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Tools rule!
In a messy yard, a team of tools gets organized, then spends a busy day building a shed.
Book
A review of cutting tool life prediction through flank wear monitoring
PurposeThe aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.Design/methodology/approachThis study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.FindingsCutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.Originality/valueThis submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.
Journal Article
Precision machining technology
\"Introduces students, both at the secondary and postsecondary levels, to the exciting world of precision machining technology as it is practiced in the 21st century.\"
Book
A review on cutting tool technology in machining of Ni-based superalloys
In this paper, a state-of-the-art review on cutting tool technology in machining of Ni-based superalloys is presented to better understand the current status and to identify future directions of research and development of cutting tool technologies. First, past review articles related to the machining of Ni-based superalloys are summarized. Then machinability of superalloys is introduced, together with the reported methods used in cutting tool design. The current researches on cutting tools in the machining of superalloys are presented in different categories in terms of tool materials, i.e., carbide, ceramics, and Polycrystalline cubic boron nitride (PCBN). Moreover, a set of research issues are identified and highlighted to improve the machining of superalloys. Finally, discussions on the future development are presented, in the areas of new materials/geometries, functional surfaces on the cutting tool, and data-driven comprehensive optimization.
Journal Article
Investigation on the Performance of Coated Carbide Tool during Dry Turning of AISI 4340 Alloy Steel
The machinability of materials is highly affected by their hardness, and it affects power consumption, cutting tool life as well as surface quality while machining the component. This work deals with machining of annealed AISI 4340 alloy steel using a coated carbide tool under a dry environment. The microhardness of annealed and non-annealed workpieces was compared and a significant reduction was found in the microhardness of annealed samples. Microstructure examination of the annealed sample revealed the formation of coarse pearlite which indicated a reduction of hardness and improved ductility. A commercially CVD multilayer (TiN/TiCN/Al2O3/ZrCN) coated cemented carbide cutting tool was employed for turning quenched and tempered structural AISI 4340 alloy steel by varying machining speed, rate of feed, and depth of cut to evaluate the surface quality, machining forces, flank wear, and chip morphology. According to the findings of experiments, the feed rate possesses a high impact on surface finish, followed by cutting speed. The prominent shape of the serrated saw tooth chip was noticed at a higher cutting speed. Machined surface finish and cutting forces during turning is a function of the wear profile of the coated carbide insert. This study proves that annealing is a low-cost and economical process to enhance the machinability of alloy steel.
Journal Article
Tool book
Depicts a number of different tools used in building and the kinds of work they are used for.
Book
Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models
The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.
Journal Article
Curious Pearl tinkers with simple machines : an augmented reading science experience
\"Curious Pearl and her brother don't like chores. They'd rather play with their kitten. But Dad looks serious, so Pearl and her brother come up with a solution. They build a contraption to help them with their chores. As they build, Pearl teaches her brother all about simple machines. Free bonus video content through the Capstone 4D augmented reality app enhances the science experience.\"-- Provided by publisher.
Book
Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques
The need to monitor tool wear is crucial, particularly in advanced manufacturing industries, as it aims to maximise the lifespan of the cutting tool whilst guaranteeing the quality of workpiece to be manufactured. Although there have been many studies conducted on monitoring the health of cutting tools under a specific cutting condition, the monitoring of tool wear across multi-cutting conditions still remains a challenging proposition. In addressing this, this paper presents a framework for monitoring the health of the cutting tool, operating under multi-cutting conditions. A predictive model, using advanced machine learning methods with multi-feature multi-model ensemble and dynamic smoothing scheme, is developed. The applicability of the framework is that it takes into account machining parameters, including depth of cut, cutting speed and feed rate, as inputs into the model, thus generating the key features for the predictions. Real data from the machining experiments were collected, investigated and analysed, with prediction results showing high agreement with the experiments in terms of the trends of the predictions as well as the accuracy of the averaged root mean squared error values.
Journal Article