**2. Manufacturing applications**

• the parallel computing architecture, that has a great impact in multiple disciplines and applications, from speech and natural language processing, to image processing or prob-

Therefore, they could be of great help for today's computer integrated manufacturing and in smart factories, according to Industry 4.0 paradigm. Currently, the nature of the manufacturing process is changing with great speed, becoming more sophisticated and continuous variations are occurring due to changes in customer demand and reduced product life cycle. This requires manufacturing technologies that can easily adapt to such changes. In this context, artificial neural networks are a powerful technology to solve this problem. The use of ANNs is also widely used for process monitoring and control applications. The quality of a process can only be provided by in process monitoring through proper measurements. To ensure a

• Correct any process operations as a result of any anomalies detected from the comparison

These steps should be followed with minimal supervision and assistance from operators, if possible in unmanned manner. In addition, all processes should be implemented with special features such as: storing information, decision making, learning and integration. It should be noted that most manufacturing processes are regulated by many variable parameters and for

This may be attributable to the fact that they are exposed to external disturbance and noise and often subjected to parameter variations. Furthermore, there is often a great interaction between variables and therefore it is not possible to properly define the final quality of the product and the variables that influence it. Due to these characteristics the quality often varies from product to product, impairing its uniformity and decreasing the yield of the product. So, all changes that may occur in manufacturing environments cannot be easily observed by an operator, so in recent years the use of neural networks applied to process monitoring and control has been of great interest. Indeed, it has been shown that the use of artificial intelligence can overcome the above-mentioned problems [2]. The research efforts in this direction will be accelerated with greater interest in the future and will lead to the development of truly intelligent manufacturing systems that are capable of producing products without the supervision or assistance of human operators [1]. **Figure 1** classifies the functionalities needed to imbed the artificial neural networks on manufacturing processes and summarizes the current

The real world applications here in manufacturing include the modelling, monitoring and control, identification, planning and scheduling associated with the processes. The purpose of this chapter is to present some applications of artificial neural networks in manufacturing process monitoring and control, among which particular attention will be paid to the study that has been published in international journals and that has investigated the use of the

high quality of a process, you must follow the following technological steps [1]:

this reason, such systems have a random, complex and uncertain nature.

lems in bioinformatics and biomedical engineering.

202 Advanced Applications for Artificial Neural Networks

• Identify the characteristic changes of a process;

• To estimate the changes of the product quality;

between the obtained and the desired quality.

developments in manufacturing application areas.

Due to many external disturbances and many variations in process parameters, many production processes are complex and time-consuming. For these reasons, it is not always possible to identify the relationship between the product quality and the input variables of the process. Thus, there is interest to integrate the artificial intelligence into the production processes for storing, learning, reasoning and decision making. Such systems are able to adapt to changes in its environment and can truly realize unmanned operations of processes. The adoption of the neural network can be devoted to monitoring and to prediction of different parameters in many industrial areas, in order to solve issue relating to the manufacturing system design, process planning, as well as operational decision making. A summary of main NN applications field is shown in **Table 1**.


**Table 1.** Summary of neural network manufacturing applications.

Manufacturing information, such as the sequence of operations, lot size; multiple process plans were given special consideration in their approach to solve the generalized part family formation problem. Many authors also point out that the method of artificial neural networks is flexible and can be efficiently integrated with other manufacturing functions. Below, from the literature, some important artificial intelligence applications to particular production processes are described.

#### **2.1. Injection moulding processes**

Injection moulding processes are characterized by dynamic characteristics since process input variables are the melting temperatures, the velocity of the cylinder, the holding, the pressure that produces the polymer flow into the model cavity and they vary in a complex manner. The phenomena occurring in the process are very complex, time-varying, nonlinear and uncertain. This complexity makes it difficult to relate the input operating variables to the product quality such as geometry accuracy and geometry surface smoothness. These processes have been implemented and optimized with the use of artificial intelligence with the use of *multilayer perceptron* which is found to be the most popular network and tries to model the process dynamics and based on this to predict the part quality [3–9].

#### **2.2. Gas metal arc welding processes**

In gas metal arc (GMA) welding processes [10–12], the flow of an electric current is generated by an electric arc that is maintained between the consumable wire electrode and the welding metal as shown in **Figure 2**.

Both the filling metal and the consumable electrode are automatically fed by a wire feeding device. A good quality of the welds is determined by the relatively high depth to width ratio of the molten welding pool. So, the monitoring and control of weld geometry and the surface temperatures that are strongly related with the formation of the weld pool, are very important for the penetration depth or the back bead width. For this purpose, the temperatures by noncontact were measured. Infrared temperature sensing system and recent studies conducted the ANN multilayer perceptron to detect and control with great success all the surface temperature information.

**Figure 2.** Characteristics of the GMA welding process.
