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Tool wear

About: Tool wear is a research topic. Over the lifetime, 10580 publications have been published within this topic receiving 187761 citations. The topic is also known as: wear on cutting tools.


Papers
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Journal ArticleDOI
TL;DR: An overview of the recent advances in high performance cutting of aerospace alloys and composite currently used in aeroengine and aerostructure applications is presented in this paper, focusing on the role of hybrid machining processes and cooling strategies (MQL, high pressure coolant, cryogenic) on machining performance.
Abstract: This paper presents an overview of the recent advances in high performance cutting of aerospace alloys and composite currently used in aeroengine and aerostructure applications. Progress in cutting tool development and its effect on tool wear and surface integrity characteristics of difficult to machine materials such as nickel based alloys, titanium and composites is presented. Further, advances in cutting technologies are discussed, focusing on the role of hybrid machining processes and cooling strategies (MQL, high pressure coolant, cryogenic) on machining performance. Finally, industrial perspectives are provided in the context of machining specific components where future challenges are discussed.

388 citations

Journal ArticleDOI
15 Jun 1981-Wear
TL;DR: In this paper, the authors report some new findings towards the goal of understanding the mechanics of chip formation when machining Titanium and other aerospace structural superalloys, which is difficult to machine except at low cutting speeds because of rapid tool wear.

386 citations

Journal ArticleDOI
TL;DR: Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR, and experimental results have also shown thatRFs can be more accurate than support vector regression (SVR) without a hidden layer.
Abstract: Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closedform mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR. [DOI: 10.1115/1.4036350]

367 citations

Journal ArticleDOI
TL;DR: In this paper, the effect of machining parameters, e.g., cutting speed, feed rate and depth of cut on tool wear and surface roughness was studied, and it was observed that increase of reinforcement element addition produced better mechanical properties such as impact toughness and hardness, but tensile strength showed different trend.

362 citations

Journal ArticleDOI
TL;DR: In this article, some of the chatter stability prediction, chatter detection and chatter control techniques for the turning process are reviewed to summarize the status of current research in this field and to identify a research scope in this area.
Abstract: Chatter vibrations are present in almost all cutting operations and they are major obstacles in achieving desired productivity. Regenerative chatter is the most detrimental to any process as it creates excessive vibration between the tool and the workpiece, resulting in a poor surface finish, high-pitch noise and accelerated tool wear which in turn reduces machine tool life, reliability and safety of the machining operation. There are various techniques proposed by several researchers to predict and detect chatter where the objective is to avoid chatter occurrence in the cutting process in order to obtain better surface finish of the product, higher productivity and tool life. In this paper, some of the chatter stability prediction, chatter detection and chatter control techniques for the turning process are reviewed to summarize the status of current research in this field. The objective of this review work is to compare different chatter stability prediction, chatter detection and chatter control techniques to find out most suitable technique/s and to identify a research scope in this area. One scope of research has been identified as establishing a theoretical relationship between chatter vibration and tool wear in order to predict tool wear and tool life in the presence of chatter vibration.

359 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023331
2022665
2021800
2020751
2019727
2018754