Tool Deterioration Research Articles

A Cutting Force and Hole Geometry Study for Precision Deep-Hole Microdrilling of Magnesium.

Size effects, high thrust forces, limited heat dissipation, and tool deterioration are just some of the challenges that deep microdrilling poses, underscoring the importance of effective process control to ensure quality. In this paper, an investigation performed on a microdrilling process on pure magnesium using a 0.138 mm diameter microdrill to achieve an aspect ratio equal to 36 is proposed. The effect of the variation of the cutting parameters feed per tooth fz and cutting speed vc was studied on thrust force, supporting hole quality evaluation in terms of burr height, entrance, and inner diameters. The results showed that fz significantly influences the hole quality. In fact, as fz increases, the burr height decreases and the inner diameter approaches the nominal diameter. However, optimizing the hole geometry with high feed per tooth values increases the thrust forces, compromising tool life. In fact, a significant dependence of the thrust force on both cutting parameters was found. In this scenario, increasing vc can mitigate the high thrust forces by inducing material softening. The study results improve precision manufacturing by refining parameters, ensuring the quality and reliability of magnesium-based microcomponents.

Investigation of tool wear and energy consumption in machining Ti6Al4V alloy with uncoated tools

This research studies the energy consumption and wear mechanism of carbide cutting tools in dry machining of Ti6Al4V alloy. In the first phase of experiments, full factorial design experiments were employed to study the tool wear rate (R) and specific cutting energy (SCE) with respect to the machining conditions (cutting speed and feed rate). Results showed that machining responses such as tool wear and energy consumption are affected by cutting conditions, thus requiring investigation to understand the wear mechanisms. Therefore, in the second phase, additional experiments were performed to investigate the tool-workpiece interactions at the cutting conditions reported to have low, moderate, and high tool wear. It was revealed that strong adhesion and material transfer between the tool and workpiece caused high wear. Electron dispersive X-ray spectroscopy (EDX) analysis of worn tools showed that the transfer of tungsten (W) and cobalt (Co) to the workpiece and titanium to the tool surface is the primary cause of tool deterioration. As a result, diffusion and dissolution have degraded cutting performance and weakened the tool edge, leading to rapid wear. Furthermore, worn tools resulted in relatively higher energy consumption per unit volume of material removed at the tooltip. The degradation of cutting performance and weakening of the tool edge results in rapid wear and higher energy consumption. The study suggests that minimizing tool wear is crucial for energy reduction, improved sustainability, and cleaner production goals in dry machining of titanium-based alloys.

Assessment of cutting tool wear using a numerical FEM simulation model

The advancement of computational modeling techniques, such as FEM, has allowed to simulate complex machining processes with improved accuracy. Wear prediction is a crucial aspect in understanding and optimizing machining processes, as it directly impacts tool life, surface quality and overall machining efficiency. This work focuses on the FEM simulation, specially utilizing the DEFORM software, in conjunction with the Usui wear model, for wear prediction in machining operations. The Usui wear model, a well-established and widely used wear prediction approach, accounts for multiple wear mechanisms that include adhesion, abrasion, and diffusion. By incorporating the Usui wear model into the FEM simulation framework within the DEFORM software, it is possible to understand wear phenomena in machining processes. The integration of Usui wear model algorithms into DEFORM enables the simulation to accurately predict wear rates, distribution patterns, and progression of tool deterioration. This predictive capability facilitates the identification of critical wear zones and guides proactive measures to improve tool life, reduce production costs, and optimize machining productivity. This work presents research focused on wear prediction in cutting processes, utilizing FEM simulation with DEFORM software and incorporating the Usui wear model. Through a comprehensive analysis of wear phenomena, this research aims to optimize cutting parameters, improve tool life, and contribute to the advancement of machining and manufacturing technologies.

Linear Disassembly Line Balancing Problem with Tool Deterioration and Solution by Discrete Migratory Bird Optimizer

In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances.

Tool wear area estimation through in-process edge force coefficient in trochoidal milling of Inconel 718

The rapid wear and premature failure of the cutting tool are prone to happen due to increased forces during machining difficult-to-cut materials such as Inconel 718. The application of alternative toolpath such as trochoidal milling has significantly improved tool life and reduced the overall cycle time of the process. The wear pattern of the tool has a direct impact on the cutting forces, which increases with tool deterioration. The cutting forces in milling are modeled through the mechanistic force model and can be designated through a set of force coefficients, i.e. cutting and edge representing the shearing and ploughing phenomenon of metal removal. It has been established in the literature that tool wear has a considerable effect on the value of edge force coefficients. This paper aims to determine the in-process edge force coefficients for the trochoidal toolpath and correlates them with the corresponding flank wear area. The proposed correlation will further assist in predicting the level of flank wear area based on the force values in trochoidal milling.

Prediction of cutting forces including tool wear in high-feed turning of Nimonic® C-263 superalloy: A geometric distortion-based model

The hottest parts of aerospace components are often made of heat-resistant materials. Due to the high requirements in such parts and accelerated wear rates, machining is a challenging task. Recently, new high-feed machining strategies were launched by tool developers. The main idea is ​​using extremely low side cutting edge angles Kr so that high feeds and low chip thicknesses are both possible. These tools are supposed to be decisive for greater productivity rates. However, tool wear’s traceability is the key in order to replace them before dramatic failure. Additionally, the machining of such aggressive materials must be assisted by high pressure cooling. To reduce the environmental footprint, cryogenic techniques look promising. This work proposes a prediction cutting model for high feed turning of Nimonic© C-263 superalloy including tool wear. It allows to consider the geometric tool deterioration, which is the key mechanism in the evolution of cutting forces on this superalloy. The concept of reducing side cutting edge angle and its benefit on productivity is also explored. Besides, oil emulsion and CO2 cryogenic conditions are also compared. The wear prediction model showed good agreement with the experimental results by obtaining relative errors of less than 5 % using cutting speeds lower than 100 m/min and less than 14 % for higher speeds. The benefit of reducing the side cutting edge angle on tool life 8 (from 90° to 30°) was also demonstrated by increasing tool life x3 (from 8 to 24 min). Notches were removed. In contrast, using cryogenic conditions do not seem to be a clear advantage over this type of superalloy. Finally, optimum conditions were found: using vc = 70 m/min, f = 0.2 mm/rev and = 1 mm, good surface finish, chip control and tool life can be satisfied.

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

Ultrafast Laser Ablation of Inconel 718 for Surface Improvement

Inconel 718 is considered difficult to machine because of its ability to maintain its properties at high temperatures. The low thermal conductivity of the alloy causes accelerated tool deterioration when machining. Selective laser melting (SLM) additive manufacturing introduces a possibility of eliminating these difficulties, and producing complex shapes with this difficult-to-machine material. However, high surface roughness and porosity usually occur at the surface of components produced through additive manufacturing. In this study, the surfaces of Inconel 718 samples produced through selective laser melting were treated using laser ablation. The process parameters for the laser ablation process were analyzed in order to achieve a minimum surface roughness. The surfaces of the samples were observed after printing and after the laser ablation process using a microscope and the roughness average was measured using a profilometer. Optimized process parameters were achieved, and have the capability to reduce the surface roughness to a minimum Ra of 3.024 µm.

Online damage detection of cutting tools using Dirichlet process mixture models

The ability to monitor and predict tool deterioration during machining is an important goal because the state of wear has a significant influence on the surface quality of machined components. To build up a comprehensive condition monitoring system for diagnosis and prognosis, however, extensive measurements and knowledge of tool wear is required. Collecting labelled datasets that include damage information for this purpose can be expensive and time consuming.This paper suggests an unsupervised clustering approach using Dirichlet process mixture models to detect the change in characteristics of a cutting process online for diagnosis. As well as providing a useful monitoring tool, this approach has the potential to reduce the need for exhaustive wear measurements associated required for prognosis. The model is well suited to the erratic and unpredictable nature of tool wear progression, as the number of clusters required to determine the possible damage states are not set a-priori. Consequently, this method is equipped to handle variations across homogeneous and heterogeneous groups of tool material compositions.The proposed approach is demonstrated here as a method to reduce the time required for trials for wear characterisation of new tools. In the example shown, the results indicate that the approach would result in around a 30% reduction of test times (on average) during outer diameter turning of case hardened steel, across 10 Polycrystalline cubic Boron Nitride tools from two different material compositions.

Understandings the tribological mechanism of Inconel 718 alloy machined under different cooling/lubrication conditions

Machining of Ni-based superalloys is a challenging issue, and it may lead to tool deterioration, poor surface quality, and other structural defects. Therefore, the main theme of this study is to examine the effect of different sustainable cooling/lubrication conditions on the machining performance, surface integrity, and accompanying tribological aspects of Inconel 718 superalloy. In this context, the milling experiment variables were selected as two feed rates, two cutting speeds, and four cutting environments: dry, air, synthetic oil-based MQL, and LN2-assisted Cryo. Tool wear mechanisms, surface roughness/profile/topography, cutting performance (tool-chip interface temperature and energy consumption), chip shapes, microstructures, and micro-Vickers hardness of the machined surfaces were thoroughly analyzed to assess experimental observations. The Cryo medium reduces tool wear, surface roughness, cutting temperature, and cutting energy by 67 %, 61 %, 85 %, and 33 % in comparison with a dry environment, respectively. Abrasion, adhesion, and diffusion mechanisms were supposed to be the most dominant wear types following SEM observations. Besides, the Cryo environment concluded a reduction of grain size to ⁓ 4 µm in concomitant with an increase of micro-hardness ⁓ % 15. The whole observed outcomes agreed to a great extent with each other, asserting the effectiveness of the LN2 cooling protocol in improving machining characteristics of Inconel 718 superalloy.

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

The useful life of the tools is a very important economic factor in the metal industry, so any improvement of the tool or material that can extend the useful life of the tools is productive. Tool deterioration can cause damage, cracking and vibration to the tool, and even damage to the machine. Effective tool wear control is most important. Effective tool wear control is most important. At present, there are some tools that can obtain the necessary conditions through digital image processing, and study their lifespan according to their work cycle, as well as the types of possible relationships between the pixels of the image and the different modes of operation, allowing the extraction or isolation of the objects considered. This work analyzes the wear of the tool with the application of two-dimensional matrices with the toolbox of the MATLAB software, which allows to monitor the status of the inserts with the comparison of images in gray scales, in addition, a method of analysis based on interfaces is also being studied; through it, users can access the database that has been implemented, as well as a set of images used to verify the functions developed, determining the wear on the cutting tool.

An experimental investigation of hybrid manufactured SLM based Al-Si10-Mg alloy under mist cooling conditions

Focus on lightweight materials like aluminium, titanium etc. are growing as a reason of its eminent strength, resistance to corrosion, and less weight. Employing additive manufacturing (AM) process to fashion complex parts is increasing day-to-day. AlSi10Mg handled by laser-based powder bed fusion (LPBF) is an emerging material because of its high performance and reduced coefficient of thermal expansion due to the presence of silicon. However, AM process shows several advantages, poor surface quality is a mere disadvantage, which requires a post processing. The machining of LPBF parts for improving their quality is a difficult task as a reason of component orientation and layer thickness (LT). Recently, a new trend has developed, AM process combined with subtraction of materials to get good surface characteristics. This holistic investigation addresses problems like surface morphology, tool wear and temperature with varied cooling techniques (Dry, Flood and MQL) while machining AM-AlSi10mg work material. Generation of heat at the cutting region is responsible for surface deterioration and frequent change of tools. Compared to dry and flood condition, the MQL improved the surface trait by 45–63% and 23–43%; flank wear by 45–29% and 18–31%, respectively. SEM images show flakes in all the cutting environments. It has also been established that MQL method increases microhardness on the machined face by removing the heat in the cutting region.

Adaptive neighborhood simulated annealing for sustainability-oriented single machine scheduling with deterioration effect

This research considers the realistic phenomenon of cutting tool deterioration in the scheduling of jobs and tool replacement activities to ensure sustainable machining operations. Typically, each job has a basic processing time (independent of tool deterioration) and a tool age-dependent prolongation function. Nevertheless, such a strategy may replace the cutting tools before its useful lifespan to avoid longer processing time as it leads to higher energy consumption and likelihood of tardy jobs. Given the reduced tool operating duration, the prospect of exploiting its capability to limit the processing time prolongation is not considered by prior works. In this research, a single-machine scheduling approach that determines the job processing time based on tool age and operating duration (utilization) is studied. The problem is formulated as a mixed-integer linear program (MILP) with the objective of minimizing the weighted costs of energy consumption, tooling, and job tardiness. Due to its computational complexity, a new variant of simulated annealing (SA) algorithm is proposed to solve large instances, where an adaptive strategy is utilized to determine the neighborhood size for enhanced exploration and exploitation. Computational results for an exhaustive set of instances indicate the proposed scheduling strategy to always outperform the existing method, yielding up to a 43% reduction in total cost. Besides, the proposed variant of SA effectively solves the scheduling problem and consistently dominates the traditional SA method.

Tool deterioration of 316 stainless steel in dry down-milling using carbide insert

Machining of difficult-to-cut material such as 316 Stainless Steel has become a vital concern in machining industry. This is due to its mechanical properties of high hardness which leads to the high tendency of cutting tool breakage. Hence, the understanding of thorough progression of cutting tool deterioration is crucial, so that a better improvement of machining 316 Stainless Steel could be established. Thus, a series of documented experiments had been conducted to monitor and understand the tool deterioration of coated tungsten carbide insert in dry down-milling of 316 Stainless Steel. It was observed that the cutting tool started to deteriorate after the second pass of milling experiments and achieved VB = 0.3 mm after the sixth pass on average. Two types of tool deterioration mechanisms were identified which were tool wear and chipping. However, it was observed that the most predominant mode of tool deterioration was flank wear (tool wear). Cutting tools deteriorated gradually due to blunting and rubbing of tool edge over the surface of machined workpiece. .

Progression in Designing and Fabrication of Minimum Quantity Lubrication (MQL) System with an Arduino based Controller

Using cutting fluids in machining with petroleum-based creates health hazards issues to the machining operator and environmental problems due to the waste. One effort to reduce it is by applying minimum quantity lubrication (MQL). This research aims to develop an MQL system with an Arduino controlled based on the previous design. The main hardware was changed, i.e., pump, power supply, nozzle, sensor, and temperature setting changed from 70°C to 150°C. The fluid consumption was measured in machining with a depth of cut of 2.3 mm and 2.5 mm and cutting liquid composition of 3:7 and 7:3 (water to dromus cutting fluid). The calculation showed that cutting fluid consumption is mostly more than 1000 ml/h, or it has not achieved a maximum of 500 ml/h. However, compared to the previous design, it has reduced up to half of the former scenario. From the tool deterioration perspective, the depth of cut of more than 2.0 mm results in severe tool deterioration in the form of chipping. The higher the depth of cut, the intense the tool deteriorated. For the next design, we should add an air compressor in the MQL system and solenoid(s) to regulate the spraying.

Cutting Performance of Austenitic and Duplex Stainless Steels with Drills of Three Cutting Edges

Austenitic and duplex stainless steels are considered be the best in corrosion resistance among different grades of stainless steels. Due to high strength, duplex stainless steels applications are increasingly as an alternative to the austenitic stainless steels. In this sense, the machining study of this materials is an important issue, in order to better understand the performance of the tools and the quality of the parts manufactured for high-demand industries. In this research, the machinability of both stainless steels was evaluated in the drilling operation, using drills with three cutting edges. This type of drill geometry is particularly useful when conventional solid carbide drills fail. The drill point of triple edge is very stable, demonstrating optimal positioning accuracy and better performance in deep bores. Using the same tool geometry, a comparative analysis of drilling performance on austenitic and duplex stainless steels was made. In experimental procedure, external low-pressure cooling or internal high-pressure cooling was applied alternatively. The cutting vibration, the tool wear, the roughness and the hole diameter accuracy were evaluated in the series of holes made. The obtained results show that the most important factor to increase the number of holes made is the use of high-pressure internal cooling. When external cooling is used, AISI 304 have a worse behaviour than duplex stainless steel, due to greater susceptibility to built-up-edge formation and work hardening. The tool deterioration is mainly non-uniform chipping for external cooling and flank wear for internal cooling.

Performance of TiAlN PVD coated carbide tool in machining AISI 4340 with Minimum Quantity Lubrication (MQL) condition

There are several ways to assess machinability, viz. forces that are working during machining, the surface roughness of machined part, chip formation, and tool performance. This article aims to observe the machinability from the tool performance perspective when machining with minimum quantity lubrication (MQL). The MQL device can either be time-controlled or temperature-controlled using an Arduino. The machining was carried out on a conventional lathe toward a bar of AISI 4330 using carbide coated insert tools. The experiments were carried out using Taguchi design L9, by the varying depth of cut, coolant composition and method of applying the coolant. Tool performance was evaluated by tool deterioration value. There are four levels of tool deterioration according to its severities after machining. Analysis of variance shows that depth of cut more influence to the tool performance than the coolant application method by 32.69% and 12.82% each, respectively. However, it is proof that MQL with temperature-controlled would contribute to the minimum tool deterioration.

Study on Tool Deterioration in Machining with Minimum Quantity Lubrication (MQL) Condition

This study aims to observe the tool deterioration following application of minimum quantity lubrication method (MQL). The designed MQL system is completed with an Arduino controller system which was able to be either manually-controlled or automatically-controlled. The tool used in this study is DCMT 11 insert type. The Taguchi method using the Orthogonal Array L9 design was used to compile the design of experiments with variations in depth of cut, coolant composition, and cooling fluid application methods. Each variable consists of three levels. Tool deterioration evaluated by observing it under an optic microscope from three sides of the tool. There are four levels of tool deterioration. The value of tool deterioration of each tool is the accumulation of each side view. ANOVA analysis found that depth of cut, cutting tool composition and method of applying coolant influence tool deterioration in percentage by 32.69%, 17.30 % and 12.82% respectively. Moreover, the minimum tool deterioration would be achieved when using the parameter combination of depth of cut of 1.6 mm; mixture composition of 3:7; and using the temperature-controlled MQL.

Effect of CAD-CAM tool deterioration on the trueness of ceramic restorations

Statement of problemTools will gradually deteriorate with repetitive milling. However, tool lifetime can vary depending on the type of milling machine, the hardness of the ceramic material, and the size of the restoration. Studies evaluating the effect of tool deterioration on the trueness of milled restorations are lacking. PurposeThe purpose of this study was to evaluate the effect of tool deterioration on the trueness of milled restorations. Material and methodsA patient requiring a ceramic crown was recruited. Repetitive milling of zirconia crowns (inCoris TZI; Dentsply Sirona) with tungsten carbide rotary instruments and glass-ceramic crowns (VITABLOCS mark II; Vita Zahnfabrik) with diamond rotary instruments was performed by using a 5-axis milling machine (inLab MC X5; Dentsply Sirona) until the machine software program indicated replacing the largest tool. The percentage of deterioration for the largest tool was recorded before each milling. The trueness for different crown areas was evaluated by using a 3-dimensional evaluation software program (Geomagic Control X; 3D systems), and the crowns were clinically evaluated. The crowns were grouped into 4 quadrants as per the deterioration percentage. The Pearson correlation (r) between the deterioration percentage and trueness and linear contrast analysis (partial eta squared [η]) among the groups were performed. ResultsGlass-ceramic crown areas had low correlation except for mesial (high, r=0.63, high, η=0.37) and distal (medium, r=0.42, high, η= 0.34) areas, whereas zirconia crowns had low correlation for inner and internal areas and medium to high correlation for mesial (r= -0.31, η=0.33), distal (r= -0.53, η=0.36), occlusal (r= -0.32, η=0.164), external (r= 0.69, η=0.52), and marginal (r= -0.44, η=0.19) areas. Zirconia crowns had excellent marginal fit and occlusal contact and light to excellent proximal contact, whereas glass-ceramic crowns had excellent marginal fit, minor to major occlusal correction, and heavy proximal contact. ConclusionsThe effect of tool deterioration on trueness depended on material type and crown area. Only proximal areas were affected in the glass-ceramic crowns, whereas external, proximal, occlusal, and marginal areas were affected in the zirconia crowns. Trueness did not necessarily reflect the clinical quality of the crown, as both crown types were clinically satisfactory. Although zirconia crowns were overmilled in comparison with the reference design and glass-ceramic crowns, they had better clinical quality.

An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint

Tool deterioration is a common issue in Numerical Control (NC) machining, which directly affects part quality, production efficiency and manufacturing cost. Due to the complexity of machining, multiple deterioration forms of tool are involved during the tool deterioration process, which imposes a significant challenge for tool condition prediction because of the coupling effects among different deterioration forms. In order to address this issue, an accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint is proposed in this paper. A base model for prediction of each deterioration form is firstly constructed by using neural network, and then a 3-order tensor is constructed by stacking the weight matrices of the hidden layers of the base neural networks. The hidden relationship among the multiple related tasks is expected to be learned by means of a mathematical approach, i.e., constraining the 3-order tensor with low rank, which is realized by joint training the base prediction models. The experimental results show that the proposed method can reduce the prediction error by about 13 % for wear and 25 % for chipping respectively compared with the corresponding single task models.