Cutting Tool Research Articles

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Cutting tool wear minimization in drilling operations of titanium alloy Ti-6Al-4V

Cutting tool wear during drilling operations can cause damage to cutting tools, machine tools, and workpieces which should be analyzed and minimized. Cutting tool wear impacts not just tool life but also the quality of the final product in terms of dimensional accuracy and surface integrity. High mechanical and thermal loads are generated during drilling operations of difficult-to-cut materials such as Ti-6Al-4V alloy which can reduce the life of cutting tool during chip formation process. Thus, to increase the accuracy of drilled parts from titanium alloy Ti6-Al-4V, the cutting tool wear during drilling operations should be accurately predicted in order to be minimized. Application of virtual machining systems is developed in the study in order to predict and minimize the cutting tool wear during drilling operations of titanium alloy Ti-6Al-4V. To predict the tool wear during drilling operations, cutting forces and temperature are calculated. Then, the finite element method (FEM) is utilized to predict the tool wear using the analytical model of Takeyama–Murata and updating the cutting tool geometry during chip formation process. To minimize the cutting tool wear during drilling operations, the optimum drilling parameters of feed rate and spindle speed are obtained using the Taguchi method-based response surface analysis algorithm. As a result, optimized drilling parameters are used in order to minimize the cutting tool wear during drilling operations. To validate the study, the experimental works are implemented to the sample workpiece from titanium alloy Ti6-Al4-V and the values of tool wear are then measured. To present the effectiveness of the proposed virtual machining system in minimization of cutting tool wear, the obtained results with and without optimized machining parameters are evaluated and compared. So, precision and productivity in drilling operations of titanium alloy Ti6-Al4-V can be enhanced using the developed virtual machining system in the study.

Cutting Parameters Optimization for Minimal Total Operation Time in Turning POM-C Cylindrical Stocks into Parts with Continuous Profile Using a PCD Cutting Tool

Turning is a complex machining process that can be characterized by a number of performances for a given machining system, workpiece material, cutting tool, and selected cutting regime. In addition to the characteristics of the machined surface quality, the estimation of machining time is particularly important for manufacturers, since machining time is directly related to other important performances of the turning process, such as productivity, cost, and energy consumption. In this paper, a model for estimation of total operation time in turning of a part with continuous profile, made of polyoxymethylene copolymer (POM-C), using a polycrystalline diamond (PCD) cutting tool, was developed. Face centred central composite design (CCD) and Box–Cox transformation approaches were applied for that purpose. The developed model was then used as the objective function in the proposed optimization model, which also included three practical constraints related to quality of the machined surface (surface roughness and workpiece deflection) and machinability aspects of the workpiece material (favourable chip forms). Nonlinear and linear models, used as constraints, were developed based on the results of experimental investigation of turning of POM-C using a PCD cutting tool. The total operation time estimation model showed good agreement with the results of tool path simulations in CAM software and validation experimental trial in real manufacturing environment. By applying the optimal solution, 44% of the total time being saved for machining of a single part can be achieved, compared to the recommended cutting parameter values, which indicates significant optimization benefits in turning industrial plastics.

Development and Optimization of Multi Point Cutting Tool from Recycled Steel using Agrowaste as Case Hardening

This research created a cutting tool made of recycled steel, palm kernel shell and alloy additives. Scrapped crankshaft and rod (100 kg) containing 0.5607% carbon were charged into an electric induction furnace with a maximum temperature of 3000℃ for melting. Before and after melting, the chemical composition of the charged components was determined using a UV-VIS spectrometer. Alloy additives were added in order to meet the carbon and other elements content of high Speed Steel materials. Samples were pulverized with carbon and heat treated. The performance of developed tool was evaluated using wear rate, toughness and micro structural analysis respectively. High proportion of carbide and strengthened martensite were obtained from the microstructure. The tool’s surface hardness and micro-hardness were 76.8 and 47.9 HB, respectively, while that of the control sample’s were 76.3 and 46.1 HB. Minimum wear rate and toughness of the 1ool were 1095x10-8m2/N and 15.01 J, while those of the control sample were 1.127x10-8 Cm2/N and 17 J respectively.

Numerical Evaluations for Robotic Turning with a Scheduled Modulatory Gain-Based Chatter Controller

During the turning production process, a material is cut by using a specified material removal strategy in order to produce the desired final form and dimension. Tool chatter is a widespread undesired dynamic instability that arises during fundamental machining processes such as milling, drilling, and turning. The dynamic interaction of the components of the cutter tool and the surface of the material being cut is just one of the numerous process variables that can result in chatter vibrations. Usually, the vigorous turning that quickly releases the metal chip causes the cutter to oscillate excessively with respect to the workpiece. Tool chatter can result in excessive wear, a shorter tool life, poor surface quality, and misaligned geometry. Due to the poor quality of the surface finish, this can consequently result in greater prices, postponed deliveries, and even lost orders. The ability of a gain modulation-based control strategy to improve the product finish in robotic turning has been examined in this investigation along with a number of other process parameters. A two-link manipulator dynamical model has been used and numerical outcomes are presented for the efficacy of the control toward achieving an improved product surface finish.

Simulation tool for chatter prediction of varying tool–machine configurations based on dynamic substructuring

Cutting tools of different type, shape and size are used in milling processes. Every individual tool changes the dynamic behavior of the system and results in varying occurrences of chatter vibrations. This paper presents a simulation tool using a frequency based substructuring method for predicting the process stability without separate measurements of the tool-machine configuration. In particular, frequency response functions of any combined tool-machine configuration is synthesized by the receptance coupling substructure analysis. It enables the calculation of critical machining parameters and maximizing material removal rates by avoiding unstable cutting conditions. Verification is done via finite element analysis of exemplary tool and machine structures.

Taguchi Analysis of the Deformation Characteristics of Single-Point Cutting Tool with Micro-Tool Coatings During Orthogonal Machining

Machining, the art of processing materials, has progressed significantly over the past century. Advances in tool materials look at blended advantages of conventional and modern tool materials through coatings at micro-scales. Tool coatings improve the hot hardness and wear resistance of tool tip and faces. The current study utilizes the Taguchi technique to analyze the deformation response of HSS tools with three different tool coatings—titanium carbide, titanium nitride and titanium aluminum nitride. The coating thicknesses of 10, 50 and 100 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m were employed on the rake face and the flank faces of the single-point cutting tool. The response of the tools while machining medium carbon steel 42CrMo4 has been studied using the finite element approach—transient structural analysis module on ANSYS®. The tool deformation was taken up as the key metric.

Mitigating Mishaps: Diachronic Trends in Handaxe Shaping and Knapping Error Management at Amanzi Springs Area 2 (Eastern Cape, South Africa)

ABSTRACT Tracing the acquisition of knapping skill in the Acheulian technocomplex is complicated by incomplete records of lithic production. Some studies have turned attention to examining knapping errors as a means of identifying signatures of toolmaking expertise in the deep past. Such insights have recently been applied to handaxes from the Area 1 spring eye at Amanzi Springs, which have suggested this locality functioned as a Large Cutting Tool workshop. Here we extend our analysis to handaxes from the Surfaces 2/3 (∼530–480 ka), Surface 1, and Cutting 5 (<480–408 ka) excavation areas within the Area 2 spring eye, which focuses on the development and frequency of step and hinge fractures and the management of cross-sectional shape. We identify differences in both the flaking strategies and the mitigation of knapping errors that demonstrate a gradual adaptation to local quartzite raw material and the acquisition of technological skill through time.

Continuity and change in lithic techno-economy of the early Acheulian on the Ethiopian highland: A case study from locality MW2; the Melka Wakena site-complex.

Recent research has made great strides clarifying the chronology, temporal span, and geographic and technological patterning of the Acheulian in eastern Africa. However, highland occurrences of the Acheulian remain under-represented and their relationship to cultural dynamics in the Rift are still poorly understood. Recently, a stratified sequence of four archaeological layers, recording Acheulian occupations dated between ~1.6 Ma and ~1.3 Ma, has been discovered in locality MW2 of the Melka Wakena site-complex (south-central Ethiopian highlands). This database enabled a systematic exploration of the question of tempo and mode of technological changes at a local sequence, allowing, for the first time, comparison with other highland sites as well as in the Rift. The detailed techno-economic study presented in this study shows that the early Acheulian at the locality was characterized by the co-existence of lithic reduction sequences for small debitage and for flake-based Large Cutting Tool production. In the early, ~1.6 Ma assemblage, a strategy of variable raw material exploitation and technological emphasis on small debitage were coupled with production of few crude bifacial elements. These shifted at ~1.4 Ma towards a preferential and intensive exploitation of a highly knappable glassy ignimbrite and emphasis on Large Cutting Tool production, including higher investment in their techno-morphological aspects. The MW2 sequence tracks lithic technological trends observed in the Rift, with only a short time lag. Diachronic changes in the raw material economy and land use patterns may have occurred at MW2 earlier than previously reported for the Acheulian on the highlands. The behavioral dynamics gleaned from the early Acheulian assemblages at MW2 are important for our understanding of the diachronic changes in the abilities of Acheulian hominins to exploit the diverse geographic and ecological habitats of eastern Africa and beyond.

Improving the Efficiency of Metalworking by the Cutting Tool Rake Surface Texturing and Using the Wear Predictive Evaluation Method on the Case of Turning an Iron–Nickel Alloy

The article proposes and substantiates the function of predictive evaluation using the criterion of the relative efficiency of using a cutting tool with a microtextured rake surface based on tangential force and cutting temperature. Comprehensive durability tests carried out under various processing modes with the measurement of heat power parameters made it possible to create an experimental base for mathematical modeling. An empirical model of cutting parameters based on modified multiplicative functions with non-constant indicators in the form of linear dependencies on processing factors was used based on planning an experiment for processing a heat-resistant alloy for predictive wear assessment in order to determine rational cutting modes. Predicting the rational use of a cutting tool with a microtextured work surface made it possible to obtain a 1.3-fold increase in durability.

EFFECTS OF MACHINING PARAMETERS ON TOOL LIFE

Cutting tools, which are used extensively in machining, are expected to have high performance and longer service life. Tool life term defines the time period between the starting of the cutting tool and the sharpening of the tool so that the tool is actively used. According to international standards, tool deterioration by wear is a specific criterion used to determine tool life. In this study, the effects of cutting parameters on the cutting performance and tool life of standard end mills with diameters Ø6, Ø8, Ø10, Ø12 which are used extensively in the die-making industry, were investigated. Detection of cutting-edge wear conditions was achieved by conducting detailed surface observations with a high-resolution camera of a three-dimensional (3D) digital microscope

Analysis of Results of Non-Contact Coordinate Measurement of a Cutting Tool Applied for Mould Machining

Analysis of Results of Non-Contact Coordinate Measurement of a Cutting Tool Applied for Mould Machining

Study of Wear, Stress and Vibration Characteristics of Silicon Carbide Tool Inserts and Nano Multi-Layered Titanium Nitride-Coated Cutting Tool Inserts in Turning of SS304 Steels.

Cutting tool characterization plays a crucial role in understanding the behavior of machining operations. The selection of a suitable cutting material, the operating conditions for the work piece, is necessary to yield good cutting-tool life. Several pieces of research have been carried out in cutting-tool characteristics for turning operation. Only a few pieces of research have focused on correlating the vibrations and stress with wear characteristics. This research article deals with stress induced in silicon carbide tool inserts and coated tool inserts while machining SS304 steel. Since this material is much less resistant to corrosion and oxidation it is widely used in engineering applications such as cryogenics, the food industry and liquid contact surfaces. Moreover, these materials have much lower magnetic permeability so they are used as nonmagnetic engineering components which are very hard. This article focuses on the machining of SS304 by carbide tool inserts and then, the cutting forces were observed with a tool dynamometer. Using observed cutting forces, the induced stress in the lathe tool insert was determined by FEA investigation. This research also formulates an idea to predict the tool wear due to vibration. Apparently, the worn-out tool vibrates more than new tools. Using the results, the relation between stress, strain and feed rate, depth of cut and speed was found and mathematically modeled using MINI TAB. It was observed that carbide tool inserts with coating withstand better than uncoated tools while machining SS304. The results were anticipated and correlation between the machining parameters furnished the prediction of tool life and obtaining the best machining outcomes by using coated tool inserts.

Continuous technological and behavioral development of late Pleistocene hominins in central South China: Multidisciplinary analysis at Sandinggai

Lithic technological development in the Late Pleistocene of South China has received substantial attention in recent years due to a series of recent fossil discoveries in the region, including some assigned to early modern Homo sapiens. Here we present a comprehensive study of the open-air site of Sandinggai, which is located in the middle valley of the Yangtze River in central South China. Luminescence dating based on quartz and feldspar has shown that the initial occupation of the site began from ca. 96.6 ± 5.1 ka and continued until the end of the last deglacial period (i.e., ca. 13.3 ± 0.3 ka). The site thus provides a substantial chronological sequence to investigate the behavioral development of early humans. Technological analysis shows that a Large Cutting Tool (LCT) techno-complex based mainly on coarse-grained quartz sandstone co-existed with a small flake and flake tool production system based on chert, with the latter playing a more prominent role. In addition, this small débitage flaking component lacks skillful prepared core technology, which is often present in the West at contemporaneous sites. Furthermore, from a wider regional context, an evolutionary pattern is suggested which shows that the pre-existing Middle Pleistocene LCT techno-complex underwent a marked decline during the Late Pleistocene, while in contrast, the local small flaking system became prominent and continued to develop through time, without discernible external influence. These data suggest a re-evaluation of current ideas on the movement of early modern humans into the region, along with their potential influence in the different regions of South China.

Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images.

Cutting tool wear state assessment during the manufacturing process is extremely significant. The primary purpose of this study is to monitor tool wear to ensure timely tool change and avoid excessive tool wear or sudden tool breakage, which causes workpiece waste and could even damage the machine. Therefore, an intelligent system, that is efficient and precise, needs to be designed for addressing these problems. In our study, an end-to-end improved fine-grained image classification method is employed for workpiece surface-based tool wear monitoring, which is named efficient channel attention destruction and construction learning (ECADCL). The proposed method uses a feature extraction module to extract features from the input image and its corrupted images, and adversarial learning is used to avoid learning noise from corrupted images while extracting semantic features by reconstructing the corrupted images. Finally, a decision module predicts the label based on the learned features. Moreover, the feature extraction module combines a local cross-channel interaction attention mechanism without dimensionality reduction to characterize representative information. A milling dataset is conducted based on the machined surface images for monitoring tool wear conditions. The experimental results indicated that the proposed system can effectively assess the wear state of the tool.

Methods to estimate subpixel level small motion from video of vibrating cutting tools

Cutting tools can vibrate with frequencies of up to a few kHz with amplitudes ranging from a few µm to hundreds of µm. To characterize these vibrations using vision-based modal analysis methods, video of vibrating tools must be acquired at high speed and resolution. High speed acquisition will ensure modes are temporally resolved without aliasing, whereas high resolution acquisition ensures that the pixel size is small enough to resolve small motion. However, since most digital cameras trade speed for resolution, estimating small motion of high frequency modes becomes difficult. To address this issue, this paper presents new methods to estimate small motion from video of vibrating cutting tools. Two methods are illustrated on a video of a slender end mill. One is hardware-based, and the other is software-based. In the hardware-based method, we use combinations of extension tubes and a reverse ring fitted with a standard lens to achieve a pixel size of as less as 1.3 µm. In the software-based method, we leverage the capability of intensity- and phase-based motion registration methods to detect small subpixel level motion, even when the pixel size is 83 µm. In both methods we use the object’s own features to detect and register motion, thus overcoming the limitation of digital image correlation schemes that need markers for target tracking. Modal parameters evaluated from motion estimated with both methods are found to agree with those extracted from twice integrated accelerations. Since methods proposed herein proffer new ways to register small oscillatory motion, we believe that our findings will further help leverage the adoption of vision-based modal analysis methods in machine tool systems as an alternative to the more traditional experimental modal analysis procedures.

Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear

Cutting tool wear strongly affects machining processes. Conventionally, multiple types of sensors are installed on the main rotating axis and platform of a machine, and signal analysis is used to determine the condition of the tool in real time through machine learning. With the traditional approach, to measure tool wear, it is required to remove the tool and place it underneath the microscope for measurement to obtain relatively precise results. The time cost, however, is relatively high; it is not cost-effective. With the charge-coupled device (CCD) camera installed in the machine tool, tool wear may be estimated through programed processing. The CCD camera, however, will affect spatial configuration in the machine tool and additional time is needed for tool wear measurement upon completion of processing each time. With the accelerometer sensor combining an offline or online tool wear measurement system and using the training method in this research model upon completion of the dataset, forecasting can be done with the precompleted model, which saves the time needed for offline or online wear measurement. However, the deployment of multiple sensors is difficult and expensive in practice. Used in this study were milling data, namely acoustic emission (AE), vibration, and current data from sensors installed on the rotating axis and platform of a machine. The data were from a National Aeronautics and Space Administration (NASA) dataset. The accelerometer data were used to develop a novel machine-learning algorithm. The vibration signals were integrated with other machining parameters. Signal preprocessing was used to reduce interference from environmental noise, and parameter records and feature signals were analyzed. A 1-D convolutional neural network (CNN) model similar to the DenseNet framework was developed. The model was optimized with framework and parameter adjustment and verified using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -fold cross-validation. The model had a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.09. Compared with other machine-learning models, the developed model has higher accuracy.

Cutting Tools Assignment and Control Using Neutrosophic Case-Based Reasoning and Best Worst Method

Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.

Collision-free and smooth path generation method of five-axis ball-end milling considering effective tool diameter change

Ball-end cutters are widely used in five-axis sculptured surface machining. It is desired to avoid drastic changes of rotary axis movements for improving the kinematics performance of machine tools while ensuring collision-free during five-axis machining. It cannot acquire the optimum solution when cutter orientations are first generated and then adjusted to avoid collisions. The cutter orientations should be generated and optimized in their feasible regions wholly while the computation load is usually large. Different relative postures between a ball-end cutter and workpiece during the machining process will produce different effective tool diameters of the ball-end cutter, resulting in different cutting speeds. Using a variable cutting speed along a tool path can unfavorably affect the process' technological effects (e. g. surface quality, tool life). It is desired to keep the effective tool diameter constant or at a very slightly varying value during five-axis ball-end machining. Moreover, it is also desired to make the cutting point in a high position of the ball-end cutter tool edge, increasing the effective tool diameter. A smooth and collision-free tool path generation method of five-axis ball-end machining while minimizing the effective tool diameter variation and increasing the effective tool diameter of the ball-end cutter along a tool path is proposed. Tool orientations along a tool path are generated and optimized within their feasible regions and the proposed method solves the smoothness tool path generation problem by applying sequentially for the rough and fine search spaces to reduce the computing time. The numerical examples and the machining experiment illustrate the effectiveness of the proposed method.

Penerapan Demonstrasi Mengasah Pahat Bubut pada Kompetensi Membubut dapat Meningkatkan Keterampilan Praktek Siswa Kelas X FL SMK Negeri 2 Karang Baru Aceh Tamiang

ABSTRACT&#x0D; Novendri, 2022. "The application of the Lathe Chisel Sharpening Demonstration on turning competence can improve the practical skills of class X FL students at SMK Negeri 2 Karang Baru Aceh Tamiang".&#x0D; &#x0D; The skill of sharpening a lathe chisel or cutting tool (tool cutter) greatly affects the level of student learning/practice outcomes in the Lathe Practice lesson. At SMK Negeri 2 Karang Baru, there are still many students who have difficulty in sharpening Lathe chisels and cutting tools, this is indicated by the low average score of the Lathe Practice exam results. In addition, in the process of learning to read measuring instruments, and technical drawings are also not good. it is necessary to apply a demonstration method of presenting material, one of which is a demonstration of sharpening a lathe chisel/cutting tool (tool cutter). This study aims to improve the quality and learning outcomes of students in the practice of turning turning through the application of demonstrations of sharpening lathe chisels in class X Metal Fabrication Engineering (FL) SMK Negeri 2 Karang Baru. This type of research is classroom action research which is carried out in 2 cycles, each cycle consisting of planning, action, observation, and reflection stages. From the research that has been done, there is an increase in student learning mastery in each cycle, namely the first cycle of the first meeting (60%) and the second meeting (65%) of the second cycle of the first meeting (91%) and the second meeting (91%). This shows that the demonstration of sharpening a lathe chisel/tool ​​cutter can improve lathe practice skills in lessons on doing work with a lathe in class X Metal Fabrication Engineering (FL) students in the even semester of SMK Negeri 2 Karang Baru Aceh Tamiang 2021/2022.&#x0D; Keywords: Lathe Chisel Sharpening Demonstration in Lathe Machine practice, tool cutter