Tool-Life Distributions—Part 1: Single-Injury Tool-Life Model

1977 ◽  
Vol 99 (3) ◽  
pp. 519-522 ◽  
Author(s):  
S. Ramalingam ◽  
J. D. Watson
Keyword(s):  
Tool Life ◽  
Manufacturing Systems ◽  
Rational Design ◽  
Probabilistic Models ◽  
Probabilistic Approach ◽  
Distribution Functions ◽  
Automated Manufacturing Systems ◽  
Tool Failure ◽  
Life Model ◽  
Life Distributions

The statistical variability of tool life in production machining must be accounted for in any rational design of large-volume or automated manufacturing systems. The probabilistic approach needed for such a design is presently limited by lack of data on tool-life distributions and by lack of knowledge of the underlying causes giving rise to tool-life scatter. Given these circumstances, on the basis of relevant physical arguments one may construct probabilistic models that produce distribution functions germane to the problem of tool-life scatter. This paper is concerned with such a study. This first part presents the results obtained on the assumption that the useful life of a tool is terminated by a single, catastrophic injury. Cases where resistance to tool failure is time-independent and time-dependent are examined. The case of tool failure caused by multiple injuries will be presented in Part 2.

Tool Life Distributions—Part 3: Mechanism of Single Injury Tool Failure and Tool Life Distribution in Interrupted Cutting

1978 ◽  
Vol 100 (2) ◽  
pp. 193-200 ◽  
Author(s):  
S. Ramalingam ◽  
Y. I. Peng ◽  
J. D. Watson
Keyword(s):  
Tool Life ◽  
Hazard Function ◽  
Rational Design ◽  
Tool Material ◽  
Distribution Functions ◽  
Life Distribution ◽  
Tool Failure ◽  
Interrupted Cutting ◽  
Life Distributions ◽  
Machining Lines

Tool life distribution under production machining conditions must be suitably accounted for in any rational design of large volume or automated machining lines. Reliable data on the type of distributions likely to be encountered are, however, unavailable. To remedy this, using relevent physical arguments, probabilistic models of tool failure which produce distribution functions germane to tool life scatter have been proposed and developed in earlier parts of this paper. An arbitrarily introduced hazard function was used to predict the life distributions likely to be obtained. The details of the mechanisms giving rise to tool failure were, however, not examined. Mechanistic questions connected with the single-injury tool failure (tool fracture) are examined in this part. The arbitrarily introduced hazard function is shown to have a physical basis. It is shown that the hazard function is determined by the interaction between the characteristics of the environment in which the tool operates and the mechanical properties of the tool material. The concepts outlined and the mechanistic model of tool failure proposed have been tested experimentally in interrupted cutting. It is shown that the predicted Weibull-distributed tool life is obtained when tool failure is due to a single injury and that the parameters of the Weibull distribution are governed by the properties of the tool material as well as those of the machining system.

Tool-Life Distributions—Part 2: Multiple-Injury Tool-Life Model

1977 ◽  
Vol 99 (3) ◽  
pp. 523-528 ◽  
Author(s):  
S. Ramalingam
Keyword(s):  
Tool Life ◽  
Multiple Injury ◽  
Linear Wear ◽  
Life Distribution ◽  
Subsequent Work ◽  
Tool Failure ◽  
Linear Wear Rate ◽  
Life Model ◽  
Life Distributions ◽  
Log Normal

The single-injury tool-life model developed in Part 1 of this paper is extended to the case of tool failure due to a multitude of injuries. The expected tool-life distribution in the case of tool failure from multiple injuries due to constant, time-independent stochastic hazards is shown to be a gamma distribution. The result obtained is based on a linear wear-rate assumption. The model is further extended to ensure applicability in the nonlinear wear region. It is shown that the expectation of a log-normal tool-life distribution when tool failure is due to crater wear is not unrealistic. No specific mechanism of tool wear is used to develop the model. The nature of the hazards and the wear mechanisms that are consistent with the multiple-injury tool-life model will be discussed in a subsequent work.

Fracture and Wear as Factors Affecting Stochastic Tool-Life Models and Machining Economics

1977 ◽  
Vol 99 (1) ◽  
pp. 281-286 ◽  
Author(s):  
S. Rossetto ◽  
R. Levi
Keyword(s):  
Failure Modes ◽  
Probabilistic Approach ◽  
Cutting Tools ◽  
Distribution Functions ◽  
Profit Rate ◽  
Tool Failure ◽  
Factors Affecting ◽  
Machining Economics ◽  
Fracture Processes ◽  
Production Conditions

Under production conditions cutting tools often fail under several failure modes, the occurrence of a single one only for a given operation being rather exceptional. In light of this observation a stochastic model is developed, considering as causes of tool failure both wear and fracture processes. Machining economics are then analyzed with a probabilistic approach, deriving distribution functions of profit rate.

scholarly journals Improved Multi-Step Look-Ahead Control Policies for Automated Manufacturing Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68824-68838 ◽  
Author(s):  
Chao Gu ◽  
Zhiwu Li ◽  
Naiqi Wu ◽  
Mohamed Khalgui ◽  
Ting Qu ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Control Policies ◽  
Look Ahead

Research on Tool Wear Based on Texture Fractal Dimension

2011 ◽  
Vol 66-68 ◽  
pp. 1163-1166
Author(s):  
Mao Jun Chen ◽  
Zhong Jin Ni ◽  
Liang Fang
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
Manufacturing Systems ◽  
Experimental Results ◽  
Machining Process ◽  
Work Piece ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Wear Condition

In automated manufacturing systems, one of the most important issues is the detection of tool wear during the machining process. The Hausdorff-Besicovitch (HB) dimension is used to analyze the feature of the surface texture of work-piece in this paper. The value of the fractal dimension of the work-piece surface texture tends to decrease with the machining process, due to the texture becoming more complex and irregular, and the tool wear is also becoming more and more serious. That can describe the inherent relationship between work-piece surface texture and tool wear. The experimental results demonstrate the probability of using the fractal dimension of work-piece surface texture to monitor the tool wear condition.

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