Aluminium nitride high-temperature co-fired ceramic (AlN HTCC) is a promising substrate material for electronic packaging, and the fabrication of microchannel arrays on its backside can significantly enhance its heat dissipation performance. However, the high hardness and brittleness of AlN present considerable challenges in the machining of microstructures. Therefore, this study proposes a hydrochloric film-assisted laser processing (HFALP) technique. Initially, a systematic analysis was conducted on the multi-factor energy attenuation mechanism of laser beams. Based on this, a temperature distribution model capable of accurately predicting the line-etching morphology was developed. Furthermore, through transient observations, flow field simulations, and comparative experiments under different liquid phases, this study reveals, for the first time, the intrinsic relationship between the cavitation bubble dynamic behaviour, laser, and liquid layer. This leads to the clarification of two material removal mechanisms, tunnel channel ablation and cavitation ablation, induced by multi-mechanism synergy within the ternary system of “laser-reactive medium-thermally-active material”. Finally, narrow microgrooves with an aspect ratio of 8.6:1 were successfully fabricated on AlN by coupling the two ablation mechanisms. Compared with laser chemical milling, HFALP improved the machining depth, mean deviation of the contour, and processing efficiency by 41.24%, 30.11%, and 3845.2%, respectively, and achieved stable fabrication of microchannel array structures with a profile deviation of only 0.1%. This study not only provides a reliable process for the thermal management application of AlN but also, through successful validation on other reactive materials, establishes a universal theoretical framework for “reactive medium-assisted laser processing”. This offers reusable technical pathways and mechanistic support for efficient and high-quality machining of various thermally-active materials.

Aerostatic spindles are extensively employed in ultra-precision machining owing to their superior rotational accuracy, negligible wear, and low thermal distortion. However, during practical machining operations, the combined gravitational effects of the workpiece and fixture inevitably induce journal misalignment, which alters the air film pressure distribution and degrades spindle rotational accuracy, particularly under variable external load conditions. In this study, a comprehensive dynamic model of an aerostatic spindle is developed by incorporating journal misalignment, nonlinear air film forces, rotor unbalance, and alternating sinusoidal loads. The transient compressible Reynolds equation is coupled with the rotor dynamic equations, and the time-varying spindle axis trajectory is numerically solved using an iterative Euler scheme. The influences of journal misalignment angle, rotational speed, rotor mass eccentricity, and alternating load amplitude and frequency on spindle rotational accuracy are systematically investigated through time-domain trajectory analysis and statistical indicators. The results demonstrate that journal misalignment leads to a pronounced axial non-uniformity in air film pressure and a reduction in throttle orifice outlet pressure, thereby significantly increasing spindle vibration amplitudes. Under misalignment conditions, increases in rotational speed and rotor mass eccentricity further amplify vibration responses and deteriorate rotational accuracy. When subjected to alternating sinusoidal loads, the spindle exhibits enhanced vibration amplitudes and trajectory distortion, while non-synchronous excitation frequencies induce irregular axis trajectories and reduced dynamic stability. The proposed model provides a quantitative and efficient approach for predicting spindle rotational accuracy under realistic machining conditions, offering valuable guidance for the design, load evaluation, and performance optimization of ultra-precision aerostatic spindle systems.

In recent years, the digitalization and intelligence of the information fusion in manufacturing process have gradually become a research hotspot for smart manufacturing. The material reduction involved cutting technology triggered the impact on the consumption of production resources and the environment. In terms of difficult-to-machine titanium alloy parts, the tool condition not only affects the energy consumption of machining center, but also closely influences the milling quality and efficiency. Currently, the design and production optimization of tool ontology approximates to a bottleneck. Therefore, the intelligent fusion of cutting information and accurate prediction of the tool wear condition have been one of the key ways to further achieve sustainable manufacturing. Aiming at the problems of complex modelling of the traditional tool wear mechanism, strong reliance on basic experiments, high data cost and poor model interpretability, this work innovatively proposes an interpretable foreknowledge framework for multimodal sensing fusion. Furthermore, a physical-knowledge inspired time-varying tool wear prediction model is established based on few shot samples with unbalanced data distribution from realistic scenery. Based on the monitoring cutting force, vibration and acoustic emission signals, the feature extraction of multimodal signal is realized. Then, using the tool wear distribution and its gradient as classification criteria, an unsupervised learning algorithm is adopted to achieve multi-cluster centralization of tool wear. In addition, an interpretable strategy based on Shapley's superposition is applied to capture and characterize the key feature information of various tool wear stages, to improve the interpretability and transparency of foreknowledge model. Compared with the benchmark models of other four advanced algorithms, the predictive performance of the proposed model proposed is improved by 77.70%, 70.76%, and 80.38% in MAE, RMSE, and MAPE index. Furthermore, the key impact monitoring features of three tool wear stages are identified from the macro and micro perspective, as well as the quantitative characterization of the evolution from feature driving function on tool wear prediction model. Therefore, the proposed interpretable foreknowledge model in this work provides a potential method for information fusion in the sustainable manufacturing.

Copper-based diamonds have the advantages of high thermal conductivity and a thermal expansion coefficient that matches that of chips, and they are widely used in various electronic packaging heat dissipation such as aerospace. Although it is difficult and costly to improve the heat dissipation capacity in terms of materials preparation, the fabrication of microstructures on the surface is the key to solving this problem. In this study, laser and micro-grinding (LAG) is proposed to efficiently remove difficult-to-machine copper-based diamonds. A nanosecond ultraviolet laser with a top-hat laser beam was used to complete the rough machining. Next, the ablation effect of copper-based diamond under variations laser fluence, scanning speed, and filling pitch were explored. Finally, the finish machining was realized by a grinding test. The results showed that the diamond particles in the composites began to graphitize at laser fluences greater than 124.14 J/cm2. Moreover, graphite stacking occurred at low scanning speeds, and the filling pitch had a small effect on the modification. The ablation depth was most affected by the scanning speed; it was more than 160 mu m at 1 mm/s. The optimal laser process parameters of 162.34 J/cm2, 5 mm/s, and 2 mu m were obtained from the comprehensive analysis, and the etching depth was 182.3 mu m at these settings. Using this result as the basis for the grinding test, a surface roughness of only 0.621 mu m was obtained, which is 54.1 % lower than that of the original surface. Furthermore, tool fracture was avoided and tool wear was significantly less than when the single grinding method was used.

This paper presents a novel two-step laser chemical milling approach to prepare microstructure on copper-based diamonds. High laser fluence with chemical milling was utilised for rough machining, and then the finishing process was completed by rapid scanning and low laser fluence. The results depict that laser processing parameters significantly affect the surface morphology and quality; laser chemical milling was able to remove the recast layer that formed on the surface after laser ablation. The laser fluence of 159.24 J/cm2, scanning speed of 5 mm/s, and filling pitch of 4 mu m were the most effective parameters for the desired profile and material removal rate (MRR) during rough machining. The composite material exhibited an ablation threshold of 31.85 J/cm2, with a laser scanning speed of 15 mm/s and a filling pitch of 2 mu m. After finishing, the surface roughness (Sa) reached a minimum value of 1.31 mu m. Additionally, the heat-affected zone had a thickness of just 10 mu m. The surface exhibited a characteristic peak G and minimal graphitisation when the composite underwent four rounds of laser scanning and chemical milling.

Monitoring tool wear is vital in the cutting process as it guarantees the quality production of intricate aerospace components and boosts manufacturing efficiency. However, traditional monitoring methods may fall short when confronted with varying cutting conditions and a scarcity of data. To address this, the paper introduces an innovative algorithm known as MKWADTL (Model and Knowledge-Guided Multi-Expert Weighted Adversarial Deep Transfer Learning). The primary aim of MKWADTL is to refine the main network's performance by tapping into the inherent knowledge embedded within the cutting parameters. Moreover, the algorithm capitalizes on the correlation between force signals and the variance in tool wear across different time frames to formulate a loss function that is informed by physical principles. In addition, the paper puts forward a multi-expert weighted adversarial structure. Through this framework, multiple experts can independently learn and identify various signal characteristics. Subsequently, the features extracted by these experts are integrated to ensure more precise data feature extraction, facilitating the monitoring of tool wear across a spectrum of processing environments. The MKWADTL algorithm's exceptional accuracy in monitoring is exemplified on the customcrafted dataset, the NUAA dataset and the NASA dataset.

Diamond encounter significant difficulties in contact with ferrous metals due to severe mechanochemical wear, restricting their practical utility. Ultrasonic vibrations, cryogenics, and inert gas shielding reduce wear, but their high costs and extensive modification requirements limit industrial use. This work proposes a surface modification technique for transforming a diamond surface with a few layers of graphene (DfG) covalently bonded together on the surface to enhance its endurance. In this process, a nanosecond laser was employed, instantly transformed diamond sp3 into sp2 graphite. The sp2 graphite was then exfoliated using electrochemical exfoliation to achieve graphene layer. The optimal surface was achieved under parameters of 10 V, C 1.0 M of (NH4)2SO4, and T of 30 min at pH 9 of the electrolyte. The modified surface (DfG) exhibited outstanding lubrication properties and strong resistance to mechanical wear. Under normal loads ranging from 1 to 8 N and enduring 15,000 cycles against a GCr15, DfG significantly suppressed COF (44-63 %), noise friction (15.6317.65 %), and relative wear (78.4-81 %) while maintaining its structural stability throughout the testing phase. This research aims to improve the durability of diamond surfaces and can enable uniform application on both flat and non-planar surfaces, expanding its industrial utilities.

Polyetheretherketone (PEEK) is the specialty plastic that is widely used in space satellites. Due to the poor surface quality of 3D-printed PEEK, the silver lines printed on the surface will diffuse after curing. To address these issues, this paper proposed a liquid nitrogen cooling assisted micro-milling PEEK material, and analyzed the influence of three different temperatures (high temperature 140 ℃, normal temperature 25 ℃, low temperature -196 ℃) on the hardness of PEEK, and optimized the milling parameters (feed per tooth) through single factor experiments to reduce the surface roughness, and finally achieved the purpose of reducing the diffusion degree of silver lines. The result demonstrated liquid nitrogen could reduce the intermolecular force, increase the hardness and reduce surface roughness. When the feed per tooth was 1.5 μm/z, the surface roughness was reduced to minimum (0.2234 μm) at low temperature, and the diffusion degree of silver lines were greatly reduced.

ObjectivesChemical vapor deposition (CVD) diamond has excellent material properties and a wide range of application prospects, but due to its high hardness, brittleness and chemical inertness, microstructure processing on the surface and inside is extremely difficult. Based on the advantages of high precision, high efficiency and easy automation of laser processing, this paper adopts the ultraviolet nanosecond laser for CVD single diamond etching, and combines the laser line etching energy model with the basic research on the law of laser processing and etching principle.MethodsA UV nanosecond laser is used to carry out basic research on the laws of laser processing and etching principle through the ablation threshold test, laser energy modeling and line etching test, scanning electron microscope observation and energy spectrum analysis test. The purpose of the ablation threshold test is to obtain a suitable range of processing parameters, set the laser scanning speed of 1 mm/s, the number of scans for 1 time, the laser repetition frequency range from 20 to 50 kHz with 10 kHz increment, and the laser power range from 1.2 to 12.0 W with 1.2 W increment, and observe the erosion situation by engraved lines. After obtaining a suitable range of processing parameters, a laser energy density model is established in which the main variables are laser power and scanning speed. Changes in the model are observed by varying different combinations of parameters, and the processing law is predicted and verified by combining with the actual line etching test. The test situation is mainly through the scanning electron microscope observation and record test data, one of the line etching groove of the internal and peripheral energy spectrum analysis, through the comparison of elemental changes and combined with the literature on the principle of ablation to carry out certain analysis. ResultsFrom the results of the ablation threshold test, when the laser repetition frequency is 20 kHz, the etching traces can be produced in the range of power 1.2-12.0 W, when the repetition frequency is 30 kHz, the etching traces start to be produced when the power is increased to 7.2 W, and when the repetition frequency is 40 and 50 kHz, the etching traces are not produced in the range of power 1.2-12.0 W, therefore, in order to obtain stable etching results, fixed the laser repetition frequency of 20 kHz in the subsequent line etching test. Therefore, in order to obtain stable etching results, the laser repetition frequency is fixed at 20 kHz in the subsequent line etching test, and the results of the line etching test can be seen from the side of the CVD diamond, the groove basically appears as a “V” shape, and in combination with the line etching model of the laser, the energy density of the laser shows a high distribution in the middle and low distribution around the edges, i.e., the center of the spot is high and the edges are low. Furthermore, the energy at the center of the spot is high, and the energy at the edge is low. In the one-factor test of power, when the power is 1.2, 3.0, 5.4, 10.8 W, the width of the line etching groove is 39.8, 39.8, 41.0, 38.8 μm, and the depth is 35.7, 41.1, 42.1, 57.2 μm, respectively, and the width and depth of the diamond line etching groove increase with the increase of the laser power. In the one-factor test of scanning speed, when the scanning speed is 3, 13, 21 and 29 mm/s, the widths of the wire-etched grooves are 39.5, 39.9, 35.6 and 26.3 μm, and the depths are 77.6, 37.9, 22.3 and 18.0 μm, and the widths as well as the depths of the grooves gradually decrease with the scanning speed increasing.The results of EDS analysis show that, compared with the unfinished area, the proportions of C elements in the grooves' interior, sidewalls and peripheral areas show a decrease in the proportion of C elements and an increase in the proportion of N and O elements compared to the unprocessed area, and they are nearly the same. Conclusions(1) Combined with the line etching energy model, the increase in power leads to an increase in the energy density of the laser, and at the same time, the peak energy increases, increasing the degree of ablation of the material, resulting in deepening the depth of the groove, in addition, when increasing the laser power, the height of the model increases more than the increase in the magnitude of the edges, and therefore it can be observed that the width and depth of the diamond line etching groove increases with the increase in laser power, but the impact on the depth is greater than the impact on the width phenomenon. Scanning speed increase leads to increase the spacing between individual laser pulses, the amount of superposition between the pulses and the superposition area decreases, making the energy at the superposition decreases, which leads to a reduction in the peak value of the energy at the superposition, in addition, the scanning speed increase also leads to the reduction of the number of laser pulses irradiated in the unit area, which, under the combined effect, reduces the depth of the wire etching groove. (2) EDS analysis shows that, compared with the unprocessed area, the C element content in the processed area is reduced, and the N and O element content is increased, so it can be initially judged that the mechanism of nanosecond laser processing of single-crystal diamond is the phase transformation of diamond at high temperatures, graphite oxidation, and sputtering of graphite and heterogeneous compounds.

Diamond coatings possess numerous excellent properties, making them desirable materials for high-performance surface applications. However, without a revolutionary surface modification method, the surface roughness and friction behavior of diamond coatings can impede their ability to meet the demanding requirements of advanced engineering surfaces. This study proposed the thermal stress control at coating interfaces and demonstrated a novel process of precise graphenization on conventional diamond coatings surface through laser induction and mechanical cleavage, without causing damage to the metal substrate. Through experiments and simulations, the influence mechanism of surface graphitization and interfacial thermal stress was elucidated, ultimately enabling rapid conversion of the diamond coating surface to graphene while controlling the coating’s thickness and roughness. Compared to the original diamond coatings, the obtained surfaces exhibited a 63%–72% reduction in friction coefficients, all of which were below 0.1, with a minimum of 0.06, and a 59%–67% decrease in specific wear rates. Moreover, adhesive wear in the friction counterpart was significantly inhibited, resulting in a reduction in wear by 49%–83%. This demonstrated a significant improvement in lubrication and inhibition of mechanochemical wear properties. This study provides an effective and cost-efficient avenue to overcome the application bottleneck of engineered diamond surfaces, with the potential to significantly enhance the performance and expand the application range of diamond-coated components.

The widespread application of tool condition monitoring technology in practical manufacturing processes cannot be separated from the development of wireless monitoring technology. However, most existing toolholder-type wireless monitoring technologies alter the original structure, which may result in reduced stiffness, significant cost increases, or diminished spindle compatibility. To address this issue, this study proposes an Intelligent Wireless Tool Condition Monitoring (IWTCM) system composed of an independently developed monitoring ring and a deep-learning model.The developed monitoring ring acquisition module acquires tool shank vibration signals with a power consumption of only 0.458 W. The monitoring ring housing design, based on a chuck-type structure, can clamp onto toolholders with diameters ranging from 40 to 80 mm. Reliability tests demonstrate that the proposed monitoring ring output is highly comparable to the output of commercial vibration-signal sensors. Additionally, the monitoring ring has been verified for dynamic balancing. The tool wear condition recognition model built based on the Convolutional Neural Networks - Long Short Term Memory (CNN-LSTM) classical deep learning algorithm uses vibration data collected from the monitoring ring as input and recognition accuracy can reach 100 % in the test set, which verifies the excellent performance of the proposed IWTCM system. This study further developed a tool condition monitoring software that bridges the gap in such software. Based on the principle of multi-threading, the monitoring software realizes serial communication, data saving, data visualization, and tool wear condition recognition.

Tool wear is critically important for the optimization of cutting parameters. However, the increasing nature of tool wear presents challenges to traditional meta-heuristic cutting parameter optimization methods. To address this issue, we propose an innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account. More specifically, we use the Markov Decision Process to simulate the optimization process of cutting parameters. Firstly, an innovative deep transfer learning algorithm is used for monitoring tool wear. With the progress of tool wear, the proximal policy optimization method of the transformer with multi-head attention mechanism interacts with the processing environment through a process of trial and error, and accumulates a wealth of experience in selecting cutting parameters through the reward function. The deep reinforcement learning model has quickly discern the best cutting parameters, relying on real-time tool wear value. The experimental results show that the proposed method outperforms other algorithms.

Inconel 718 is widely used in aerospace due to thermomechanical properties, but machining causes rapid tool wear. This study develops a covalent diamond-nanographite-graphene (CDGG) tool using nanosecond lasers and flywheel cleavage to convert sp3 diamond into sp2 graphene, welds it onto a carbide handle, and sharpens cutting edges. The friction-wear tests and cutting experiments show that CDGG tool's apparent friction coefficient is 49-59 % lower than diamond tool, rake face wear depth is 55-65 % lower than diamond tool, 75-85 % lower than ceramic tool. Graphene forms self-lubricating layers, minimizes wear, and enhances Inconel 718 machining efficiency and tool durability. (c) 2025 CIRP. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Aluminium nitride (AlN) ceramic is a typically difficult-to-machine material used in electronic packaging. Laser and chemical milling enhanced micro milling (LCMEM), a high-quality and efficient processing method for AlN, is proposed in this study. Rough machining is completed by repeated alternating laser ablation and chemical removal; the final finish is achieved by micro milling (MM). To achieve precise laser and chemical milling parameters based on the target structure, a laser and chemical milling (LCM) prediction model is established and verified; this can be used to predict the morphology of the grooves after LCM. The change trend of the grooves after LCM with the laser parameters is investigated. It was found that LCMEM can improve the surface quality by 60%, and an optimal finishing surface with a surface roughness Ra value of 54nm can be obtained when the feed per tooth is 0.4 μm/z. Lastly, this study uses LCMEM to produce a high aspect ratio groove with aspect ratio of 2.5 and depth of 1250 μm. Compared with conventional MM, LCMEM reduces the allowance of MM by 71.95%, improves the groove accuracy, and reduces tool wear.

Diamond tools experience severe chemical wear when machining ferrous metals, which hinders their practical applications. In order to improve the wear resistance of diamond cutting tools, in the diamond-graphite strong covalent structure prepared by laser induced solid-phase diffusion, carbon nanosheets (CNS) can be obtained by electrochemical stripping of the graphite layer onto the diamond matrix, which provides a new way to improve the limitations of diamond tools in application. After 14,400 cycles of reciprocating sliding against the GCr15 ball at a normal load of 2–8 N, friction was reduced by 45.9 %–65.6 % with high durability. The oxygen content is reduced by an order of magnitude during this process, suggesting that the CNS can prevent oxidation behavior at the sliding interface. The bare diamond had a relative wear rate of 4.1–15.4 times that of the CNS. It showed competitive inhibition of mechanochemical wear. Our work provides a convenient and green method of preparing in-situ CNS covalently bonded on a diamond surface, extending the way for the prospect of carbon materials.

The fabrication of superhard micro milling cutters with extreme sharpness is highly demanded to meet the strict requirements of micro structures in terms of machining accuracy, surface roughness and burr height. In this study, nanosecond laser induced graphitization assisted grinding was proposed by considering machining efficiency and machining quality simultaneously for chemical vapor deposited (CVD) diamond micro flat-end milling cutters. An optimal cut-section was obtained by adopting moderate laser cutting energy and avoiding unstable thermodynamic coupling effect. The following laser induced graphitization mechanism on the cut-section was studied by analysing the different graphitization behaviours. The extensive graphitization caused sintered and banded graphite while the weak graphitization could not eliminate the defects caused by laser cutting. The uniform and dense graphitization resulted from appropriate energy distribution contributed to the smooth transition layer which was the significant factor in subsequent grinding. Moreover, the subsequent grinding behaviours of different transition layers were investigated, the grooved defects on transition layer caused the unstable grinding and fracture removal model, leading to the cracked ground surface and blunt cutting edge. Nevertheless, the grinding model of smooth transition layer was scratching without transcrystalline cracks, resulting in the smooth ground surface and sharp cutting edge. Thus, the laser parameters were finally optimized based on the feedback. Furthermore, the even graphitization, undamaged transition layer, scratching, as well as less material stress resulted in the extreme sharpness. In this way, the CVD diamond micro flat-end milling cutter (CVDM) with a diameter of 200 μm, surface roughness Sa of 30nm, aspect ratio of 3, and edge radius of around 0.12 μm was prepared, the extreme sharpness was indicated by comparing with the edge radius of 1–5 μm reported heretofore. Finally, the micro grooves with surface roughness Sa of 17.8nm and tiny burrs were machined on Ti-6Al-4V by the homemade CVDM, which thereby indicated the outstanding cutting performance of the CVDM in micro-milling.

The efficient monitoring of tool wear is crucial in ensuring precise part manufacturing and enhancing machining efficiency during the cutting process. However, the presence of variations in cutting conditions, especially when significant disparities in feature distributions exist between datasets, renders traditional single-source transfer learning methods inadequate for achieving effective monitoring. To address this challenge effectively, this paper proposes a novel algorithm called MS-MMEDTL (Multisource Multibranch Metric Ensemble Deep Transfer Learning). In the MS-MMEDTL algorithm, a multibranch architecture is initially employed to extract distinctive features from diverse source and target datasets. Subsequently, a metric learning method is applied to enhance the discriminability of features obtained from target data samples. To align the feature distributions of different source and target datasets, the Maximum Mean Discrepancy (MMD) algorithm is utilized. Additionally, prediction results from each branch are assigned specific weights and then fused through weighted summation. The experimental results clearly illustrate that the MS-MMEDTL algorithm surpasses other multi-source transfer learning algorithms in terms of accuracy on the milling dataset. These findings underscore the effectiveness and potential of the MS-MMEDTL algorithm, emphasizing its significance in the design and application of tool wear monitoring algorithms.

Accurately monitoring tool wear during the cutting process is crucial for ensuring the precision manufacturing of components and enhancing machining efficiency. However, even under the same cutting conditions, there are differences in the feature distributions between offline data and online data, leading to poor monitoring effectiveness of traditional offline transfer learning algorithms. To effectively address this challenge, this paper proposes an innovative algorithm named MS-LARODTL (Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning). In the MS-LARODTL algorithm, a lightweight convolution layer design is initially adopted, resulting in a reduction of 86.64 % in convolution time to enhance the algorithm's execution speed. Subsequently, online data features are replayed to adapt batch normalization layer parameters through a replay buffer, thereby improving the monitoring accuracy of the algorithm. Lastly, by replaying source data and online data features and leveraging Maximum Mean Discrepancy to align data features, the algorithm's monitoring accuracy is further enhanced. Experimental results distinctly demonstrate that the MS-LARODTL algorithm surpasses other multi-source transfer learning methods across all evaluation metrics on the self-constructed CNC milling dataset. These findings underscore the effectiveness and potential of the MS-LARODTL algorithm, emphasizing its importance in the design and application of tool wear monitoring algorithms.

Processing microchannels inside laminated aluminum nitride high-temperature co-fired ceramics (AlN HTCC) packaging, a typical difficult-to-cut ceramic, can effectively solve the heat-dissipation problem of integrated chips used in smart skin. In order to improve the processing efficiency and quality of AlN, the machinability of AlN after laser chemical milling (LCM) was studied through the milling force, machined surface quality, surface defects, formation mechanism, and tool wear. This study established a milling force model that can predict the milling forces of AlN and analyses the reasons for the improvements in the milling force based on experimental data and predicted data. The results from the model and experiments demonstrated that the milling force of the laser chemical milling assisted micro milling (LCAMM) decreased by 85%–90% and 85%–95%, respectively, due to the amount of removal of a single edge was more uniform and the actual inclination angle increased during the cutting process in LCAMM. Moreover, the machined surface quality improved by 65%–76% after LCM because of less tool wear, fewer downward-propagating cracks generated during each feed, and the surface removal mode transformed from intergranular fracture to transgranular fracture, which effectively reducing tool wear and improving tool life. Finally, when feed per tooth and depth of cut were 0.4 μm/z and 5 μm, the optimal machined surface quality was obtained, with a roughness of 64.6 nm Therefore, milling after LCM can improve the machinability of AlN and providing a feasibility for the high-quality and efficient machining of microchannels.