**1. Introduction**

The advent of Industry 4.0 has brought significant enhancements to manufacturing processes, which are now based on smart and autonomous systems, and are incorporated with data and machine learning (ML) [1]. The enhancements to smart manufacturing processes, especially concerning welding technology, must be extended and established, in the context of modern manufacturing [2]. Among various welding technologies, laser beam welding (LBW) has a higher precision, productivity, flexibility, effectiveness, and numerous other advantages, which include a deeper penetration, higher welding speed, and lower distortion, compared to other welding technologies, owing to the properties of the power sources in LBW [3–6]. In addition, the LBW is a suitable joining technology for welding dissimilar metals and advanced materials such as NiTi alloy [7–9]. The LBW presents a substantial potential for manufacturing applications [10], and more study on the LBW process is required to increase the benefits of the LBW. In particular, there seems to be a need of research in the field of monitoring and controlling the welding process and weld quality, which is attributed to the complexity of LBW systems and the characteristics of the LBW process. Considering that the technologies in this field are essential to develop a smart manufacturing system for LBW, extensive research must be carried out to achieve this objective.

The monitoring of a process, and consequently the quality assessment, can be divided into three categories: pre-process, in-process, and post-process [11]. The pre-process monitoring concerns the weld seam tracking before welding or ahead of the laser heat source. The in-process monitoring focuses on the monitoring of the phenomena on the welded zone during welding, such as the keyhole's shape stability. The post-process monitoring, generally, is concerned with the weld defect detection, and the measurement of the form of the weld seam, after welding, or behind the weld pool. In the pre-process and post-process monitoring methods, ultrasonic and camera-based techniques have been dominantly used. In the case of in-process monitoring, a wider range of measuring methods, including optical (ultraviolet, visual, infrared) and acoustic detectors, X-ray radiography, and camera-based methods, have been used to adequately deal with welding phenomena generated by the high-energy-density laser.

According to Stavridis et al. [10], the in-process quality assessment, i.e., in-process monitoring of LBW, can be further sub-divided into four parts, based on the monitoring techniques used, such as image processing, acoustic emission, X-ray radiography, or optical signal techniques. Vision and thermal images are mainly used in image processing techniques to observe the weld pool [12], keyhole [13], plume, and spatters [14]. The acoustic emission techniques have been applied for the measurement of the melting, vaporization, plasma generation, keyhole formation [15], and propagation of cracks [16], and the X-ray techniques have been used to observe the welding phenomena and defects, including slag inclusions, blow holes, incomplete penetration, and undercuts [17]. In the case of optical signal techniques, they use vision systems (charge-coupled device and complementary metal–oxide–semiconductor cameras), photodiodes [18], and spectrometers (which are highly related with the topic of the study).

Sibillano et al. [19] studied the dynamics of the plasma plume, produced in LBW of 5083 aluminum alloy, with the help of correlation spectroscopy, and presented the results of the influence of welding speed on the loss of alloying elements. Rizzi et al. [20] investigated the spectroscopic signals, produced by the laser-induced plasma optical emission, together with energetic and metallographic analyses of CO2 laser-welded stainless-steel lap joint, using the response surface methodology (RSM). This statistical approach allowed the study of the influence of the laser beam power and laser welding speed, on the plasma plume electron temperature, joint penetration depth, and melted area. Konuk et al. [21] used a spectrometer to collect the optical emissions of the welding area, and calculate the electron temperature, and the data measured and calculated were used to determine the weld quality and to control the laser power. Sebestova et al. [22] designed a sensor to monitor the pulsed Nd:YAG laser welding process, based on the measurement of the plasma electron temperature, and this sensor was used to detect the weld penetration depth. Zaeh and Huber [23] investigated the radiation emission of the laser-induced plume during LBW of aluminum and steel alloys, and they developed a system to control the chemical composition of the melt pool, during the active welding process. Chen et al. [24] proposed a spectroscopic method based on a support vector machine (SVM) and artificial neural network (ANN) for the detection and classification of fiber laser welding defects. The spectral data captured by the spectrometer was processed by selecting sensitive emission lines and extracting features of the evolution of the spectral data; the SVM and ANN models were designed based on the processed spectral data. It has been verified that these two models were quite effective in detecting and classifying the weld defects. Zhang et al. [25] designed a multiple-sensor system that includes an auxiliary illumination visual sensor system, an ultraviolet- and a visible-band visual sensor system, a spectrometer, and two photodiodes to capture the real-time welding signal and determine the laser welding quality. A deep learning framework based on the stacked sparse autoencoder (SSAE) was established to model the relationship between the multi-sensor features and their corresponding welding statuses; a genetic algorithm (GA) was used to optimize the parameters of the SSAE framework. Lee et al. [26] developed an in-situ monitoring system using a spectrometer for laser welding on galvanized steel. They applied Fisher's criterion to rank several features for extraction of the most valuable features, and then used the K-nearest neighbors and SVM algorithms to classify welding conditions related to the welding defects. Although some studies have been carried out to investigate weld quality assessment using a spectrometer in LBW process, till date, little attention has been paid to the quality assessment methodology, in LBW, based on the spectrometer and ML algorithms.

This study presents a deep neural network (DNN)-based quality assessment method in spectrometry-based LBW. We designed a spectrometer, that can measure and analyze the light reflected from the welding area in LBW process. The weld quality of LBW was classified through welding experiments, and the spectral data were analyzed using the spectrometer, according to the welding conditions and weld quality classes. Subsequently, the spectral data were converted to CIE 1931 RGB values, to standardize and simplify the spectral data. A weld quality prediction model was designed based on DNN, and the DNN model was trained using the experimental data. The weld-quality prediction accuracy of the model was calculated.

The remainder of the paper is organized as follows. Section 2 describes the experimental procedure and setup. The obtained results are discussed in Section 3, and Section 4 concludes the paper with a brief summary.
