## 5 Human-Technology Comparison
### Weighted Factor Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Human Operator** & **IP Software** & **AI Software** & **3D Scanning** \\ \hline \multirow{8}{*}{**Strengths**} & \(\bullet\) & Strong cognitive ability & \(\bullet\) & Good performance on small datasets (rare defects) & Ability to assist the human inspector in the most challenging and tedious types of & \(\bullet\) & High detection rate of any type of irregularity \\  & \(\bullet\) & Sophisticated & defects) & most challenging and & & from the ideal shape \\  & \(\bullet\) & decision-making & \(\bullet\) & Ability to detect a big variety of defect & tedious types of & \(\bullet\) & Defect quantifiable and \\  & \(\bullet\) & Ability to deal with & manifests (edge defects) & inspection, i.e., & & thus categorisable \\  & \(\bullet\) & Ability to deal with & manifestations (edge defects) & \(\bullet\) & Photoscope & (accept/reject) \\  & \(\bullet\) & high variation of parts & \(\bullet\) & Methods) & \(\bullet\) & Continuous learning & High reliability and \\  & and different defect & \(\bullet\) & Not prone to human factors & and improvement of & repeatability \\  & \(\bullet\) & Qualified and certified & \(\bullet\) & High repeatability & new dataset & \(\bullet\) & Versatile—can scan any \\  & \(\bullet\) & to perform inspection & \(\bullet\) & Fast processing times & \(\bullet\) & Not affected by human factors & \(\bullet\) \\  & \(\bullet\) & Use of common sense & & & \(\bullet\) & High repeatability & \\  & & & & \(\bullet\) & Fast processing times & \\ \hline \multirow{8}{*}{**Weaknesses**} & \(\bullet\) & Prone to err & \(\bullet\) & Performance static, i.e., does not improve & \(\bullet\) & Currently low accuracy & \(\bullet\) & High investment cost \\  & \(\bullet\) & Inspection around & & does not improve & (significantly lower & \(\bullet\) & Slow scanning and \\  & 68–83\% accurate in & & automatically (manual & than human operator) & & labour intersive, unless \\  & borescope and & & algorithm tuning & \(\bullet\) & Requires large datasets & & fully automated, \\  & piece-part inspection, & & required) & & to train algorithm & & requiring even higher \\  & respectively & \(\bullet\) & Currently low accuracy & \(\bullet\) & Additional work for & & investment \\  & \(\bullet\) & Performance variances & (significantly lower & operator to label data & \(\bullet\) & Only suitable for \\  & across the system (low & & than human operator) & \(\bullet\) & Only possible for & & piece-part inspection \\  & repeatability within and & \(\bullet\) & Does not currently & defects with high & \(\bullet\) & Difficult to scan \\  & between operators) & & detect surface defects & occurrence, difficult to & & reflective parts, thus \\  & \(\bullet\) & Performance affected by & and edge defect with no & & detect rare defect types & requires part \\  & human factors, e.g., & 3D deformation & & (lack of data) & preparation & \\  & compliance, & Slow and manual & \(\bullet\) & Long setup time to train & \(\bullet\) & Sensitive to ambient \\  & distraction, lack of & process (image & algorithm—additional & & light and cannot be \\  & awareness, lack of & acquisition not & time for operator to & & used outdoors or in \\  & knowledge and fatigue & & automated) & \(\bullet\) & \(\bullet\) & bright inspection \\  & & Human error and bias & validate results & & environments \\  & & can be programmed & \(\bullet\) & Extensive development & \(\bullet\) & Sensitive to motion of \\  & & into algorithm & process until & & the part or scanner \\  & & \(\bullet\) & Requires other assisting & performance is & \(\bullet\) & Might require 3D \\  & & technologies & comparable to human & & scanning specialist to \\  & & (automated, & operator & & setup and operate \\  & & standardised image & \(\bullet\) & Learns from human and & \(\bullet\) & Large footprint not only \\  & & acquisition), causing & & may learn errors too & & for scanner but also for \\  & & additional costs & \(\bullet\) & Problems with & & parts feed and storage \\  & & Problems with & explainability & & \\  & & explainability & & & \\ \hline \multirow{8}{*}{**Opportunities**} & \(\bullet\) & Trusted by regulators & \(\bullet\) & Applicable to other & \(\bullet\) & Productivity & \(\bullet\) & Record of operational \\  & and customers & & similarly shaped parts & improvement and & & wear over part life; \\  & \(\bullet\) & Ability to make & & (e.g., vanes) without & shorter turnaround & could be used for \\  & decisions and sign off & training (only & & times, thus competitive & reverse engineering \\  & tasks & optimisation of the & & advantage & \(\bullet\) & Enabling smart \\  & \(\bullet\) & parameters) & \(\bullet\) & Enabling smart & & factories and Industry \\  & different tasks in & \(\bullet\) & Enabling smart & factories and Industry & 4.0 & \\  & different processes & & 4.0 & \(\bullet\) & High technology & \(\bullet\) & Easy to integrate into \\  & & & & awareness (trend) & & inspection operations \\  & & & & awareness (trend) & & \(\bullet\) & Opportunity to develop \\  & & & & & 3D scanning for \\  & & & & & & 3D scanning for \\ \hline \hline \end{tabular}
\end{table}