**2.3. Arc welding processes**

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.

• Robot scheduling Yih et al. [59]

Author Ref. num. Moon and Chi [53]

[54] Lee et al. [55] Moon [56] Wu [57]

Wu [57]

Kaparthi and Suresh

Cook and Shannon [58]

Chryssolouris et al. [60]

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

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

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.

**2.1. Injection moulding processes**

**Category Manufacturing**

204 Advanced Applications for Artificial Neural Networks

Application topic • Manufacturing system

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

design

control

decision

• Manufacturing process

• Manufacturing operational

**2.2. Gas metal arc welding processes**

metal as shown in **Figure 2**.

dynamics and based on this to predict the part quality [3–9].

The complexity of the relationship between the process variable and weld quality is the common factor in all manufacturing processes and in particular arc welding processes. For these reasons, the literature documents with some interesting researches, the use of ANNs for quality monitoring and control of the process. In this type of welding process, the ANN input data are generally the surface temperature, the welding voltage and the current and torch speed. The majority type of ANN employed for this case was, again, the multilayer perceptron [10, 13–21]. Their use was found to be very satisfactory to predict the weld defects, the geometry such as bead width, head height and penetration.

#### **2.4. Machining processes**

To perform a correct quality control in all machining systems, it is very important to check particular parameters such as the cutting tool state, the vibrations, the forces and the temperature obtained during real-time machining operation. To optimize this particular type of supervision of the cutting tool state, some authors document in literature the use of the ANNs which used the above-mentioned process data to classify the status of tool wear, prediction tool life and detect tool failure in an on-line manner. Examples of typical sensors are tool dynamometers, acoustic emission sensor accelerometers and thermocouples. In this process, the networks in frequent use are the multilayer perceptron and Kohonen [22–46].

#### **2.5. Semiconductor manufacturing processes**

The complexity of plasma etching processes in integrated circuits fabrication promoted the use of ANNs for monitoring and control. In this field, the use of artificial intelligence brings advantages that could not be achieved with traditional open loop controls. Where used the multilayer perceptron networks that are the most popular for this process. The ANNs use the fundamental parameters that affecting process dynamics, such as power, gas flow rate, dc bias voltage and throttle positioning. Proper use of networks in this field allows real-time monitoring and estimation of quality variables such as the etching thickness and the etching time [47–52].
