# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 1 Introduction
### Related Work in Robotic Disassembly  
In response to the above issues, robotic disassembly offers a promising solution to the challenges posed by e-waste recycling. Disassembly robots would be able to handle hazardous conditions and toxic substances found in e-waste without endangering human health. By recovering valuable resources like rare metals efficiently and reducing the environmental impact, robotic disassembly could also make e-waste recycling more economically viable by reducing labor costs [14]. Currently, few commercial systems exist that can disassemble complete electronic products. Fuji Film Co., Ltd. (Tokyo, Japan) created a system that could completely sort and disassemble single-use cameras without any manual intervention [15]. In 2016, Apple announced the Liam system, a series of robotic arms and work cells that could disassemble iPhone 6 models. This was improved in 2018 with the Daisy system, which could differentiate and disassemble nine different iPhone models [16]. [PERSON] et al. [17; 18; 19] presented a system that could disassemble liquid crystal display (LCD) TVs. This system used a cognitive agent to decide on the next step in the disassembly process. The system was expanded by [PERSON] et al. [20; 21], who designed a learning framework that used a database of previous disassembly experiences to create rules and relationships between different components to aid disassembly runs. [PERSON] and [PERSON] [22] designed a robotic disassembly system that utilized multiple cameras and a sensor-equipped screwdriver tool to find and remove screws from the back of laptops and then integrated the cognitive architecture SOAR [23] to remember the location of screws on various laptop models to improve disassembly times. Previous research on automated disassembly has primarily concentrated on developing tools and strategies for unscrewing operations. [PERSON] et al. [24] created an unfastening tool that used torque and position sensing to monitor when the screw was removed, and a spiral control strategy to help the tool mate with the screw and overcome positioning errors. [PERSON] et al. [25] proposed a method in which images of a screw were used to facilitate the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**E-Waste Category** & **Product Examples** & \begin{tabular}{c} **Total E-Waste** \\ **Generated in 2019** \\ **(Mt)** \\ \end{tabular} & \begin{tabular}{c} **Percentage of Total** \\ **E-Waste (\%)** \\ \end{tabular} & \begin{tabular}{c} **Percentage Increase** \\ **from 2014 to 2019 (\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Temperature and \\ exchange equipment \\ \end{tabular} & \begin{tabular}{c} Refrigerators, freezers, \\ air conditioners, \\ and heat pumps \\ \end{tabular} & 10.8 & 20.1 & +7 \\ \hline \begin{tabular}{c} Screens and monitors \\ \end{tabular} & \begin{tabular}{c} Televisions, monitors, \\ laptops, and tablets \\ \end{tabular} & 6.7 & 12.5 & \(-1\) \\ \hline \begin{tabular}{c} Lamps \\ \end{tabular} & \begin{tabular}{c} Fluorescent lamps, high \\ intensity discharge \\ lamps, and LED lamps \\ \end{tabular} & 0.9 & 1.7 & +4 \\ \hline \begin{tabular}{c} Large equipment \\ \end{tabular} & \begin{tabular}{c} Washing machines, \\ dryers, electric stoves, \\ and copying equipment \\ \end{tabular} & 13.1 & 24.4 & +5 \\ \hline \begin{tabular}{c} Small equipment \\ \end{tabular} & \begin{tabular}{c} Vacumums, microwaves, \\ toasters, calculators, \\ electronic toys, \\ and electronic tools \\ \end{tabular} & 17.4 & 32.5 & +4 \\ \hline \
\begin{tabular}{c} Small IT and \\ telecommunications \\ equipment \\ \end{tabular} &
\begin{tabular}{c} Mobile phones, GPS \\ devices, routers, \\ personal computers, \\ printers, \\ and telephones \\ \end{tabular} & 4.7 & 8.8 & +2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: E-waste categories, as defined by the European Commission, examples, and e-waste generated (overall and percentage increase) [2]. orientation of the screw-tool engagement and torque monitoring was used during screw removal process. [PERSON] et al.