Wear characteristics of ultra-hard cutting tools when machining austempered ductile iron

Nodularised Ductile Cast Iron, when subjected to heat treatment processes - austenitising and austempering produces Austempered Ductile Iron (ADI). The microstructure of ADI also known as "ausferrite" consists of ferrite, austenite and graphite nodules. Machining ADI using conventional techniques is often a problematic issue due to the microstructural phase transformation from austenite to martensite during machining. This paper evaluates the wear characteristics of ultra hard cutting tools when machining ADI and its effect on machinability. Machining trials consist of turning ADI (ASTMGrade3) using two sets of PCBN tools with 90% and 50% CBN content and two sets of ceramics tools; Aluminium Oxide Titanium Carbide and Silicon Carbide - whisker reinforced Ceramic. The cutting parameters chosen are categorized as roughing and finishing conditions; the roughing condition comprises of constant cutting speed (425 m/min) and depth of cut (2mm) combined with variable feed rates of 0.1, 0.2, 0.3 and 0.4mm/rev. The finishing condition comprises of constant cutting speed (700 m/min) and depth of cut (0.5mm) combined with variable feed rates of 0.1, 0.2, 0.3 and 0.4mm/rev. The benchmark condition to evaluate the performance of the cutting tools was tool wear evaluation, surface texture analysis and cutting force analysis. The paper analyses thermal softening of the workpiece by the tool and its effect on the shearing mechanism under rough and finish machining conditions in term of lower cutting forces and enhanced surface texture of the machined part.

Wear characteristics and bearing performance of aluminium-mica particulate composite material

Some tribological properties of a mica-dispersed Al-4%Cu-1.5%Mg alloy cast by a conventional foundry technique are reported. The effect of mica dispersion on the wear rate and journal bearing performance of the matrix alloy was studied under different pressures and under different interface friction conditions. The dispersion of mica was found (a) to increase the wear rate of the base alloy, (b) to decrease the temperature rise during wear and (c) to improve the ability of the alloy to resist seizure.

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.

Micro-scale wear characteristics of electroless Ni-P/SiC composite coating under two different sliding conditions

The electroless nickel composite (ENC) with various silicon carbide contents was deposited onto aluminium alloy (LM24) substrate. The wear behaviour and the microhardness of the composite coating samples were investigated and compared with particles free and aluminium substrate samples using micro-scale abrasion tester and microhardness tester respectively. The wear scar marks and wear volume were analysed by optical microscope. The wear tracks were further studied using scanning electron microscopy (SEM). The embedded particles were found to get pressed into the matrix which helps resisting further wearing process for composite samples. However, random orientation of microcuts and microfallow were seen for ENC sample but more uniform wearing was observed for EN sample. The composite coating with low content of SiC was worn minimum. Early penetration into the substrate was seen for samples with higher SiC content. Microhardness was improved after heat treatment for all the samples containing various SiC content. Under dry sliding condition, inclusion of particles in the matrix did not improve the wearing resistance performance in as-deposited state. The wearing worsened as the content of the particles increased generally. However, on heat treatment, the composite coatings exhibited improved wear resistance and the best result was obtained from the one with low particle contents.

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.