Modern manufacturing technology and equipment

-Quality Control Analysis of Mechanical Automation Technology

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 increasingly prominent, in this context, quality control as a critical link in the manufacturing process, to ensure that the product complies with the specifications, to meet customer needs and enhance the competitiveness of enterprises has an unignorable role.

Difficulties in quality control of mechanical automation technology

1.Difficulties in human-machine collaboration

Automation systems usually need to work in collaboration 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, increasing the probability of errors, for example, the occurrence of miscalculation of parameter settings or incorrectly executed machining instructions.

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 from 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 unauthorised access. Typical manifestations are:

Firstly, 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 some enterprises’ automation technology system 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 being used correctly in multiple segments, has become a complex and important challenge in the automated production of machinery.

3.Problems with data communication and fusion

In the era of highly digitalised manufacturing, the huge amount of data generated by machinery automation systems requires effective communication and fusion between different devices and systems to support decision-making, quality control and real-time optimisation of the production process.

However, data communication and fusion is not smooth sailing in machine automation technology, and its operation faces the dual pressures of diverse data sources, different communication standards and formats, and real-time and security.

For example, inconsistency in data formats and standards is usually one of the difficulties in executing commands in machine automation technology. In an automation system, as the host needs to synchronise the control of devices and sensors from different vendors that correspond to or use different data formats and communication standards, this inconsistency complicates the integration and analysis of data, and makes the production process require additional processing steps to solve the data fusion problems, increasing system complexity and development costs.

Advantages of the application of machinery automation technology

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 uses 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 is able to identify and compare the product appearance, size, colour, etc. with high precision, which helps to detect problems such as surface defects, foreign objects, shape deviations, etc., and ensures that the appearance and size of the products comply with the standards.

In addition, the use of optical inspection systems, intelligent sensor networks, automated defect recognition and other technologies is conducive to achieving comprehensive automated control of the entire production process and improving product production quality.

2.Machine learning to improve production accuracy

The combination of mechanical automation technology and machine learning for the manufacturing industry has brought higher production accuracy, and is manifested in the machine learning algorithms can monitor the production process in real time, and its real-time adjustment and optimisation according to the detection results, to ensure that the production parameters are in an optimal state, and improve the production accuracy.

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 synchronise the design of the regularization parameter S with the number of the ‘tree’ of the identifier, and the system automatically extracts the number of groups of models of the production products, and finally estimates the number of model values corresponding to each product parameter by using the MCC model algorithm.

The algorithm model is shown in Equation (1), in which the model parameter values are represented; the data can be used to evaluate the quality level of the product.

Where, denotes the model parameter value; 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.

3.Automated production to mitigate 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 the standardisation 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.

Mechanical automation technology application points

1.Coordination of mechanical structure and automation system function relationship

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 desired 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 achieve a close interconnection, as well as seamless connection between the different sub-systems, in order to achieve the transfer of data and instructions. , so it is vital to co-ordinate the functional relationship between the mechanical structure and the automation system.

In this process, enterprises can take the work approach includes the use of mechanical automation technology to build the corresponding automation control system according to the operating conditions of the mechanical equipment used in the modern manufacturing industry, and the control system in the various functional units connected to the various operating ports of the machinery and equipment in order to facilitate the use of automation systems to achieve the mechanical control of the functional units.

As shown in Figure 1, the figure demonstrates the spectrometer transmission structure in the modern manufacturing industry, in 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 functional instructions for the orderly operation of the structure, and make the operation of the B, C, D and so on points to be able to The operation of points B, C and D can form a batch control structure framework of R2 type, which improves the application effect of machine automation technology.

C, D and so on points to be able to The operation of points B, C and D can form a batch control structure framework of R2 type, which improves the application effect of machine automation technology.

Fig. 1 Spectrometer transmission structure

2.Optimise the design of the mechanical automation controller

Optimising the design of the mechanical automation controller can improve the stability and control accuracy of the system, and if the control system has a more accurate monitoring and response to the system state, it can reduce the vibration and fluctuation of the system, and improve the consistency of the production process and product quality.

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 identifies 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 products 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 been greatly improved. 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.Modelling of Algorithmic Parameters for Optimal Automated Control Systems

Automation control systems use a variety of algorithms to achieve intelligent control of the system, different types of systems and application areas may require different control algorithms, such as PID control algorithms are suitable for a wide range of systems, such as simple household temperature control to complex industrial process control; MPC algorithms use a dynamic model of the system to predict the future behaviour of the system is applicable to the need to take into account the future moments of the chemical process and the Mechanical systems, the algorithms are usually selected 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 modelling of the algorithms based on mechanical automation control technology in the following ways:

First of all, taking into account the current manufacturing product process plan usually need more than one piece of equipment running at the same time, each piece of equipment corresponds to the corresponding automatic control module for sequential processing, so the mechanical automation technology design can be used MPC algorithms, the midpoint measurement of the various control modules, to meet the application of this application scenario on the automation control system’s functional requirements.

Secondly, in the design of the integrated measurement module, taking into account the control surface of the mechanical automation system needs to cover all the production space, therefore, in the production, the relevant departments should choose the detection algorithm that can be used to measure whether the process is qualified to carry out integrated intervention and guide the operation of the mechanised control to reduce the possibility of process changes, parameter loss of control and manual intervention in the manufacturing industry now.

4.Optimise 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 analyse in detail, so that it understands the physical structure of the system, the principle of operation, the connecting relationship between sensors and actuators, 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, differential equations, differential equations and so on can be used as the basis of calculation in the system design to describe the dynamics and operation of the mechanical automation system, so as to constitute a scientific and reasonable framework for the operation of the system, the framework for the energy conversion and the framework for the 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, in the selection of sensors and actuators, the designer needs to be based on the physical quantities obtained in the measurement of mechanical control equipment (including equipment displacement, equipment speed and shaft transmission structure, etc.), select and match the application requirements of the sensor equipment, in order to protect the sensor accuracy and resolution on the basis of the use of system design algorithms to reduce the response time of the sensor, so that the sensor can be used to control the machinery directly. Operating platform, so that the feasibility of the application of mechanical automation technology to be guaranteed.

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 analyse the challenges and future development trends, so as to provide a guarantee for the sustainable development of modern manufacturing industry.