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Micro components have been demanded increasingly due to the global trend of miniaturization of products and devices. Micro milling is one of the most promising processes for micro-scale production and differs from conventional milling due to the size effect introducing phenomena like the minimum chip thickness, making the prediction of micro milling process hard. Among challenges in micro milling, tool life and tool wear can be highlighted. Understanding tool wear and modelling in micro milling is challenging and essential to maintaining the quality and geometric tolerances of workpieces. This work investigates how to model the diameter reduction of a tool caused by tool wear for micro milling of H13 tool steel. Machining experiments were carried out in order to obtain cutting parameters affecting tool wear by considering the diameter reduction. Dry full slot milling with TiAlN (titanium aluminium nitride)-coated micro tools of diameter d = 400 μm was performed. Three levels of feed per tooth ( f z = 2 μm, 4 μm and 5 μm) and two spindle speed levels ( n = 30,000 rpm and 46,000 rpm) were used and evaluated over a cutting length of l c = 1182 mm. The results show that lower levels of feed per tooth and spindle speed lead to higher tool wear with a total diameter reduction over 22%. The magnitude of the cutting parameters affecting tool wear was determined by ANOVA (analysis of variance), and the model validation meets the statistical requirements with a coefficient of determination R 2 = 83.5% showing the feasibility of the approach to predict tool wear using diameter reduction modelling in micro milling.

The continuous optimization of processing technology and evaluation methods is an important way to achieve high processing quality and processing efficiency for difficult-to-process materials. To solve the problem of frequent defects in the slot, the ultrasonic vibration-assisted slot-milling (UVASM) technology was developed for processing titanium alloy. Simultaneously, comparative experiments were carried out between UVASM and conventional slot milling (CSM), in terms of surface features of slot bottom and slot sidewall, cutting force, tool trajectory, chip morphology, and micro-hardness. The results show that uniform vibration micro-texture could significantly improve surface topography of slot bottom in UVASM, while numerous tool feed trajectories and observable machining defects detract from the surface quality in CSM. The UVASM can greatly reduce material spalling and edge breakage, thereby maintaining a smooth and regular edge profile of the slot sidewall. The tool tip trajectories of the two machining methods are highly corresponding to the machining textures of the slot sidewall surface. There is a high-frequency and small-amplitude force fluctuation signal on the axial force waveform in UVASM, which can reduce the instantaneous maximum milling force and milling force in the stable stage by 8.7 and 12.2%, respectively. The UVASM has a better chip breaking effect and surface anti-scratch effect, and the UVASM can obtain higher surface micro-hardness and deeper plastic deformation layer than those CSM. In summary, the multi-dimensional evaluation of slot processing status has been completed, and the processing quality of the slot has been improved.

Carbon fiber reinforced polymers (CFRP) have got rapidly increased applications in aerospace/aircraft and other fields due to their attractive properties of high specific strength/stiffness, high corrosion resistance, and low thermal expansion. These materials have also some challenging properties like heterogeneity, anisotropy, and low heat dissipation. Due to these properties, the issues of excessive cutting forces and machining damages (delamination, fiber pull-out, surface/subsurface defects, etc.) are encountered in machining. The cutting forces are required to be minimized for qualified machining with reduced damages. In this research, a novel cutting force prediction model has been developed for vibration-assisted slot milling. The experimental machining has been carried out on CFRP-T700 composite material. The effective cutting time per vibration cycle and the force of friction have been expressed/calculated. The feasibility of vibration-assisted machining for CFRP composites has also been evaluated. The relationships of the axial and feed cutting forces with machining parameters were investigated. The results have shown the variations below 10% among experimental and corresponding simulation values (from the model) of cutting forces. However, the higher variations have been found in some experiments which are mainly due to heterogeneity, anisotropy, and some other properties of such materials. The developed cutting force model then validated through pilot experiments and found the same results. So, the developed cutting force model is robust and can be applied to predict cutting forces and optimization for vibration-assisted slot milling of CFRP composite materials at the industry level.

The design of fixtures for slot or slab machining operations and their clamping schemes is an intricate and highly static problem that entails workpiece deformation due to the clamping and machining forces. The numerous parameters, such as clamping and machining forces, von Misses stress, and workpiece deformation in the component, are of particular relevance in this context. A practical fixture layout must include the optimum values of clamping, machining forces, von misses stress and w/p deformation. More research is required to address the complex issue in the fixture design field. This paper explores to resolve a model of the workpiece-fixation system for the slot milling operation under cutting and clamping pressures through Finite Element Analysis. The workpiece-fixture system must use a finite element model to identify a relationship between the clamping forces and von Misses stress. To determine factors like workpiece deformation, von Misses stress, clamping forces, and machining forces, the usage of machining force is recommended. This work aims to minimize the magnitude of clamping, machining forces and deformation of the work piece for the slot milling process. The behavior of the workpiece's deformation and von Misses stress were discovered in this analysis. Computer-aided process planning and automation of fixture design are the driving forces behind this current study.

Nickel-based superalloy Hastelloy C276 is widely used in high-performance industries due to its strength, corrosion resistance, and thermal stability. However, these same properties pose substantial challenges in machining, resulting in high tool wear, surface defects, and dimensional inaccuracies. This study investigates methods to enhance machining performance and surface quality by evaluating the tribological behavior of TiSiN/TiAlN-coated carbide inserts under six cooling and lubrication conditions: dry, MQL with coconut oil, Cryo-LN2, Cryo-LCO2, MQL–Cryo-LN2, and MQL–Cryo-LCO2. Open-slot finishing was performed at constant cutting parameters, and key indicators such as cutting zone temperature, tool wear, surface roughness, chip morphology, and microhardness were analyzed. The hybrid MQL–Cryo-LN2 approach significantly outperformed other methods, reducing cutting zone temperature, tool wear, and surface roughness by 116.4%, 94.34%, and 76.11%, respectively, compared to dry machining. SEM and EDS analyses confirmed abrasive, oxidative, and adhesive wear as the dominant mechanisms. The MQL–Cryo-LN2 strategy also lowered microhardness, in contrast to a 39.7% increase observed under dry conditions. These findings highlight the superior performance of hybrid MQL–Cryo-LN2 in improving machinability, offering a promising solution for precision-driven applications.

In the present study, prediction and optimization of the surface roughness and cutting forces in slot milling of aluminum alloy 7075-T6 were pursued by taking advantage of regression analysis, support vector regression (SVR), artificial neural network (ANN), and multi-objective genetic algorithm. The effects of process parameters, including cutting speed, feed per tooth, depth of cut, and tool type, on the responses were investigated by the analysis of variance (ANOVA). Grid search and cross-validation methods were used for hyperparameter tuning and to find the best ANN and SVR models. The training algorithm of developed NNs was one of the hyperparameters which was chosen from Levenberg-Marquardt and RMSprop algorithms. The performance of regression, SVR, and ANN models were compared with each other corresponding to each machining response studied. The ANN models were integrated with the non-dominated sorting genetic algorithm (NSGA-II) to find the optimum solutions by means of minimizing the surface roughness and cutting forces. In addition, the desirability function approach was utilized to select proper solutions from the statistical tools.

Non-conventional machining processes are capable of achieving higher performance compared to conventional ones due to their inherent characteristics and higher amount of parameters which can be favorably regulated. Although the correlation between the most important process parameters and process outcome has been already established for a wide range of conditions and workpiece materials, the introduction of new considerations related to the three pillars of sustainability require further investigation on new means for the enhancement of AWJ milling process. As one of the most important parameters in AWJ milling is the abrasive material, the introduction of new materials may offer considerable advantages from different perspectives. Thus, in the present work, a comprehensive investigation on the efficiency of using eco-friendly, mixed abrasives is carried out under various conditions such as different traverse feed rate, abrasive mass flow rate, water jet pressure, jet impingement angle, and mixing ratio. The feasibility of using mixed abrasives is evaluated in terms of achievable depth of penetration, kerf width, kerf taper angle as well as material removal rate (MRR), and cutting efficiency. The findings indicate that among other factors, the mixing ratio plays a noticeable role especially regarding MRR and cutting efficiency and can offer an additional effective means to achieve the desired kerf characteristics in conjunction with other significant parameters such as water jet pressure.

Carbon fiber-reinforced polymer (CFRP) composite parts are usually made as near-net shape in aerospace and astronautic industry. However, mechanical machining such as milling is often required for part finishing in order to meet the dimensional accuracy before assembly. The milling process is vulnerable to damage like fiber breakage and pullout, and delamination. The cutting force is generally recognized as an indicator of machinability for milling of CFRP. In this study, slot milling of unidirectional CFRP was conducted with different cutting orientations and conditions. The patterns of cutting force and resulting defects along with the inherent mechanism are investigated. A mechanistic milling force model is proposed in which the specific cutting energies are modeled and calibrated considering the instantaneous chip thickness, fiber cutting angle, and cutting speed. The model is applicable to force prediction in milling both unidirectional CFRP and multi-directional CFRP.

Minimum quantity lubrication (MQL) is the efficient and environmentally friendly technology, which is desirable to achieve sustainability during machining process. The nozzle distance has its significance in dominating the MQL spray and the related droplet transportation and penetration. In this paper, the nozzle distance has been optimized for MQL milling process through numerical and experimental methods. A two-way computational method has been employed to solve for the comprehensive flow field and particle trajectories, with the wall condition established on the spray impingement theory. The interactions of air flow rates and spindle rotational speeds on droplet penetration are investigated in details. The optimal ranges of nozzle distance setup obtained by simulation for different conditions were verified via slot milling tests, in which the nozzle distances were selected according to the key points obtained in the numerical process. The cutting force and surface roughness were recorded for the verification of adhesion ability and the related cutting performance. The comparison between numerical simulations and milling experiments has shown great consistency. This paper has achieved better understanding of the nozzle orientation setup and device development in MQL milling process, especially for external MQL. The theoretical basis and scientific instruction have been provided for the optimization of MQL operating parameters in industrial applications.

The aim of the work was to investigate the influence of the machining parameters on the surface roughness and tool wear during slot milling of a polyurethane block (PUB). In the experiment, the influence of the cutting speed, the feed per tooth and the depth of cut on the roughness Ra and Rz of the milling slot surface and wear of the end mill was analyzed. A three-axis CNC milling machine Emco Concept Mill 55 was used to perform the study. After the machining, the values of parameters Ra and Rz were measured using the Hommel Tester T500 induction profilometer. Three polyurethane materials of different densities were considered: the Labelite 45, the Prolab 65 and the LAB 1000. The wear of the end mill was also examined for each of the tested materials by a workshop microscope. In conclusion, it was indicated how and to what extent the variation in the machining parameters affects the surface geometrical structure of a polyurethane plate. Moreover, the research results for the tested materials were compared with each other.