In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

In this paper, we develop an adaptation of the LuGre friction model so as to allow the development of the friction force and its application in any directions on systems subjected to varying normal forces. This is achieved by projecting a modified LuGre model adapted to varying normal forces in 3D along an arbitrary orthogonal system. Consequently, the direction of the friction force is automatically oriented in the correct direction, thus stick, stick-slip, and slip behavior can be represented in all directions. The projected friction model has the following friction features: stick-slip, presliding displacement, frictional lag, varying break-away force, viscous friction, Stribeck effect, and is adapted to varying normal forces. The equivalence of this projected LuGre model with the modified one is proven analytically. The friction model is then applied to simulate the friction on two mechanical systems. The first system consists of a cube sliding on a plane with a transition from stick to slip due to varying normal forces and with a pulling force oriented in multiples directions of the contact plane. The second one is a more complex system consisting of three turbine blades that uses friction to damp their resonance. The results obtained for both systems are consistent with literature.

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.

Optimal replacement of machining cutting tools is a major challenge in today's manufacturing industry. Due to the degradation of the tool during machining, late replacement of the tool leads to the risk of producing parts that do not meet technical specifications, while early replacement increases machine downtime and tool costs. To replace tools at the right time, it is necessary to monitor their degradation. Therefore, this paper compares the classification performance of different artificial intelligence approaches to classify the condition of cutting tools from cutting signals. Different approaches, namely: Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF) and k-Nearest Neighbour (k-NN) are tested, and their performance is compared. It is highlighted that ANN and RF methods obtain better classification performances (88.8% and 86.4%, respectively) than the rest of the approaches (80%). Nevertheless, all approaches can monitor the degradation of cutting tools in a satisfactory manner (i.e., 80% accuracy). A comparison of training times highlights that training a neural network takes longer than the other approaches. However, with the computational power currently available, this is not an obstacle for their implementation in real applications as this training can still be achieved in a couple of minutes.

In manufacturing, tool wear monitoring is crucial as it directly influences production quality and operating costs. Inaccurate replacement strategies can result in increased costs and substandard parts. Various Artificial Intelligence (AI) methods have been proposed to monitor tool wear using cutting signals, but a comprehensive performance comparison is lacking. This paper evaluates three distinct AI approaches: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and K-Nearest Neighbours (K-NN). The selection of these approach is based on their learning mechanisms. Each method is optimized using a GridSearch algorithm and their real-time wear monitoring capabilities are compared. The results shows that all AI techniques monitored tool wear with similar precision, making it challenging to draw a definitive conclusion in this regard. The choice of the most appropriate AI method is heavily dependent on the manufacturing environment. For large-scale manufacturing under similar cutting conditions, K-NN and SVM are a good choice. The ANN is better suited to all scenarios, but particularly where there are substantial fluctuations in cutting conditions or, in general, larger databases.

Because they account for realistic effects in opportunistic maintenance modeling, dependency hypotheses are extremely diverse in the literature. Despite recent reviews, a clear view of the dependency hypotheses is currently missing in the literature, especially regarding component interactions, resource constraints and human factors. In this paper, we provide a conceptual background on dependence modeling and the notion of maintenance opportunity. Then, a critical systematic literature review, following the PRISMA guidelines, is carried out, focusing on the current hypotheses in opportunistic maintenance, including component interactions, workers’ skills and resource constraints, economic dependence and optimization objectives. The different dependence types are identified and defined, and their presence in the literature is quantified. The included papers in this review ($n=91$) were selected on the basis of relevance to the research questions from the Web of Science, Scopus and Google Scholar databases. Exclusion criteria were set, related to the year of publication (from 2000) and language (limited to French or English), and inclusion criteria required the paper to cover modeling, simulating or reviewing literature related to opportunistic maintenance with dependencies. The results show that economic dependence is mostly modeled by sharing downtime or set-up costs. The objective function for optimization is mostly found to be the economic cost of maintenance, with concerningly little consideration for environmental indicators. These results are finally discussed in light of advances in predictive analytics and current challenges in the sustainability of industrial processes. Further developments should consider including the social and environmental aspects of sustainability in the dependencies, but also look into the benefits that predictive analytics can bring to opportunistic maintenance. The variety of modeling assumptions and dependences presented in the literature does not always allow comparing the results of the models.