Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Molecular dynamics simulation model for the quantitative assessment of tool wear during single point diamond turning of cubic silicon carbide

Silicon carbide (SiC) is a material of great technological interest for engineering applications concerning hostile environments where silicon-based components cannot work (beyond 623 K). Single point diamond turning (SPDT) has remained a superior and viable method to harness process efficiency and freeform shapes on this harder material. However, it is extremely difficult to machine this ceramic consistently in the ductile regime due to sudden and rapid tool wear. It thus becomes non trivial to develop an accurate understanding of tool wear mechanism during SPDT of SiC in order to identify measures to suppress wear to minimize operational cost.

In this paper, molecular dynamics (MD) simulation has been deployed with a realistic analytical bond order potential (ABOP) formalism based potential energy function to understand tool wear mechanism during single point diamond turning of SiC. The most significant result was obtained using the radial distribution function which suggests graphitization of diamond tool during the machining process. This phenomenon occurs due to the abrasive processes between these two ultra hard materials. The abrasive action results in locally high temperature which compounds with the massive cutting forces leading to sp3–sp2 order–disorder transition of diamond tool. This represents the root cause of tool wear during SPDT operation of cubic SiC. Further testing led to the development of a novel method for quantitative assessment of the progression of diamond tool wear from MD simulations.

Tool wear analysis in sheet metal stamping

This thesis advances the understanding of die wear in sheet metal stamping. It was found that transient conditions exist at the die radius, resulting in severe contact pressures that are critical to the wear behaviour. The findings challenge applicability of traditional wear tests and models for sheet metal stamping processes.

Tool wear in sheet metal stamping

This paper discusses our recent research on wear at the die radius in sheet metal stamping. According to wear theory, contact pressure and sliding distance are the two dominant factors in determining sliding wear. We applied the finite element analysis to accurately quantify the contact pressure and sliding distance at the die radius in sheet metal stamping. The results were then applied to analyze sliding wear at the die radius. We found that a typical two-peak steady-state contact pressure response exists during a channel forming process. The steady-state contact pressure response was preceded by an initial transient response, which produced extremely large and localized contact pressures. We proposed a method to numerically quantify the sliding distance, which was applied to examine the contact sliding distance at the die radius. Correlating the contact pressure and sliding distance, a new insight into the wear/galling that occurs at the die radius in sheet metal stamping was gained. The results show that the region close to zero degrees on the die radius is likely to experience the most wear, with the identified transient stage contributing to a large proportion of the total wear.

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)

Tool wear evaluations in friction stir processing of commercial titanium Ti–6Al–4V

This research addresses the application of friction stir welding (FWS) of titanium alloy Ti–6Al–4V. Friction stir welding is a recent process, developed in the 1990s for aluminum joining; this joining process is being increasingly applied in many industries from basic materials, such as steel alloys, to high performance alloys, such as titanium. It is a process in great development and has its economic advantages when compared to conventional welding. For high performance alloys such as titanium, a major problem to overcome is the construction of tools that can withstand the extreme process environment. In the literature, the possibilities approached are only few tungsten alloys. Early experiments with tools made of cemented carbide (WC) showed optimistic results consistent with the literature. It was initially thought that WC tools may be an option to the FSW process since it is possible to improve the wear resistance of the tool. The metallographic analysis of the welds did not show primary defects of voids (tunneling) or similar internal defects due to processing, only defects related to tool wear which can cause loss of weld quality. The severe tool wear caused loss of surface quality and inclusions of fragments inside the joining, which should be corrected or mitigated by means of coating techniques on tool, or the replacement of cemented carbide with tungsten alloys, as found in the literature.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

The influence of cutting tool geometry upon aspects of chip flow and tool wear:a theoretical, three dimensional examination of the cutting geometry and the shape of the twist drill

This work is undertaken in the attempt to understand the processes at work at the cutting edge of the twist drill. Extensive drill life testing performed by the University has reinforced a survey of previously published information. This work demonstrated that there are two specific aspects of drilling which have not previously been explained comprehensively. The first concerns the interrelating of process data between differing drilling situations, There is no method currently available which allows the cutting geometry of drilling to be defined numerically so that such comparisons, where made, are purely subjective. Section one examines this problem by taking as an example a 4.5mm drill suitable for use with aluminium. This drill is examined using a prototype solid modelling program to explore how the required numerical information may be generated. The second aspect is the analysis of drill stiffness. What aspects of drill stiffness provide the very great difference in performance between short flute length, medium flute length and long flute length drills? These differences exist between drills of identical point geometry and the practical superiority of short drills has been known to shop floor drilling operatives since drilling was first introduced. This problem has been dismissed repeatedly as over complicated but section two provides a first approximation and shows that at least for smaller drills of 4. 5mm the effects are highly significant. Once the cutting action of the twist drill is defined geometrically there is a huge body of machinability data that becomes applicable to the drilling process. Work remains to interpret the very high inclination angles of the drill cutting process in terms of cutting forces and tool wear but aspects of drill design may already be looked at in new ways with the prospect of a more analytical approach rather than the present mix of experience and trial and error. Other problems are specific to the twist drill, such as the behaviour of the chips in the flute. It is now possible to predict the initial direction of chip flow leaving the drill cutting edge. For the future the parameters of further chip behaviour may also be explored within this geometric model.

An investigation of the relationship between temperature, forces and tool wear in turning and drilling

Developing a means of predicting tool life has been and continues to be a focus of much research effort. A common experience in attempting to replicate such efforts is an inability to achieve the levels of agreement between theory and practice of the original researcher or to extrapolate the work to different materials or cutting conditions to those originally used. This thesis sets out to examine why most equations or models when replicated do not give good agreements. One reason which was found is that researchers in wear prediction, their predictions are limited because they generally fail to properly identify the nature of wear mechanisms operative in their study. Also they fail to identify or recognise factors having a significant influence on wear such as bar diameter. Also in this research the similarities and differences between the two processes of single point turning and drilling are examined through a series of tests. A literature survey was undertaken in wear and wear prediction. As a result it was found that there was a paucity in information and research in the work of drilling as compared to the turning operation. This was extended to the lack of standards that exist for the drilling operation. One reason for this scarcity in information on drilling is due to the complexity of the drilling and the tool geometry of the drill. In the comparative drilling and turning tests performed in this work, the same tool material; HSS, and similar work material was used in order to eliminate the differences which may occur due to this factor. Results of the tests were evaluated and compared for the two operations and SEM photographs were taken for the chips produced. Specific test results were obtained for the cutting temperatures and forces of the tool. It was found that cutting temperature is influenced by various factors like tool geometry and cutting speed, and the temperature itself influenced the tool wear and wear mechanisms that act on the tool. It was found and proven that bar diameter influences the temperature, a factor not considered previously.

Influence of cutting parameters and tool wear on martensitic stainless steel surface integrity after a face milling process

Surface finish is one of the most relevant aspects of machining operations, since it is one of the principle methods to assess quality. Also, surface finish influences mechanical properties such as fatigue behavior, wear, corrosion, etc. The feed, the cutting speed, the cutting tool material, the workpiece material and the cutting tool wear are some of the most important factors that affects the surface roughness of the machined surface. Due to the importance of the martensitic 416 stainless steel in the petroleum industry, especially in valve parts and pump shafts, this material was selected to study the influence of the feed per tooth and cutting speed on tool wear and surface integrity. Also the influence of tool wear on surface roughness is analyzed. Results showed that high values of roughness are obtained when using low cutting speed and feed per tooth and by using these conditions tool wear decreases prolonging tool life. Copyright © 2009 by ASME.

Empirical expression of tool wear when face milling 416 SS

During a machining process, cutting parameters must be taken into account, since depending on them the cutting edge starts to wear out to the point that tool can fail and needs to be change, which increases the cost and time of production. Since wear is a negative phenomenon on the cutting tool, due to the fact that tool life is reduced, it is important to optimize the cutting variables to be used during the machining process, in order to increase tool life. This research is focused on the influence of cutting parameters such as cutting speed, feed per tooth and axial depth of cut on tool wear during a face milling operation. The Taguchi method is applied in this study, since it uses a special design of orthogonal array to study the entire parameters space, with only few numbers of experiments. Also a relationship between tool wear and the cutting parameters is presented. For the studies, a martensitic 416 stainless steel was selected, due to the importance of this material in the machining of valve parts and pump shafts. Copyright © 2009 by ASME.