## 1 Introduction  
All devices utilize a PCB board to function and incorporate various electronic devices. One of these components is a board-to-board connector used to connect two wires or two circuits robustly. Figure 1 illustrates a surface-mounted board-to-board connector with 20 \(\times\) 2 pins. Electric connectors use in various applications, such as mobile devices, tablets, and transportation [1]. Furthermore, experts assess the importance of automation in the assembly line in the industrial sector according to four main points: (1) increasing production rate, (2) enhancing production speed, (3) optimizing production, (4) assisting human tasks, and (5) improving cost-effectiveness.  
Due to design complexity, fine pitch 0.4 mm board-to-board connectors are assembled manually in the production units. The industry's most common challenges faced in the assembly line are aligning male and female pairs and fast-mating the connectors because of the complex design. In addition, performing the mating process without damage is one of the biggest challenges because humans cannot perceive the correct amount of force applied. Sometimes, this leads to a fault or damage to the pins in the connectors. In addition, the board-to-board connector assembly relies on a faintly audible clicking sound made by the connector during successful mating. However, it is not a reliable method to determine whether the process is a successful insertion. In addition to these challenges, there are various other constraints such as (1) the position of the male and female board-to-board connectors on the device, (2) obstacles surrounding the connector, (3) the size, and (4) the number of pins, as this increases the complexity of the mating process.  
Due to the utilization of different types of connectors for other purposes and variations in design, these constraints may vary and affect the adopted method. The model-based approach has limitations due to the system's complexity, tolerance range, and size. In addition, there is less research available that addresses the challenge of fine-pitch connector insertion. To the best of our knowledge, no study has been conducted on the fine pitch for the sole purpose of insertion position optimization. Considering these factors gives rise to a data-driven approach to overcoming the constraints above. Advancements in machine learning techniques have proved to be one of the best data analysis techniques. We can exploit these properties to make the mating process more reliable, efficient, and faster.  
Due to the design complexity, as illustrated in Figure 2, the operation of board-to-board mating is performed manually in the industry. This is mainly because of the immense complexity and challenges in design and pitch size (0.4 mm). This research aims to determine an effective mating process by predicting the mating connector location based on the collected force data on the BtB mating using our proposed approach, which uses an encoding technique and CNN model for prediction.  
Our proposed approach allows the user to analyze the collected force dataset, especially from the fine pitch BtB connector, which uses an industrial robot mounted with a force sensor. Here, the industrial robot performs designated motion on the BtB connector based on a defined trajectory. Then, we encode the collected force data into 2D image data to train the CNN model and 1D time series models to compare the model's prediction performance. Based on this study, we can understand which model performs better in this application.  
Figure 1: Fine pitch board-to-board (BtB) connector.  
Figure 2: Mating connection.