Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming

Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming

Mohammad Zadshakoyan (University of Tabriz, Iran) and Vahid Pourmostaghimi (University of Tabriz, Iran)
Copyright: © 2018 |Pages: 25
DOI: 10.4018/978-1-5225-4151-6.ch005


The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Therefore, tool wear prediction plays an important role in industry automation for higher productivity and acceptable product quality. Therefore, in order to increase the productivity of turning process, various researches have been made recently for tool wear estimation and classification in turning process. Chip form is one of the most important factors commonly considered in evaluating the performance of machining process. On account of the effect of the progressive tool wear on the shape and geometrical features of produced chip, it is possible to predict some measurable machining outputs such as crater wear. According to experimentally performed researches, cutting speed and cutting time are two extremely effective parameters which contribute to the development of the crater wear on the tool rake face. As a result, these parameters will change the chip radius and geometry. This chapter presents the development of the genetic equation for the tool wear using occurred changes in chip radius in turning process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology with the adequate hardware and software support. The results obtained from genetic equation and experiments showed that obtained genetic equations are correlated well with the experimental data. Furthermore, it can be used for tool wear estimation during cutting process and because of its parametric form, genetic equation enables us to analyze the effect of input parameters on the crater wear parameters.
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Manufacturing processes like drilling, milling, or turning can be optimized significantly using reliable and flexible tool monitoring systems. The most important tasks in this context are (Golz, Schillo, Wolf, & Kaufeld, 1995):

  • The fast detection of collisions, i.e. unintended contacts between the tool or the tool-holder and the work piece or components of the machine tool,

  • The identification of tool fracture (breakage), e.g. outbreaks at cutting edges,

  • The estimation or classification of tool wear caused by abrasion or other influences.

While collision and tool fracture are sudden and mostly unexpected events which require reactions in real-time, the development of wear is more or less slowly proceeding. This chapter focuses on the determination of wear, the most difficult of the three tasks. The importance of tool wear monitoring is implied by the possible economic advantages. By exchanging worn tools in time (considering the current machining process such as rough or finish turning, for instance), it is possible to avoid the production of waste. Furthermore, tools costs can be reduced noticeably with a precise exploitation of a tool’s life time. With an accurate estimation of tool wear, it is even possible to adjust the tool position in order to meet geometric specifications and to control the tool wear rate in order to guarantee a certain surface quality of the work piece or roughness.

Chip form and tool wear are two of the major machining performance measures that have been the subject of extensive studies over several decades. It must be said that having the true conception of chip formation and chip movement is an essential task in the prediction of chip breakability. Furthermore, geometrical features of produced chip give us valuable information about tribological phenomena in cutting zone such as tool wear, cutting zone temperature, etc.

On the other hand, tool wear and economical estimations related to tool life are among essential issues associated with machining optimization, because in automated manufacturing operations, tool must be removed from the cutting process well before it fails, otherwise the parts produced become out of the allowable tolerance (Devillez, Lesko, & Mozer, 2004). Therefore, tool wear is of great significance in manufacturing since it affects the quality of the components, tool life and machine costs.

Mechanisms responsible for tool failure are abrasive, adhesive and diffusion wear (Devillez et al., 2004). Considering that the chip radius has a direct relation with the form of crater wear and this type of wear is formed due to adhesive and diffusion factors, so it can be resulted that these factors affect chip geometry impressively.

By considering the basic nature of the various wear mechanisms that are generally observed in machining operations, it has been showed that during the progressive tool wear, some major wear parameters contribute to the variation in chip flow, chip curl and chip breakability in metal machining, typically in a turning operations. Figure 1 shows various wear features studied in mentioned research.

Figure 1.

Major tool wear parameters affecting chip geometry in a turning operation, VB: major flank wear; KT: crater wear depth; KB: crater wear length; KS: wear retract of the cutting edge; KK: crater wear width; N: tool nose wear (Devillez et al., 2004).

To predict the tool crater wear in this work, two independent data sets are obtained on the basis of measurements: training data set and testing data set. Chip radius is used as independent input variable, while crater wear is the dependent output variable. An equation for the tool crater wear is developed on the basis of training data set and the accuracy of obtained model is proved on the testing data set by using fitness functions.

Figure 2.

A schematic overview of the methodology used in present study

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