**1. Introduction**

The use of artificial intelligence and specifically artificial neural networks (ANNs) has allowed yielding revolutionary advances in manufacturing. However, most of the applications of artificial intelligence in the production field concerned expert systems and fewer attentions were paid to neural networks (NNs). Most important characteristics of the ANNs are:

• the self-adaptive behaviour that allows to adapt the forecast to changing of the environment, in this way improve the networks' ability to learn and to predict;

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• the parallel computing architecture, that has a great impact in multiple disciplines and applications, from speech and natural language processing, to image processing or problems in bioinformatics and biomedical engineering.

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


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

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 developments in manufacturing application areas.

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

**Figure 1.** Functionality of the artificial neural networks and their manufacturing applications.

ANNs for the monitoring, control and optimization of a welding processes. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical properties of the welded joints. Finally, an evaluation of the time-cost parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and benefits of the prediction model adopted.
