Showing papers on "Tool wear published in 2019"

Advanced cutting tools and technologies for drilling carbon fibre reinforced polymer (CFRP) composites: A review

Abstract: Carbon fibre reinforced polymer (CFRP) composites have excellent specific mechanical properties, these materials are therefore widely used in high-tech industries like the automobile and aerospace sectors. The mechanical machining of CFRP composites is often necessary to meet dimensional or assembly-related requirements; however, the machining of these materials is difficult. In an attempt to explore this issue, the main objective of the present paper is to review those advanced cutting tools and technologies that are used for drilling carbon fibre reinforced polymer composites. In this context, this paper gives a detailed review and discussion of the following: (i) the machinability of CFRP including chip removal mechanisms, cutting force, tool wear, surface roughness, delamination and the characteristics of uncut fibres; (ii) cutting tool requirements for CFRP machining; and (iii) recent industrial solutions: advanced edge geometries of cutting tools, coatings and technologies. In conclusion, it can be stated that advanced geometry cutting tools are often necessary in order to effectively and appropriately machine required quality features when working with CFRP composites.

A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids

Abstract: The cutting fluid is significant in any metal cutting operation, for cooling the cutting tool and the surface of the workpiece, by lubricating the tool-workpiece interface and removing chips from the cutting zone. Recently, many researchers have been focusing on minimum quantity lubrication (MQL) among the numerous methods existing on the application of the coolant as it reduces the usage of coolant by spurting a mixture of compressed air and cutting fluid in an improved way instead of flood cooling. The MQL method has been demonstrated to be appropriate as it fulfills the necessities of ‘green’ machining. In the current study, firstly, various lubrication methods were introduced which are used in machining processes, and then, basic machining processes used in manufacturing industries such as grinding, milling, turning, and drilling have been discussed. The comprehensive review of various nanofluids (NFs) used as lubricants by different researchers for machining process is presented. Furthermore, some cases of utilizing NFs in machining operations have been reported briefly in a table. Based on the studies, it can be concluded that utilizing NFs as coolant and lubricant lead to lower tool temperature, tool wear, higher surface quality, and less environmental dangers. However, the high cost of nanoparticles, need for devices, clustering, and sediment are still challenges for the NF applications in metalworking operations. At last, the article identifies the opportunities for using NFs as lubricants in the future. It should be stated that this work offers a clear guideline for utilizing MQL and MQL-nanofluid approaches in machining processes. This guideline shows the physical, tribological, and heat transfer mechanisms associated with employing such cooling/lubrication approaches and their effects on different machining quality characteristics such as tool wear, surface integrity, and cutting forces.

State-of-the-art of surface integrity in machining of metal matrix composites

Abstract: Metal matrix composites (MMCs), as advanced substitutes of monolithic metallic materials, are currently getting an increasing trend of research focus as well as industrial applications for demanding applications such as aerospace, nuclear and automotive because of their enhanced mechanical properties and relative lightweight. Nevertheless, machining of MMC materials remains a challenging task as a result of their structural heterogeneity which leads to deterioration of the machined surface integrity and rapid tool wear. While most of the research was focused on testing and analytical/numerical investigations of the tool wear, limited work was focused on machined surface integrity of MMCs. This paper presents a detailed literature survey on the conventional and non-conventional machining of metal matrix composites with the primary focus on the aspects related to workpiece surface integrity. The contribution of material mechanical and microstructural properties as well as the material removal mechanism upon the quality of workpiece surfaces/subsurface are discussed along with their influences on the fatigue performance of machined part.

Hybrid cooling-lubrication strategies to improve surface topography and tool wear in sustainable turning of Al 7075-T6 alloy

Abstract: In machining of soft alloys, the sticky nature of localized material instigated by tool-work interaction exacerbates the tribological attitude and ultimately demeans it machinability. Moreover, the endured severe plastic deformation and originated thermal state alter the metallurgical structure of machined surface and chips. Also, the used tool edges are worn/damaged. Implementation of cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability. That is why, this paper deliberately discussed the influence of pure C/L methods, i.e., such as dry cutting (DC) and nitrogen cooling (N2), as well as hybrid C/L strategies, i.e., nitrogen minimum quantity lubrication (N2MQL) and Ranque–Hilsch vortex tube (RHVT) N2MQL conditions in turning of Al 7075-T6 alloy, respectively. With respect to the variation of cutting speed and feed rate, at different C/Ls, the surface roughness, tool wear, and chips are studied by using SEM and 3D topographic analysis. The mechanism of heat transfer by the cooling methods has been discussed too. Furthermore, the new chip management model (CMM) was developed under all C/L conditions by considering the waste management aspects. It was found that the R-N2MQL has the potential to reduce the surface roughness up to 77% and the tool wear up to 118%. This significant improvement promotes sustainability in machining industry by saving resources. Moreover, the CMM showed that R-N2MQL is more attractive for cleaner manufacturing system due to a higher recyclability, remanufacturing, and lower disposal of chips.

Parametric optimization and process capability analysis for machining of nickel-based superalloy

Abstract: The manufacturing of parts from nickel-based superalloy, such as Inconel-800 alloy, represents a challenging task for industrial sites. Their performances can be enhanced by using a smart cutting fluid approach considered a sustainable alternative. Further, to innovate the cooling strategy, the researchers proposed an improved strategy based on the minimum quantity lubrication (MQL). It has an advantage over flood cooling because it allows better control of its parameters (i.e., compressed air, cutting fluid). In this study, the machinability of superalloy Inconel-800 has been investigated by performing different turning tests under MQL conditions, where no previous data are available. To reduce the numerous numbers of tests, a target objective was applied. This was used in combination with the response surface methodology (RSM) while assuming the cutting force input (Fc), potential of tool wear (VBmax), surface roughness (Ra), and the length of tool–chip contact (L) as responses. Thereafter, the analysis of variance (ANOVA) strategy was embedded to detect the significance of the proposed model and to understand the influence of each process parameter. To optimize other input parameters (i.e., cutting speed of machining, feed rate, and the side cutting edge angle (cutting tool angle)), two advanced optimization algorithms were introduced (i.e., particle swarm optimization (PSO) along with the teaching learning-based optimization (TLBO) approach). Both algorithms proved to be highly effective for predicting the machining responses, with the PSO being concluded as the best amongst the two. Also, a comparison amongst the cooling methods was made, and MQL was found to be a better cooling technique when compared to the dry and the flood cooling.

Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks

Abstract: Predictive maintenance, in contrast to preventive maintenance, raises the manufacturing quality and reliability, where the integrity is monitored continuously in service. To prevent cost intensive reengineering of outdated production plants, a retrofitting approach is presented to enable older machines towards Industry 4.0. The tool wear of a CNC milling machine is monitored using a programmable prototyping platform equipped with built-in senors. An artificial neural network is trained with acceleration data in order to classify the tool state. This demonstrative example proves the feasibility of retrofitting older machines, supported by the strongly growing field of computational intelligence and big data analysis.

Performance Evaluation of Vegetable Oil-Based Nano-Cutting Fluids in Environmentally Friendly Machining of Inconel-800 Alloy.

Abstract: Recently, the application of nano-cutting fluids has gained much attention in the machining of nickel-based super alloys due their good lubricating/cooling properties including thermal conductivity, viscosity, and tribological characteristics. In this study, a set of turning experiments on new nickel-based alloy i.e., Inconel-800 alloy, was performed to explore the characteristics of different nano-cutting fluids (aluminum oxide (Al2O3), molybdenum disulfide (MoS2), and graphite) under minimum quantity lubrication (MQL) conditions. The performance of each nano-cutting fluid was deliberated in terms of machining characteristics such as surface roughness, cutting forces, and tool wear. Further, the data generated through experiments were statistically examined through Box Cox transformation, normal probability plots, and analysis of variance (ANOVA) tests. Then, an in-depth analysis of each process parameter was conducted through line plots and the results were compared with the existing literature. In the end, the composite desirability approach (CDA) was successfully implemented to determine the ideal machining parameters under different nano-cutting cooling conditions. The results demonstrate that the MoS2 and graphite-based nanofluids give promising results at high cutting speed values, but the overall performance of graphite-based nanofluids is better in terms of good lubrication and cooling properties. It is worth mentioning that the presence of small quantities of graphite in vegetable oil significantly improves the machining characteristics of Inconel-800 alloy as compared with the two other nanofluids.

Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process

Abstract: Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

Investigation of machining characteristics of hard-to-machine Ti-6Al-4V-ELI alloy for biomedical applications

Abstract: Dry machining of Ti-6Al-4V alloy was investigated using SNMA120408 grade inserts. The material studied is designed for orthopedic applications. The effects of main cutting speed (VC), at constant feed rate (F) and depth of cut (DC) on machining characteristics (Feed force (Ff), radial force (Rf), Tangential Force (Tf)) and surface integrity (i.e., tool-chip contact length, chip segmentation, surface roughness, and tool wear) were examined. Experimental data indicate the cutting speed as the major parameter with direct impact on the machining characteristics. Increasing of the cutting speed promotes higher tangential forces that allow a decrease of the chip contact length; a smaller contact length results in a lower surface roughness and flank wear rate, respectively. To gain further insight from the simulated turning process an advanced Finite Element (FE) model was developed. The numerical model was built on the DEFORM-3D commercial software by incorporating the experimental cutting parameters. The numerical simulations results agree very well with experimental outputs in terms of cutting forces (FCS), tool-chip (T-C) contact length. Therefore, it was possible to estimate with accuracy the effective stress (σE) and the cutting temperature (TC). Further, due to its high robustness, the numerical model developed can be implemented in solving the industrial challenge (i.e., biomedical field) for predicting formations of serrated chip segment, chip thickness, potential types of chips, types of fracture mechanism and tool wear mechanism/rate generated during machining process.

Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

Abstract: The monitoring of tool condition in machining processes has significant importance to control the quality of machined parts and to reduce equipment downtime. This study investigates the application of a special variant of artificial neural networks (ANNs), in particular, wavelet neural network (WNN) for tool wear monitoring in CNC end milling process of high-speed steel. A mixed level design of experiments with machining parameters of cutting speed, feed rate, cutting depth, and machining time is developed, from which 126 experiments are conducted. For each experiment, tool wear and surface roughness of machined workpiece are measured. The tool wear images are processed, and the descriptor of wear zone is extracted. The WNN is then applied to predict the flank wear of the cutting tool and compared with commonly used types of ANNs and the statistical model. Different input combinations with the inclusion of wear zone descriptor and surface roughness of machined parts are used to evaluate the performance of all models. Results show that the WNN with the input parameters of cutting speed, feed rate, depth of cut, machining time, and descriptor of wear zone predicts the degree of tool wear most accurately.

An investigation of drilling high-strength CFRP composites using specialized drills

Abstract: Machining of high-strength carbon fiber reinforced polymers (CFRPs) has faced great challenges in quality control and tool wear management due to their inherent heterogeneity and high abrasiveness leading to serious workpiece damage and rapid tool wear. The present paper contributes to an experimental investigation of evaluating the machinability of one type of high-strength T800/X850 CFRPs representative of aircraft components. The novelty of this work lies in identifying the effects of different specialized drills on the drilling process of the high-strength CFRPs by covering a variety of aspects involving the drilling forces, hole morphologies, workpiece damage, hole dimensional accuracy, and tool wear. Both the in-situ and post-process measuring results were correlated with the input process parameters and the used drill bits. A particular focus was placed on the inspections of the resulting tool morphologies and wear mechanisms governing the drilling of the high-strength CFRPs. The results highlight the importance of using functionally designed drills and optimum cutting conditions in realizing the damage-free drilling of T800/X850 composites.

Hybrid data-driven physics-based model fusion framework for tool wear prediction

Abstract: An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently.