### 1. Introduction

_{.}However, this has a drawback in that the amount of computation increases as the number of samples increases, and the computation time becomes longer.

### 2. Experiments

### 2.1 Materials

### 2.2 Welding Equipment and Process

_{2}, and the gaps between the workpieces were fixed at 0.0 mm and 0.5 mm. Also, an ER70S-3 level wire with a diameter of 1.2 mm was used, and the experiments were conducted at a wire feed rate of 4 m/min. For reliability, the experiments were repeated two times under each set of conditions.

### 3. Genetic Algorithm

### 3.1 Genetic Algorithm Optimization Technique

*P*is the set of chromosomes for the ith generation.

_{i}*n*is the total number of chromosomes (population).

*P*is the jth chromosome of the ith generation. The genetic information of the first chromosome of the first generation (

_{i,j}*p*

_{1,1}) is shown in Eq. (2).

*q*is the values of the genetic information of each chromosome. m is the number of types of genetic information of each chromosome.

*q*is the kth genetic information value of

_{i,j,k}*P*, which is the jth chromosome in the ith generation. Generally, the genetic information value q is given minimum and maximum value limits and assigned a random value in the initial generation.

_{i,j}*P*

_{1}, which has chromosomes that were given genetic information values.

*f*is the fitness function, and the genetic algorithm designer must configure it personally. If one or more of the chromosomes that have been evaluated by the appropriate function

*f*has a score that is greater than or equal to the expected appropriate value, it becomes the optimal solution, and if not, the next generation is generated using the first generation chromosomes which were given scores. Roulette wheel selection was used, which is a method that probabilistically selects two chromosomes with a probability that increases as their fitness scores increase. When the first generation’s a

^{th}(

*p*

_{1,a}) and b

^{th}(

*p*

_{1,b}) chromosomes are selected as shown in Eqs. (4) and (5) below, the crossover operation is performed.

*p*

_{2,1}, and it is shown in Eq. (6).

*p*

_{2,1}, which is the kth gene value of

*q*

_{2,1,k}that was produced by arithmetic crossover.

*α*has a random value between 0 and 1. That is,

_{k}*q*

_{2,1,k}, which is the kth gene value of the descendent

*p*

_{2,1}, has a random value between the kth gene value of the a

^{th}chromosome in the first generation and the kth gene value in the b

^{th}chromosome in the first generation. After the crossover operation is performed in this way, a mutation operation is performed to generate the second generation of chromosomes by changing gene values randomly with certain probabilities. Then, the fitness function

*f*is used to perform evaluation again and determine whether a subsequent generation will be generated.

### 3.2 Selecting Deep Learning Hyperparameters

##### Table 3

Hyper parameters from previous research11) | |
---|---|

Number of hidden layer | 4 |

Number of node in each layer | 24, 24, 24, 24 |

Learning rate | 0.01 |

Batch size | 32 |

Dropout rate | 0.5 |

### 3.3 Genetic Algorithm-based Optimization

### 4. Results and Observations

### 4.1 Signal Analysis and Feature Extraction

*V*and

_{p}*V*are the maximum and minimum voltage data values during the 0.1-second, respectively.

_{s}*s[V]*is the standard deviation for the 0.1-second of data.

*s[V*is the standard deviation of the voltage data during 5 instantaneous short circuits (orange circles).

_{s}]*s[V*is the standard deviation of the voltage data during 5 peaks (blue circles).

_{p}]*s [T*] is the standard deviation of the arc periods within 0.1-second.

_{a}*s [V (T*)]and

_{s}*s [V (T*)]are the standard deviation values of voltages measured during the short circuit periods and the arc periods, respectively. Table 4 shows each of these variables.

_{a}##### Table 4

### 4.2 Results of Optimization Using a Genetic Algorithm

##### Table 5

Variables | Value |
---|---|

Initial number of chromosomes | 256 |

Minimum number of chromosomes | 64 |

Maximum generation | 15 |

mutation probability | 15 % |

##### Table 6

##### Table 7

##### Table 8

Total test data | True | Error | accuracy | |
---|---|---|---|---|

DNN structure from previous research | 361 | 323 | 38 | 89.4737 |

Optimized GA- DNN structure | 336 | 25 | 93.0748 |

### 4.3 System for Real-time Use After Optimization

### 5. Conclusions

1) Feature variables were derived by measuring the arc voltage waveforms that occur during the GMAW process in real-time and performing data preprocessing.

2) The new genetic algorithm that was proposed by this study reduced calculation time by around 30% compared to before and increased the number of first- generation chromosomes, thus improving the diversity of the initial chromosomes.

3) The results of verifying the optimization results showed that the DNN model that was optimized by the genetic algorithm had a prediction accuracy of 93.1%, which was an increase of 3.60% compared to a previous study’s DNN model prediction rate.