# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 1 Introduction  
Polymer materials are widely used in the interiors and exteriors of automobiles because they are lighter than metallic materials. Their excellent strength and moldability at low temperatures represent additional advantages of polymers, as this reduces production costs and enhances design flexibility [1, 2]. Researchers have developed various techniques for producing components from polymer materials. Adhesive and mechanical fastening are typical bonding methods for polymers, but the recent development of direct thermal energy for joining has reduced costs and provided eco-friendly products. Various joint configurations were compared to distinguish the bonding quality, and the use of chromium interlayers allowed an increase in adhesion forces. Laser beams, ultrasonic waves, andradio frequency (RF) are also commonly used for polymer joining. Joining techniques have improved significantly over the past few decades [1; 2; 3; 4; 5; 6]. A laser is a very versatile, controllable, and easy to use tool for the polymer joining process. In particular, direct thermal energy deposition provides strong joins, and there is little possibility of contamination during the joining process [2; 5]. Furthermore, joining using thermal energy can be easily applied to various thermoplastic polymeric materials during the fabrication of automotive parts, biomedical devices, and electrical devices [2; 3; 4; 5]. The advantages of direct joining include lower costs compared to adhesives and the eco-friendly nature of the technology, as no chemicals are used. Lasers are also a preferred thermal energy source, as they are readily available in the industry, easy to use, and highly precise [3; 4; 5; 6]. Lasers are also less sensitive to materials and can be used for most types of polymer joining, such as joining polymers to other polymers, glass, or other materials. They also have the advantage of good sealing, which produces better esthetic results after joining compared to other joining processes, such as ultrasonic and adhesive joining [2]. In laser joining, the thermal energy imparted by the laser beam is absorbed in the interface between transparent and opaque materials. The top layer is transparent to the laser, while the bottom layer is opaque [2; 7]. As the most absorptive wavelength can be used for laser joining, the joining efficiency can be maximized without damaging the surrounding area. After the energy has been absorbed on the boundary, the joining region is heated to over the glass transition temperature and forged by an external pressing force [8; 9]. In the era of the 4 th industrial revolution, artificial intelligence (AI) technology using deep learning (DL) is being applied in various fields [10; 11; 12; 13; 14; 15; 16; 17; 18]. A computer vision-based DL algorithm has been used for autonomous vehicle driving, games, and medical applications. Recently, this technology has also been used to improve the production quality in manufacturing applications [11; 12; 13; 14]. In DL for product quality monitoring, the greater the amount of correctly classified training data, the more accurate the results are. A system for monitoring porosity in laser welding processes was developed using a DL algorithm [10], and real-time weld pool data were collected. DL was also used to predict the hardness distribution with the laser heat treatment of AISI H13 tool steel [11]. During the fabrication of sensor systems using laser welding processes, DL-based computer vision systems have been used to capture signals indicating the welding status [10]. A monitoring system to determine the degree of penetration during laser welding has also been introduced [10]. Convolution neural networks (CNNs) are the most commonly used networks for image-based DL. CNNs, as multilayer perceptrons, have layers in which each neuron is connected to all the neurons in the next layer. CNNs analyze hierarchical patterns in data and then generate smaller, simpler patterns.