is weak.
Application Process of Artificial Intelligence in CNC Machining Program Optimization
Collecting Data
First, the original CNC machining program data, which has not been optimized, is collected. Next, the collected program data is analyzed to identify hidden machining path patterns.
This analysis helps clarify the program’s running requirements, laying a solid foundation for program optimization.
Training algorithm
(1) Genetic algorithm.
The algorithm is a simulation of the genetic mechanism, emphasizing the natural selection of biological evolution, and searching for the best solution through crossover, mutation and other operations.
When applying the genetic algorithm to optimize the CNC machining program, each parameter in it can be coded as a chromosome, and individuals with different parameter combinations will form a population.
The processing performance index corresponding to each individual is calculated and used as the fitness value. Based on this, selection is made.
The optimized program should inherit the advantages of the original program. This is achieved through iterative updates of the optimized machining program parameter combinations.
(2) Neural network algorithm.
The algorithm is trained using a large number of sample data. During this process, it learns the patterns and relationships present in the data.
This allows the algorithm to effectively regulate the connection weights between neurons, leading to the creation of the corresponding algorithmic model.
When using the neural network algorithm in the optimization of CNC machining programs, the input values are the machining parameters and the output values are the machining results.
After constructing the neural network model, it can be trained using data from various processing parameters.
Once trained, the model can effectively predict the processing outcomes under different parameter combinations.
This capability helps optimize the algorithm by guiding the search for the optimal parameter combinations.
(3) Particle swarm algorithm.
The algorithm is a simulation of the behavior of the flock of birds such as foraging, and each particle represents a potential solution, which can be searched on the fly to obtain the best solution.
In the use of the particle swarm algorithm for optimizing CNC machining programs, the parameters in the program can be mapped to the position of a particle.
Each iteration involves updating the particle’s position to find its individual extreme value.
At the same time, the global extreme value of the entire group is considered. By combining both individual and global extreme values, the particle’s speed and position are updated.
This process allows for the search of the optimal combination of machining parameters, ensuring the machining quality requirements are met.
(4) Ant colony algorithm.
The algorithm is to simulate the ant colony foraging behavior, the ants will release pheromone in the path of foraging, and other ants will choose the path with high pheromone concentration to find the shortest path to the food.
The use of the ant colony algorithm in the optimization process of CNC machining programs can be compared to the search for the shortest foraging path problem.
In this analogy, the tool path and machining parameters are likened to ants.
During the continuous exploration and optimization process, more pheromones are released along the paths.
The paths with higher pheromone concentrations will attract more ants, guiding them toward selecting the optimal machining program.
To ensure the effective application of artificial intelligence algorithms, it is crucial to focus on training these algorithms properly.
Additionally, it is important to control the frequency with which the algorithm is used in the background.
Finally, creating a comprehensive and well-structured background database is essential for optimal performance.
Develop program optimization strategies
Use artificial intelligence to innovate algorithms and obtain scientific algorithmic models to further optimize CNC machining programs.
In the tool path optimization, the civil large model can be used for program nesting adjustment.
Verification and Implementation
After optimizing the CNC machining program, it can be integrated into the original CNC machining system. The next step is to perform machining verification to evaluate its effectiveness.
This verification can be done through actual machining or by using simulated machining to ensure the program is usable and accurate.
Upon completion of the above steps, artificial intelligence can be applied to further refine the program.
It effectively identifies and organizes the repetitive logical relationships within the code.
AI can then perform macro program nesting, which helps shorten the overall program content. This also improves the efficiency of program execution.
Artificial intelligence can also check out the statements in the program with infinite loops, and optimize and adjust them according to the needs of the instructions.
Application Case of Artificial Intelligence in CNC Machining Program Optimization
Case Overview
The CNC machine model used in the case is FANUC series Oi-MD, and the CNC machining program is a milling surface program, which is mainly used for undercutting a rectangular aluminum block by 1 mm.
The milling program is optimized using artificial intelligence to produce a new program.
Advantages of the optimized CNC machining program with artificial intelligence
(1) Use of loop structure.
With the support of artificial intelligence, the original program can be further optimized. The reciprocating program is transformed into a while loop structure.
This transformation effectively meets the demand for repeated execution of instructions.
Compared with the original program, it has less redundancy, effectively maintains the program code, and improves the readability of the code.
The application of the while loop in the program helps complete identical machining operations more efficiently.
It eliminates the need to repeatedly paste the same code blocks when changing coordinates. As a result, the program achieves a higher degree of automation.
(2) The variable control method is used to control the relevant parameters in the program.
Variable control is equivalent to a cycle counter, which can adjust the number of cycles and depth of machining according to machining requirements.
This control method is more convenient than the original program control method, simplifies the original program and makes it more readable.
To further optimize the program, it is only necessary to change the values of the variables and adjust the number of cycles.
This approach simplifies the process, even when machining longer workpieces.
The operator does not need to reissue instructions or manually adjust the number of counter cycles.
(3) Combining absolute and incremental modes.
When analyzing the program using artificial intelligence, certain patterns become evident.
The G90 program, which uses absolute coordinate positioning, shows a clear regularity in its structure. However, this also increases the amount of coordinate calculation to a certain extent.
The combined application of absolute and incremental modes can further optimize the original CNC program. This approach simplifies and standardizes the coordinate calculation process.
At the same time, it ensures that the machining progress is maintained without disruption.
(4) Clear logic and easy to understand.
CNC machining program instructions become more understandable after optimization through artificial intelligence.
Each instruction now includes clearly defined parameters and a well-structured logic flow.
This improvement deepens the operator’s understanding of the instructions and makes program execution easier. As a result, it helps avoid errors during the operation process.
Whether it is to change the subsequent program, or add a new program is very simple.
(5) Modular design.
The optimized program adopts a modular design, making it easier for people to understand the artificial intelligence model.
Similar actions are integrated into loop structures, which improves program management. This modular approach also supports future expansion.
To implement changes in the program, it is only necessary to update or modify specific modules, without the need to recreate the entire program.
Trends in Artificial Intelligence
(1) Integration of multi-source data, deep data mining.
With the rapid development of Internet of Things (IoT) technology and the continuous innovation in sensor technology, the amount of data involved in CNC machining programs is expected to grow significantly.
By applying multi-source data fusion technology, it becomes possible to process data from CNC machining operations, machine tool dynamics, and other related sources.
Through in-depth analysis and mining of this data, potential patterns and insights can be uncovered. These insights provide reliable data support for optimizing CNC machining programs.
(2) Innovative artificial intelligence algorithms.
The innovation and research of artificial intelligence algorithms will be the focus of CNC machining program optimization, and the adaptability of artificial intelligence algorithms needs to be further improved.
Optimized adaptive learning algorithms are used so that the model parameters can be adjusted in real time according to the machining process. A variety of algorithms can be effectively integrated to improve computational efficiency.
(3) Strengthen the deep integration of CNC machining programs and artificial intelligence technology.
Give full play to the advantages of intelligent technology and promote the vigorous development of intelligent CNC systems so as to facilitate real-time monitoring of the CNC machining process and realize the automatic optimization of machining parameters and tool paths.
(4) Emphasize the collaborative development of man and machine
Artificial Intelligence can assist the operator to perform the operation, and the operator can interact and make correct decisions according to the intelligent system and interface.
Conclusion
In conclusion, in the optimization process of CNC machining program should give full play to the role of artificial intelligence, which is conducive to improving the operational efficiency of CNC machining program and the stability of CNC machining production.
The optimization of a CNC machining program can not be separated from continuous learning.
A continuous training model is needed to improve the traditional machining program so that it is more advanced.
In the future development process, the application of artificial intelligence technology in optimizing CNC machining procedures will be developed in the direction of multi-source data fusion.
Artificial intelligence algorithms will continue to innovate, and their adaptability will be effectively improved while promoting the collaborative development of CNC machining.