Intelligent machining technology for online monitoring and control of tool condition: A review

This article focuses on the current computer monitoring and control as the research direction, studying the application strategies of artificial intelligence and big data technology in this field. It includes an introduction to artificial intelligence and big data technology, the application strategies of artificial intelligence and big data technology in computer hardware, software, and network monitoring, as well as the application strategies of artificial intelligence and big data technology in computer process, access, and network control. This analysis aims to serve as a reference for the application of artificial intelligence and big data technology in computer monitoring and control, ultimately enhancing the security of computer systems.

Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools

This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical system for the detection of cutting chip size. The optical system takes care of the acquisition and transfer of signals regarding chip size to the IoT application, where they are used as an indicator for the determination of tool conditions. In addition, the novelty of the presented approach is in the artificial intelligence integrated into the platform, which monitors a tool’s condition through identification of the current cutting force trend and protects the tool against excessive loading by correcting process parameters. The practical significance of the research is that it is a new system for fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of a more cost-effective sensor, improves machining efficiency and allows remote process monitoring on mobile devices. A machining test was performed to verify the feasibility of the monitoring system. The results show that the developed system with an ANN (artificial neural network) for the recognition of cutting force patterns successfully detects tool damage and stops the process within 35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the identification of tool breakage, which verifies the effectiveness and practicality of the approach.

With the advent of industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like data analytics, cloud computing, Internet of things, machine learning (ML), and artificial intelligence. The significant research area in predictive maintenance is tool condition monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool’s condition in operation. These techniques are cost saving and help industries with adopting future-proof solutions for their operations. One such technique called discriminant analysis (DA) must be examined particularly for TCM. Owing to its less-expensive computation and shorter run times, using them in TCM will ensure the effective use of the cutting tool and reduce maintenance times. This article presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data are collected using an in-house designed and developed data acquisition (DAQ) module setup on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter that gives the best model was found out to be “linear,” achieving an accuracy of 93.3%. This study confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry ready.

Cutting tool conditions significantly influence the quality and precision of the machined parts. With the ability to monitor the cutting tool condition, machining quality can be maintained and catastrophic failure can be eliminated. In this manner, production automation can be achieved. Therefore, tool condition monitoring (TCM) is extremely important to achieve high quality and automation of boring processes. Neural networks have been widely used in condition monitoring. Counterpropagation neural networks (CPNs), which are based on competitive learning, have been utilized in TCM in this research for high quality and automated boring. The inputs of the CPNs were the indexes acquired from three-axis cutting force data. The output was either the tool state or the value of tool wear. Seventy CPN network structures have been utilized for both real-time recognition and real-time measurements. The performance of the CPNs for TCM depends on the network structures. The results of this research are exceedingly successful. Real-time recognition of tool states showed excellent results, using a 2 × 30 × 1 CPN, of being able to predict tool states real time with 100 % accuracy. Real-time measurements can achieve a minimum error of 8.46 % using a 3×69×1 CPN, which is sufficient for continuous assessment of the tool degradation. Control actions can be taken to stop the boring process in order to avoid catastrophic failure and to enhance quality and automation of the boring process.

Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time - frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors.

Tool condition monitoring using laser scatter pattern

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

To realize smart manufacturing in various machining processes, tool condition monitoring (TCM) systems are employed to detect the state of the tool in optimizing tool conditions and preventing catastrophic failures. Common types of TCM systems include part-specific methods and generic methods. However, many developed TCM systems lack flexibility and require extensive set-up. To address these issues, the proposed algorithm takes advantage of repetitive machining operations in manufacturing settings and adopts similarity analysis to realize tool condition monitoring by comparing the signals collected from the tool with known conditions against the signals generated by the tool to be monitored. To validate the effectiveness of the proposed TCM system, a case study was performed in which the power signal was collected and used in the similarity analysis. According to this case study, it has been proven that the proposed TCM algorithm is able to accurately predict the state of the tool, i.e. tool wear, with a very simple and flexible solution. Furthermore, several tool condition induced machining parameters have been evaluated to satisfy various monitoring requirements. Nearly all evaluated machining parameters validated the performance and flexibility of the TCM algorithm.

Tool condition monitoring techniques in milling process — a review

Monitoring technology of machining operations has a long history since unmanned machining was introduced. Lots of research papers were presented and some of them has been commercialized and applied to shop floor. Despite the long history, many researchers have presented new approaches continuously in this area. This paper presents current state of monitoring technology of machining operations. The objectives of monitoring are shortly summarized, and the monitoring methods and the unique sensor technologies are reviewed. The main objective of the monitoring technology remains same; tool condition monitoring (TCM). The general approaches also remain similar; signal processing and decision making. But, the innovative methods for every step of process monitoring are being provided to improve the performance. More powerful computing is lowering the wall of much more data from more sensors by fast calculation. This technology also introduces the novel decision making strategies such as Artificial Intelligent. New materials and new communication technologies are breaking the limitation of sensor positions. Virtual machining technology which estimates the machining physics is being integrated with monitoring technology.

Machine tools and machining systems have gone through significant improvements in the past several decades. Recent advance in information technology made it possible to collect and analyze a large amount of data in real time. This brings about the concept of a smart machine tool, enabled by process monitoring and control technologies, to produce the first and all subsequent parts correctly. This paper presents a system framework for a smart machine supervisory system. The supervisory system integrates individual technologies and makes overall intelligent decisions to improve machining performance. The communication mechanism of the supervisory system is discussed in detail. Its decision-making mechanism is illustrated through an example that integrates process planning, health maintenance, and tool condition monitoring.

Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation

As a strategic emerging industry, biomanufacturing faces core challenges in achieving precise optimization and efficient scale-up of fermentation processes. This review focuses on two critical aspects of fermentation-real-time sensing and intelligent control-and systematically summarizes the advancements in online monitoring technologies, artificial intelligence (AI)-driven optimization strategies, and digital twin applications. First, online monitoring technologies, ranging from conventional parameters (e.g., temperature, pH, and dissolved oxygen) to advanced sensing systems (e.g., online viable cell sensors, spectroscopy, and exhaust gas analysis), provide a data foundation for real-time microbial metabolic state characterization. Second, conventional static control relying on expert experience is evolving toward AI-driven dynamic optimization. The integration of machine learning technologies (e.g., artificial neural networks and support vector machines) and genetic algorithms significantly enhances the regulation efficiency of feeding strategies and process parameters. Finally, digital twin technology, integrating real-time sensing data with multi-scale models (e.g., cellular metabolic kinetics and reactor hydrodynamics), offers a novel paradigm for lifecycle optimization and rational scale-up of fermentation. Future advancements in closed-loop control systems based on intelligent sensing and digital twin are expected to accelerate the industrialization of innovative achievements in synthetic biology and drive biomanufacturing toward higher efficiency, intelligence, and sustainability.

Under the background of the requirement of intelligent interconnection of rail transit electrical equipment, this paper organized the intelligent online monitoring technology, and established the application maturity evaluation system of intelligent electrical equipment condition monitoring technology, involving intelligent sensing, big data analytics, cloud computing and the Internet of things. Using analytic hierarchy process and fuzzy evaluation matrix, the monitoring technology of rail transit electrical equipment is analyzed based on the production capacity, application implementation, implementation benefits, technical reliability, technical economy, technical intelligence, technical automation, policy environment and asset operation. Aiming at the weak links, THE development plan and suggestions of rail transit electrical equipment monitoring technology are put forward, which lays the foundation for the establishment of rail transit electrical equipment intelligentization and its development framework in the field of condition monitoring.