In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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Tool Condition Monitoring for High-Performance Machining Systems—A Review

The work of P. Cortez was supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We
would like to thank to the three anonymous reviewers for their helpful suggestions.

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Business analytics in industry 4.0: a systematic review

An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion

This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motion control of an autonomous underwater vehicle (AUV) in the presence of parameter perturbations and wave disturbances. In the derivation of diving motion equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the vertical plane. This is a quite strong restricting condition in underwater operations and may cause serious modeling inaccuracies in AUV's dynamics. The problem of nonlinear uncertain diving behavior with restricting assumption on the pitch angle directly is resolved by a neural network (NN) based equivalent control. The online NN estimator is designed to approximate a part of the equivalent control term containing nonlinear unknown dynamics and external disturbances. Subsequently, corrective control based on an adaptive fuzzy proportional-integral control is applied to eliminate the chattering phenomenon by smoothing the switching signal and also compensate structured uncertainties. The weights of NN are updated such that the corrective control signal of the ANFSMC converges towards zero. The adaptive laws are developed to compute coefficients of PID sliding manifold and adjust the gain of fuzzy switching control. The simulation results are presented to shows the efficacy of the control performance.

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Robust Diving Motion Control of an Autonomous Underwater Vehicle Using Adaptive Neuro-Fuzzy Sliding Mode Technique

Determination of hydrodynamic derivatives of an ocean vehicle using cfd analyses of synthetic standard dynamic tests

Tool wear prediction in end milling of Ti-6Al-4V through Kalman filter based fusion of texture features and cutting forces

Autonomous underwater vehicles (AUV) are robotic devices which perform tasks underwater without operator interference. The paper presents, a simple genetic algorithm (sGA) is employed to tune gains for single input fuzzy logic controller (SIFLC) used for steering control of AUV. SIFLC is a computationally optimized form of conventional fuzzy logic controller (CFLC). In contrast to PD-like CFLC which requires two inputs, error and change in error, SIFLC uses only one input, signed distance. The universe of discourse for the input is tuned using a simple genetic algorithm (GA). GA is an optimization algorithm which mimics biological evolution to find the optimum solution to the problem. Population of likely solution is initialized and fitness of the population is calculated. The fit member of population reproduce while the unfit die out. The reproduction is replicated with help of arithmetic crossover. The population is subjected to mutation, to bring diversity to the population. The elite members of the population are preserved from extinction. The optimum value of universe of discourse are used to simulate yaw control of steering subsystem of NPS AUV II.

Single input fuzzy logic controller tuning for steering control of autonomous underwater vehicle: Genetic algorithm approach

Accurate determination of hydrodynamic derivatives, appearing in the equations of motion of a marine vehicle, is essential for the correct prediction of its manoeuvring performance. Solution of the...

Manoeuvring prediction of a container ship using the numerical PMM test and experimental validation using the free running model test

Planar Motion Mechanism (PMM) equipment is a facility generally attached with Towing Tank to perform experimental studies with ship models to determine the manoeuvring characteristics of a ship. Ship model is oscillated at prescribed amplitude and frequency in different modes of operation while it is towed along the towing tank at predefined speed.The hydrodynamic forces and moments are recorded, analyzed and processed to get the hydrodynamic derivatives appearing in the manoeuvring equations of motion of a ship. This paper presents the details about the Horizontal Planar Motion Mechanism (HPMM) equipment which is designed, developed and installed in Towing Tank laboratory at IIT Madras.

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Kunal Tiwari

Papers

Tool wear prediction in end milling of Ti-6Al-4V through Kalman filter based fusion of texture features and cutting forces

Single input fuzzy logic controller tuning for steering control of autonomous underwater vehicle: Genetic algorithm approach

Manoeuvring prediction of a container ship using the numerical PMM test and experimental validation using the free running model test

Horizontal Planar Motion Mechanism (HPMM) Incorporated to the Existing Towing Carriage for Ship Manoeuvring Studies

Fuzzy Logic Controller for Dynamic Positioning of an Offshore Supply Vessel