Abstract
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.
Highlights
The benefits of advances in digital technologies, along with the development of the Industrial Internet of Things (IIoT) have expanded at a rapid rate over the last two decades. This is due to the development of smart sensing technologies and data storage capacities that has led to the ‘Industry 4.0’ revolution, where advanced manufacturing techniques are combined with IIoT systems to drive further intelligent action back in the physical world, motivating unmanned manufacturing
Various data acquisition, processing, and decision-making artificial intelligence (AI) techniques have been proposed in an attempt to develop an industryoriented tool condition monitoring (TCM) system
Data acquisition: Until recently, previous TCM research has adopted a conventional approach, in which the sensors are mounted on the machine spindle or the workpiece
Summary
The benefits of advances in digital technologies, along with the development of the Industrial Internet of Things (IIoT) have expanded at a rapid rate over the last two decades. Real-time TCM systems continuously acquire process data at fully regulated time intervals without interrupting the machining process, but with limited latency This enables taking corrective action to avoid cutting tool failure and workpiece damage, and can be employed in an adaptive control (AC) system to execute dynamic tool compensation to improve the accuracy and economics of the machining process [3,4]. Advanced signal processing techniques are needed to overcome these challenges by extracting indicative features that accurately represent the tool health state from the acquired signals This increases the reliability and robustness of the TCM, which would help in avoiding false alarms and poor performance of the process control algorithms. Soencittioornins g5 aannddr6ecpernetsaednvtsanthceesliitnerdaettuercetirneglaatnedd ptoreivmepnlteinmgetnotoinl gchciuptptiinngg/tboroelawkaegaer, mreospneitcotirvinelgy,afnodllorweceedntbyadavcaonnccelsusiniodneftoerctminaginarnedseparrechvegnatpinsgantodopl ochssiipbpleinogp/pboreratuknaigteie,sretospectively, followed by a conclusion for main research gaps and possible opportunities to Sensors 2022, 22, 2206 develop an accurate, robust, and generalized TCM system that meets the requirements of the industry
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