Arsan, TanerHatira,A.Alsan,H.F.Arsan,T.2024-06-232024-06-2320230979-835030659-0https://doi.org/10.1109/ASYU58738.2023.10296588https://hdl.handle.net/20.500.12469/5859Robots play an important role in many sectors, automating processes and supplementing human talents. However, guaranteeing reliability is critical for effective integration and widespread adoption. As a result, forecasting and managing these errors is critical. This research examines force and torque measurements in order to better understand the causes and patterns of robot execution errors. We hope to build prediction models that improve robot design and performance, eventually boosting their reliability and efficacy, by using data analysis and machine learning approaches. This study's research aims include using a dataset of force and torque measurements to predict and define robot execution failures, We hope to uncover the complex links between force and torque measurements and failure types, find crucial signals or precursors to failures, and construct strong prediction models for correct failure categorization by tackling these research topics. This study contributes to data science by demonstrating the use of analytics approaches to improve the dependability and performance of robots in real-world scenarios. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessData analysisExecution failuresFailure classificationFailure predictionForce and torque measurementsRobotic performanceConference Object10.1109/ASYU58738.2023.102965882-s2.0-85178265442N/AN/A