Search Results (283)

Lithium niobate (LiNbO₃, LN) single crystal is widely used in optoelectronic fields due to its excellent performance. However, its low hardness, high brittleness, and strong anisotropy lead to low processing efficiency and poor surface quality. Hydrophilic fixed abrasive lapping technology was adopted for the thinning of LN wafers in this research. The effects of lapping pressure on the thinning process were investigated comprehensively in terms of the material removal rate (MRR), surface quality, and subsurface damage (SSD). The results show that lapping pressure exerted a significant influence on the machining performance. High pressure contributed to improving the MRR but aggravated surface roughness (Ra) and SSD. With low pressure, material removal was dominated by ductile removal machining, with fine scratches as the main damage form, which was favorable for obtaining low Ra and low SSD. The root mean square (RMS) of the acoustic emission (AE) signal rose with the increase in pressure, increasing slowly in the ductile removal regime and rising abnormally in the brittle removal regime. It was positively correlated with the MRR and SSD and can be used as an in situ monitoring indicator. After a comprehensive comparison of five groups of experiments, 7 kPa was determined to be the optimal lapping pressure, with the following corresponding parameters: wafer speed: 100 rpm; lapping table speed: 80 rpm; slurry flow rate: 100 mL/min; eccentricity: 60 mm; soft lapping pad; abrasive mass fraction: 50%; and lapping time: 5 min. Under these conditions, the Ra value was approximately 30 nm, the MRR exceeded 1 μm/min, and SSD was as low as 3.3 μm, realizing the synergistic optimization of high-efficiency and low-damage machining. It provides a favorable foundation for the subsequent processing of LN substrates, such as ultra-precision polishing, thin-film transfer, and bonding. Full article

To achieve both a high material removal rate and excellent surface quality, this paper proposes a solid-phase Fenton chemo-mechanical lapping (SF-CML) method. Using high-purity type 316 stainless-steel as the research object, a solid lapping tool containing Fe₃O₄ microparticles was employed in synergy with an H₂O₂-based slurry. Under locally high-pressure and high-temperature conditions, Fe²⁺ ions are released, which in turn catalyze the generation of highly reactive hydroxyl radicals (·OH). These radicals promote the formation of an oxide layer on the workpiece surface, which is continuously removed through mechanical action. The results show that at pH 2.5 and an H₂O₂ concentration of 0.05 wt%, SF-CML achieves the best processing performance, with an MRR of 16.64 μm/min and a Sa as low as 20.95 nm. XPS, EPR, and other characterization methods collectively provided evidence for the oxidation of the sample surface and the existence of ferrous ions and hydroxyl radicals in the slurry, thereby confirming the effectiveness of the solid-phase Fenton reaction. Compared with conventional homogeneous Fenton CMP and pure mechanical lapping, SF-CML not only significantly improves removal efficiency but also effectively enhances surface quality. This method avoids the problems of easy precipitation and low removal efficiency commonly encountered in traditional homogeneous Fenton systems, providing a new technical pathway for high-efficiency precision processing of metallic materials. Full article

Achieving efficient and high-quality surface processing of single-crystal diamond (SCD) remains challenging due to its extreme hardness and chemical inertness. Traditional Fenton-based slurries using H₂O₂ suffer from poor stability, safety risks, and strict acidic pH requirements. In this study, peroxymonosulfate (PMS) is introduced as an alternative oxidant to develop a novel chemical mechanical polishing (CMP) slurry for SCD. Compared with H₂O₂, PMS exhibits higher stability and generates sulfate radicals (SO₄⁻·) with stronger oxidation capability when activated by Fe²⁺. The proposed slurry achieves efficient material removal over a wider pH range (2–6). Under optimal conditions (pH = 3), a maximum material removal rate (MRR) of 676 nm/h is obtained, along with an ultra-smooth surface (Sa = 0.177 nm in the measuring area of 868 × 868 μm²). Notably, the slurry maintains high MRR (>400 nm/h) even under weakly acidic conditions (pH 5–6). XPS and radical quenching experiments confirm that continuous generation of reactive radicals promotes surface oxidation and stable material removal. This work provides a stable and efficient CMP slurry for SCD with enhanced pH adaptability. Full article

This study investigates ultrasonic vibration-assisted (UV) CNC milling of hardened 90CrSi steel cylindrical surfaces, with emphasis on ultrasonic horn design, experimental analysis, and multi-objective optimization of machining parameters, addressing the need for an integrated framework combining system design, experimental validation, and multi-objective optimization. A quarter-wavelength ultrasonic horn was designed and experimentally validated to operate at a frequency of 20 kHz. By adjusting the horn–workpiece system, stable vibration amplitudes were achieved to enable effective ultrasonic-assisted milling of cylindrical surfaces. Milling experiments based on a Box–Behnken design were conducted to examine the effects of vibration amplitude, cutting speed, feed rate, and radial depth of cut on material removal rate (MRR) and surface roughness (Ra). Surrogate models using response surface methodology (RSM) and Gaussian process regression (GPR) were developed to predict machining performance. A GPR-assisted NSGA-II algorithm was then applied to simultaneously maximize MRR and minimize Ra, resulting in a well-defined Pareto front that reveals the trade-off between machining productivity and surface quality. Furthermore, an AHP-based decision-making approach was employed to select preferred machining conditions from the Pareto-optimal solutions. The GPR models demonstrated high predictive accuracy (R² > 0.98), and validation experiments confirmed the reliability of the predicted optimal results, with deviations below 5%. In addition, a comparative analysis between ultrasonic-assisted and conventional milling showed that MRR increased by 10.81–40.17%, Ra decreased by 27.11–44.44%, and cutting force was reduced by 14.2–42.65%, providing direct experimental evidence of improved machinability. The results demonstrate that the proposed integrated framework provides an effective strategy for optimizing ultrasonic vibration-assisted milling processes and improving the machinability of hardened 90CrSi cylindrical surfaces. Overall, the proposed framework provides a practical and cost-effective strategy for enhancing machining performance and offers a robust approach for multi-objective optimization of ultrasonic vibration-assisted milling processes. Full article

This paper investigates the AWJ machinability of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) fiber/epoxy-based hybrid composites by analyzing key machinability metrics such as kerf width (KW), material removal rate (MRR), and surface roughness (Ra). Various process parameters including CNT weight percentage, CNT diameter, stand-off distance, and traverse speed have been varied to optimize the machining performance. Experimental analysis suggested that increasing the CNT weight percentage significantly enhanced material hardness, thereby reducing both the MRR and surface roughness. Moreover, adjusting the stand-off distance and traverse speed further improved the machinability of the composite. ANOVA results highlighted that CNT weight percentage was a significant factor, accounting for 94.17% of the variation in MRR and 93.72% of the variation in surface finish, while the stand-off distance influenced 87.03% of the variation in kerf width. Additionally, response surface methodology (RSM) was utilized to develop predictive models that estimated KW, MRR, and Ra with error rates of 2.95%, 2.23%, and 5.65%, respectively. These insights offer a valuable framework for tailoring the AWJ process to achieve optimal machining outcomes in HRS/CNT/epoxy composite materials Full article

As a critical pretreatment process for chemical and mechanical polishing (CMP), the lapping roughness of gallium nitride (GaN) crystals directly influences the outcome of subsequent polishing and the reliability of final devices. This study systematically investigates the key factors affecting the lapping performance of GaN single crystals, focusing on abrasive type, particle size, and spindle speed, and elucidates their mechanisms in regulating material removal rate (MRR) and surface roughness. Using a micro-thickness gauge and controlled variable method, the material removal depth of the (0001) plane of GaN was accurately measured. The results show that the MRR increases with the increase in abrasive particle size within a certain range, albeit at the cost of increased surface roughness. Meanwhile, the spindle speed and MRR exhibit a positive correlation under specific conditions. Considering these lapping parameters, a balance between high MRR and controlled roughness can be achieved, providing a technical foundation for efficient and precise lapping of GaN crystals and facilitating the fabrication of GaN-based devices. Full article

Ultrasonic vibration-assisted grinding using cubic boron nitride (CBN) wheels has emerged as an effective approach for improving surface integrity and machining efficiency in hard-to-machine materials. However, achieving a desirable balance between surface roughness and material removal rate remains a critical challenge due to their inherently conflicting nature. In this study, a multi-objective optimization framework is proposed to simultaneously minimize surface roughness (Ra) and maximize material removal rate (MRR) in external cylindrical CBN grinding performed on a computer numerical control (CNC) milling machine under ultrasonic vibration assistance. Gaussian process regression models were first developed to accurately represent the nonlinear relationships between machining parameters and the target responses. These surrogate models were subsequently integrated with the non-dominated sorting genetic algorithm II (NSGA-II) to generate a set of Pareto-optimal solutions. The convergence behavior of the optimization process was evaluated using the hypervolume indicator, confirming fast and stable convergence. The resulting Pareto front clearly illustrates the trade-off between Ra and MRR, and a knee point solution was identified as a practical compromise for industrial application. The optimized results demonstrate that ultrasonic vibration-assisted CBN grinding can significantly enhance machining performance while maintaining acceptable surface quality. The proposed methodology provides an effective decision-support tool for multi-objective process optimization in advanced grinding applications. Full article

This study systematically investigates the effects of polishing slurry components on the material removal rate (MRR) and surface morphology of the C-face of 4H-SiC substrates during chemical mechanical polishing (CMP) based on the Fenton reaction. By regulating the particle size and concentration of colloidal silica abrasives, H₂O₂ concentration, and Fe₃O₄ catalyst content, the mechanisms of each component on MRR and surface roughness (Sa) were systematically analyzed. The results indicate that in an alkaline polishing slurry at pH = 9, Fe₃O₄ effectively catalyzes the decomposition of H₂O₂ to generate hydroxyl radicals (·OH), thereby significantly enhancing the material removal efficiency. When using colloidal silica with a particle size of 110 nm at a concentration of 8 wt%, H₂O₂ at 5 wt%, and Fe₃O₄ at 0.03 wt%, a maximum MRR of 701 nm/h was achieved along with a good surface quality of Sa = 0.79 nm. The study also found that the abrasive particle size and concentration, as well as the ratio of oxidant to catalyst, significantly influence the chemo-mechanical synergy. Excessively high H₂O₂ or Fe₃O₄ concentrations can trigger ·OH quenching reactions, thereby reducing polishing efficiency. This research provides a theoretical basis and process optimization direction for the application of heterogeneous Fenton reactions in SiC CMP under alkaline conditions. Full article

Silicon carbide (SiC) substrates have been widely adopted in high-performance applications such as power electronics, optoelectronics, and semiconductors. However, achieving high-quality processing remains a formidable challenge due to SiC’s inherent hardness and brittleness. This study investigates the effects of diamond and boron carbide (B₄C) abrasives on material removal rate (MRR) and surface roughness during the lapping of SiC substrates. The results demonstrate that the mix ratio of diamond to B₄C significantly affects the roughness of the lapped substrates. Increasing B₄C proportions results in lower Sa values. Nonetheless, excessive B₄C powder leads to insufficient abrasive lapping force. Furthermore, finer B₄C powder contributes to higher surface roughness and higher SiC removal rate. Additionally, the influence of different diamond powder sizes on the depth of subsurface damage (SSD) of lapped SiC substrates was evaluated using an atmospheric inductively coupled plasma (ICP) etching method. As the diamond particle size increased from 3 μm to 4 μm, the SSD depth rose from 1.56 μm to 2.16 μm. Furthermore, this study elucidates the lapping removal process of silicon carbide substrate from the mechanism, which can provide actionable guidance for refining lapping techniques in 4H-SiC substrate manufacturing. Full article

Abrasive water jet machining (AWJM) is a non-traditional machining process that is increasingly employed for shaping hard-to-machine materials, particularly titanium (Ti)-based alloys such as Ti-6Al-4V. Owing to its non-thermal nature, AWJM enables effective material removal while minimising metallurgical damage and preserving subsurface integrity. The process performance is governed by several interacting parameters, including jet pressure, abrasive type and flow rate, nozzle traverse speed, stand-off distance, jet incident angle, and nozzle design. These parameters collectively influence key output responses such as the material removal rate (MRR), surface roughness, kerf geometry, and subsurface quality. The existing studies consistently report that the jet pressure and abrasive flow rate are directly proportional to MRR, whereas the nozzle traverse speed and stand-off distance exhibit inverse relationships. Nozzle geometry plays a critical role in jet acceleration and abrasive entrainment through the Venturi effect, thereby affecting the cutting efficiency and surface finish. Optimisation studies based on the design of the experiments identify jet pressure and traverse speed as the most significant parameters controlling the surface quality in the AWJM of titanium alloys. Recent research demonstrates the effectiveness of artificial neural networks (ANNs) for process modelling and optimisation of AWJM of Ti-6Al-4V, achieving high predictive accuracy with limited experimental data. This review highlights research gaps in artificial intelligence-based fatigue behaviour prediction, computational fluid dynamics analysis of nozzle wear mechanisms and jet behaviour, and the development of hybrid AWJM systems for enhanced machining performance. Full article

As LED devices continue to advance toward miniaturization and higher power density, heat dissipation has become a critical factor constraining their reliability and service life. Molybdenum is widely employed as a substrate material in LED devices owing to its high thermal conductivity and low coefficient of thermal expansion. However, substrate applications impose stringent requirements on surface finish, flatness, and low-damage processing. Chemical mechanical polishing (CMP) can effectively balance global and local flatness and serves as the final step in producing high-quality molybdenum substrate surfaces. To enable efficient and precise processing of molybdenum substrates, this study adopts an orthogonal experimental design for double-sided CMP to systematically investigate the effects of polishing pressure, polishing slurry pH, additives in the polishing slurry, and abrasive particle size on the material removal rate (MRR) and surface roughness (Sa). An optimal parameter combination was identified via weight-matrix optimization: a polishing pressure of 115 kPa, pH 11, H₂O₂ (0.5%) and glycine (5 mg/L) as additives, and an abrasive particle size of 0.6 μm. Under these conditions, the MRR reached 80 nm·min⁻¹ and Sa decreased to 1.1 nm, yielding a smooth, mirror-like surface. The results indicate that multi-factor synergistic optimization can substantially enhance both surface quality and processing efficiency in double-sided CMP of molybdenum substrates, providing a process basis for applications in high-power LED devices. Full article

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston’s equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model’s reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm³/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method’s effectiveness. Full article

Electric Discharge Machining (EDM) is a well-established process for fabricating complex geometries from hard materials. However, identifying the influence of process parameters remains challenging and costly due to the stochastic nature of EDM and the expense of experimental validation. Machine Learning (ML) techniques provide an alternative to mitigate these limitations by enabling predictive modeling with reduced experimental effort. This research proposes a generalizable framework employing four ML models to analyze the correlation between EDM inputs and outputs, incorporating 11 levels of cryogenic electrode treatment. Independent variables include electrode material, cryogenic conditions, pulse current, and pulse duration, while performance is assessed through Material Removal Rate (MRR) and Electrode Wear Rate (EWR). The results demonstrate that Random Forest (RF) and Artificial Neural Networks (ANNs) achieve superior predictive performance compared to alternative approaches, improving the $R^2$ metric from 0.973 to 0.9956 for EWR in the case of an ANN and from 0.980 to 0.9943 for RF with MRR, compared with previous work in the literature and the best methods across 30 executions. Both models consistently yield high predictive accuracy, with $R^2$ values ranging from 0.9936 to 0.9979 in training and testing datasets. Furthermore, ANN significantly reduces mean squared error, decreasing EWR prediction error from 5.79 to 0.68 and MRR error from 122.75 to 35.89. This research contributes to a deeper understanding of EDM process dynamics. Full article

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when only limited experimental data are available. In this study, a combined Taguchi design and Gaussian process regression (GPR) modeling framework is proposed to predict the surface roughness (Ra), material removal rate (MRR), and overcut (OC) in die-sinking EDM. An L18 Taguchi orthogonal array was employed to efficiently design experiments involving discharge current, pulse duration, and electrode material. GPR models with an automatic relevance determination (ARD) radial basis function kernel were developed to capture nonlinear relationships and varying parameter relevance. Model performance was evaluated using strict leave-one-out cross-validation (LOOCV). The developed GPR models achieved low prediction errors, with RMSE (MAE) values of 0.54 µm (0.41 µm) for R_a, 1.56 mm³/min (1.21 mm³/min) for MRR, and 0.0065 mm (0.0055 mm) for OC, corresponding to approximately 9.8%, 5.4%, and 5.9% of the respective response ranges. These results confirm stable and reliable predictive accuracy within the investigated parameter domain. Based on the validated surrogate models, multi-objective optimization was performed to identify Pareto-optimal process conditions, revealing graphite electrodes as the dominant choice within the feasible operating region. The proposed approach demonstrates that accurate and robust prediction of EDM performance can be achieved even with compact experimental datasets, providing a practical tool for process analysis and optimization. Full article

The powder-mixed electric-discharge machining (PMEDM) process has been the focus of researchers for quite some time. This method overcomes the constraints of conventional machining, viz., low material removal rate (MRR) and high surface roughness (SR) in hard-cut materials, tool failure, and a high tool wear ratio (TWR). However, to determine the optimal machining parameter levels for improving MRR, surface finish must be measured during actual experimentation using various parameter levels across different materials. It is a very costly and time-consuming process for industries. However, in the age of Industry 4.0 and artificial intelligence machine learning (AI-ML), it provides an efficient solution to real manufacturing problems when big data is available. In this study, experimentation was conducted on AISI D2 steel using the PMEDM process for SR analysis with different parameters, viz. current, voltage, cycle time (T_On), powder concentration (PC), and duty factor (DF). Moreover, machine learning models were used to predict SR values for selected parameter levels in the PMEDM process. In this research, Gaussian process regression (GPR) with a squared exponential kernel, support vector machines, and ensemble regression models were used for computational analysis. The results of this work showed that Gaussian regression, support vector machine, and ensemble regression achieved 95%, 92%, and 83% accuracy, respectively. The GPR model achieved the best predictive performance among these three models. Full article