With the rapid development of science and technology and the rapid transformation of the global manufacturing industry, modern manufacturing technology and equipment play a pivotal role in the global context. Mechanical automation technology as an important part of the modern manufacturing field, its advantages in improving production efficiency, cost reduction, product quality and other aspects of the guarantee is becoming more and more prominent, in this context, quality control as a critical link in the manufacturing process, to ensure that the product meets the specifications to meet customer demand and enhance the competitiveness of enterprises has an unignorable role.
1. Difficulties in quality control of mechanical automation technology
1.1 Difficulties in human-machine collaboration
Automation systems usually require collaborative work with operators, in which case the possibility of human error in the production process is increased if the problem of human-machine interaction cannot be solved to ensure the safety of the system in collaboration with human operators. Taking the use of mechanical automation technology for CNC machine tools as an example, in terms of the design of the operating interface, the operating interface of CNC machine tools is usually relatively complex and contains a large number of parameters and functional options.
If the operating interface is not reasonably designed, operators may have difficulty in understanding and correctly using the system, which increases the probability of errors, for example, the occurrence of miscalculation of parameter settings or incorrectly executed machining instructions.
1.2 Data security and privacy are not protected
Machine automation technology involves a large number of sensors and control systems for collecting and transmitting data during the production process, which may contain detailed information on product design, manufacturing and testing, so the generation and transmission of large-scale data increases the risk of data leakage and unauthorized access. Typical manifestations are:
First, in terms of cybersecurity threats, system units based on automation systems connected to the network are exposed to threats from cyber hackers and malware, where outsiders can hack into the system to gain access to confidential information, tamper with production data, or sabotage automation equipment, a threat that poses a potential threat to the accuracy and reliability of quality control.
Secondly, in terms of production privacy, in some production environments, employees’ personal information or production data usually need to be included in the quality control system, but in the way of the automation technology system of some enterprises is not perfect, to ensure that the data is not tampered with or leaked during the transmission process in the supply chain, as well as correctly used in multiple links, has become a complex and important challenge in the automated production of machinery.
1.3 Problems with data communication and fusion
In the era of highly digitalized manufacturing, the huge amount of data generated by mechanical automation systems requires effective communication and fusion between different devices and systems to support decision making, quality control, and real-time optimization of the production process.
However, data communication and fusion is not smooth sailing in machine automation technology, and its operation process faces the dual pressures of diverse data sources, different communication standards and formats, and real-time and security.
For example, inconsistent data formats and standards are usually one of the difficulties in executing commands in machine automation technology. In an automation system, as the host needs to synchronize the control of devices and sensors from different vendors, which correspond to or use different data formats and communication.
For example, inconsistency in data formats and standards is usually one of the difficulties in executing instructions in machine automation technology. In an automation system, since the host computer needs to synchronize and control devices and sensors from different vendors that correspond to or use different data formats and communication standards, this inconsistency complicates data integration and analysis, making the production process require additional processing steps to solve the data fusion problems, increasing the complexity and development cost of the system.
2. Advantages of the application of machinery automation technology
2.1 Automatic inspection technology improves the quality of automatic production products
In the field of mechanical automation, automatic detection technology is a key factor in improving product quality, reducing production costs and improving production efficiency. Automatic detection technology utilizes a variety of sensors, image processing, data analysis and other advanced technologies to carry out real-time monitoring and detection of products in the production process to ensure that the products meet the quality standards.
Typical performances include: in the identification of mechanical production modules, using image processing and machine learning technologies, the production equipment can identify and compare the product appearance, size, color, etc. with high precision, which helps to detect surface defects, foreign objects, shape deviation and other issues to ensure that the appearance and size of the product meets the standards.
In addition, the use of optical inspection systems, intelligent sensor networks, automated defect recognition and other technologies is conducive to the realization of comprehensive automated control of the entire production process, and improve the quality of product production.
2.2 Machine learning to improve production accuracy
The combination of mechanical automation technology and machine learning for the manufacturing industry to bring higher production accuracy, and performance of machine learning algorithms can monitor the production process in real time, according to the results of real-time adjustments and optimization, to ensure that the production parameters are in the best state, and improve the accuracy of production.
For example, an industrial product manufacturer is responsible for production through the use of mechanical automation technology, in which the automatic numerical control system has a certain degree of automatic learning ability, according to the standard illustration of the production parts, automatic identification of defective products in the production of the machine, and directly rejected.
From the viewpoint of the machine learning principle of the system, the automatic detection model in the system is based on the RF Random Forest model to identify the parameters of the production parts in the machine production, and the designer will synchronize the design of the regularization parameter S with the number of identifiers of the “tree”, so that the system automatically extracts the number of model groups of the production products, and ultimately estimates the number of model values of each product parameter by using the MCC model algorithm. Finally, the MCC model algorithm is used to estimate the performance corresponding to the model value of each product parameter, and the resulting data can be used to assess the quality level of the production product, as shown in equation (1).
Where, denotes the model parameter values; denotes the number of segments to judge the product signal quality category; denotes the number of segments incorrectly assigned to the product signal quality category.
2.3 Automated production mitigates human factor interference
In modern manufacturing, human factors may lead to errors in production, whereas automated systems are able to perform tasks with a high degree of accuracy and consistency, and mechanical equipment and automated systems perform tasks in a manner that is usually unaffected by fatigue, distraction, or other human factors, and are able to do so on a continuous basis.
For example, a photocopier production plant in the production of parts, the choice of mechanical automation technology to produce “ef – row fan” parts, in order to avoid the previous production work, usually due to operator fatigue, forgetfulness and other factors leading to the production of mechanical parts failure, the enterprise to implement standardization and Simplification of the production principle, the design and mechanical automation technology as the core of the QC tool, the tool can be in the production line for each component one by one quality comparison, shape comparison, and the existence of production abnormalities in the parts to mark the use of ringing the way to prompt the operator to reassemble, significantly reduce the impact of human factors on the enterprise manufacturing production process.
3. Mechanical automation technology application points
3.1 Coordinate the functional relationship between mechanical structure and automation system.
The main points of the application of mechanical automation technology mainly involves coordinating the functional relationship between the mechanical structure and the automation system to ensure the efficient operation of the system and achieve the expected production goals, such as the technical design of the system integration method, to ensure that the mechanical structure and the automation system between the effective communication protocols, standard interfaces to realize the close interconnection, as well as seamless connection between the different subsystems to achieve the transfer of data and instructions.
So it is crucial to harmonize the functional relationship between the mechanical structure and the automation system.
In this process, the enterprise can take the work approach includes according to the modern manufacturing industry in the operation of mechanical equipment, the use of mechanical automation technology to build the corresponding automation control system, and the control system in the various functional units connected to the mechanical equipment of the various operating port, in order to facilitate the use of automation system functional units to realize the mechanical control.
As shown in Figure 1, the figure demonstrates the spectrometer transmission structure in the modern manufacturing industry, under the application of mechanical automation technology, the transmission structure of the A point can be individually controlled using the EWMA control algorithm, by the group operating table to provide operating instructions to the A point of the functional unit to deliver control instructions, so that the structure can be in accordance with the function of the instructions to run in an orderly manner, and make the operation of the B, C, D and other points to be able to Form an R2 type batch control structure framework to improve the application effect of machine automation technology.
Fig. 1 Spectrometer transmission structure
3.2 Optimize the design of mechanical automation controller
Optimizing the design of mechanical automation controller can improve the stability and control accuracy of the system, while if the control system is equipped with more accurate monitoring and response to the system state, it can reduce the vibration and fluctuation of the system, and improve the consistency and product quality in the production process.
And under the application of mechanical automation control technology, the relevant departments can consider the development of prediction-based control function units in the controller design so that the control system can perform a series of operations such as controlling the equipment and processing operation in a timely manner according to the processing instructions of the model.
As shown in equation (2), the control method is based on the RF model to create a model of the control instructions of the automation controller, when the control system recognizes the existence of a new level of factors in the control instructions, the system will be through the training data to form a completely new copy of the modifications, modifications to the copy of the actual variables that can be involved in the production of the product factors and information, and ultimately the inspection of a number of different specifications or design parameters of the manufacturing product Control.
Assuming that the production of a new product involves the production of n parts, the predictive control unit is able to update the training database content, access to multiple parts of the production and design information, and in accordance with the model provides the optimal parameter prediction value to check whether the parts are damaged, the size of the unqualified and so on, so that the modern manufacturing industry, production quality and efficiency has increased dramatically. The prediction-based automation controller design model is shown in equation (2).
Where Uk+1 represents the optimal parameter prediction value calculated under the model; B represents the number of trees; f(x+1) represents the average response value of the terminal node corresponding to the unseen sample; and b represents the number of factors.
3.3 Modeling of algorithmic parameters for optimizing automated control systems
Automation control systems utilize various algorithms to achieve intelligent system control. Different types of systems and application areas may necessitate different control algorithms. For instance, PID control algorithms are suitable for a broad spectrum of systems, ranging from simple household temperature control to complex industrial process control. MPC algorithms, on the other hand, use a dynamic model of the system to predict its future behavior, making them applicable to chemical processes and mechanical systems where future moments need to be considered. The selection of algorithms is typically based on the nature of the system, its requirements, and application scenarios, in order to achieve effective automated control.
And in order to improve the operational efficiency of modern manufacturing and equipment, the relevant departments should properly select the parameters and modeling of the algorithms based on mechanical automation control technology in the following ways:
First of all, considering that current manufacturing product process plans often require multiple pieces of equipment to operate simultaneously, with each piece of equipment corresponding to its respective automatic control module for sequential processing, the design of mechanical automation technology can incorporate the MPC algorithm, utilizing midpoint measurements across various control modules to fulfill the functional requirements of the automation control system in this application scenario.
Secondly, when designing the integrated measurement module, it is important to consider that the control surface of the mechanical automation system must cover the entire production space. Therefore, during production, relevant departments should select a detection algorithm capable of assessing process qualification, enabling integrated intervention and guiding the operation of mechanized control to minimize the likelihood of process changes, loss of parameter control, and manual intervention in today’s manufacturing industry.
3.4 Optimize control system design
Optimizing the design of the mechanical automation control system is conducive to the improvement of system performance, efficiency, safety, maintainability and other aspects of performance, such as the designer through the entire mechanical automation system to model and analyze in detail, so that it understands the physical structure of the system, the principle of operation, sensors and actuators connection relationship, etc., which helps to determine the needs and objectives of the control system.
First of all, in the simulation design of the mechanical automation system, one can use differential equations and other mathematical tools as the basis for calculation to describe the dynamics and operation of the system. This allows for the construction of a scientific and reasonable framework for system operation, energy conversion, and physical phenomena.
On this basis, the design of the mechanical automation control system can be based on the system framework diagram to represent the relationship between the control system and the production equipment, through the simulation test to make the two form an organic unity, to ensure that the equipment can be in the mechanical automation control technology under the role of normal operation.
Secondly, when selecting sensors and actuators, the designer needs to consider the physical quantities obtained from measuring the mechanical control equipment (such as equipment displacement, speed, and shaft transmission structure). Based on these measurements, the designer should choose sensor equipment that meets the application requirements. Additionally, while ensuring the accuracy and resolution of the sensor, the designer should utilize system design algorithms to minimize the sensor’s response time, enabling direct control of machinery through the operating platform. This ensures the feasibility of applying mechanical automation technology.
Conclusion
In conclusion, the intensification of market competition and consumer demand for continuous improvement of product quality, resulting in the traditional quality control methods have been difficult to meet the needs of modern manufacturing, while the wide application of mechanical automation technology provides new possibilities and opportunities for quality control.
In the future, the relevant enterprises need to pay attention to the problems that may arise in the process of quality control of mechanical automation technology, and put forward some suggestions for improvement and innovation, and comprehensively analyze the challenges and future development trends, so as to provide a guarantee for the sustainable development of modern manufacturing industry.
