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This paper presents a model for on-line prediction of a drill flank wear using changes in vibration amplitude in the drilling process. Prediction of wear-time and wear-amplitude ratio relationships during the drilling process is enabled by this mathematical model through variations in vibration amplitude signals. An empirical method for an in-process approach for quantifying tool wear, and failure following it, is the outcome of the measurement of variations in the ratio of amplitude between torsional-axial dominant first mode (TP1) and the second mode (TP2) frequency. Performance of a series of cutting tests has been undertaken to study the effect of drill flank wear and other independent cutting parameters on the vibration amplitude signals. The objective is extended to finding the relationship between amplitude signals, drill flank wear and various other independent parameters. Details of the flank wear of drill and the ratio of amplitudes seen at various working conditions were determined and collected through experimental procedures. Verification of the model was done through comparison of the experimental values with the predicted values.

Surface roughness is an essential factor to evaluate the quality of component that decides the wear and fatigue properties and influences the quality of assembly. This research article focuses on real time control of surface finish in drilling using geometric adaptive control strategy. The dynamometer sensor and accelerometer are used to capture the force and vibration signals during drilling. A cubic SVM model is employed to model the surface roughness using the force, vibration and machining parameters. The accuracy of the prediction model is found to be 94 %, and the model is successfully used to control the roughness in drilling. The adaptive scheme uses a Neural Network (NN) controller to adjust the drilling parameters for ensuring the set roughness tolerance. The performance of the controller shows the potentiality of the presented methodology for the practical application in industries.

This paper discusses the development and design of two wheeled-type In-Pipe Inspection Robots (IPIRs), Kuzhali I and Kuzhali II, which were created to address the limitations of traditional human inspection methods and earlier robot designs. Specifically, the robots aim to overcome the motion singularity experienced by IPIRs when navigating through curved pipes. Kuzhali I was developed with wheels mounted at an asymmetric angle, which enables the wheels to maintain contact with the pipe's surface, preventing motion singularity. However, Kuzhali I had limitations due to its prismatic mechanism, and thus Kuzhali II was developed with a telescopic mechanism to allow it to pass through vertical pipes with obstacles. Motion analysis was conducted on both robots to demonstrate how they overcome motion singularity and navigate through straight and curved pipelines. Simulation results showed that the forces acting on the robots' wheels fell within 5 N to 12 N, demonstrating stability while navigating pipeline junctions. Experimental tests were conducted on Kuzhali II, and the results were compared to simulation results, showing an error of less than 5%. The results of the experiments indicate that Kuzhali II is safe to use for pipeline inspection, can navigate through vertical pipelines with ease and can overcome motion singularity in curved pipes. These robots offer a faster, more accurate, and safer alternative to human inspection, which can reduce the risk of pipeline failures and associated environmental and safety hazards. [ABSTRACT FROM AUTHOR]