This study presents a digital twin of 5-axis milling processes for thin-walled parts to predict and prevent the occurrence of static deflection issues, surface location error due to forced vibration, and regenerative chatter. Driven by real-time machine signals or machining programs, the twin continuously simulates the milling process given an initial workpiece and any 5-axis toolpath. Thanks to the implementation of an automatic mesher and a finite element model generator, the stiffness and dynamics of the part are determined based on its current geometry. This enables the digital twin to predict and visualize in 3D the static deflection, forced vibration, and process stability, allowing optimization of the process and avoidance of these issues. Finally, the twin is validated on a 5-axis machining case of a steel blade, in which a surface location error caused by a resonance is diagnosed and eliminated.
In an unbalanced three-phase system, dq-current control of a grid-tied inverter becomes challenging due to the presence of harmonics. These harmonics originate from fluctuations in the dq-axis signals, which, in turn, result from the unbalance in the corresponding alpha(3 waveforms. This paper introduces a straightforward Clarke transformation featuring minimal adaptive parameters to address this issue. In the proposed method, the amplitude and phase information are extracted using the Fast Fourier Transform (FFT). Unlike the conventional Clarke transformation, where amplitude and phase disturbances persist due to the use of constant coefficients that apply fixed scaling to the input waveforms-thus failing to restore balanced alpha(3 components-the proposed approach effectively mitigates these disturbances. The amplitude imbalance is minimized through waveform normalization, while phase deviations are corrected by deriving orthogonal waveforms via a trigonometric identity that adaptively manipulates the scaling parameters to maintain balance. The effectiveness of the proposed method was validated and compared with the conventional Clarke transformation through simulations under various unbalanced conditions. Furthermore, both transformation methods were implemented within a dq-current control scheme using a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). The results demonstrate that the proposed method achieves a Total Harmonic Distortion (THD) of 2.33% in the absence of low-order harmonics, compared to 13.3% when such harmonics, particularly the 3rd and 5th components, are present.
As the core component in industrial production, the chiller is composed of compressor, condenser, evaporation tower and other components. There are problems such as poor coordination and low heat dissipation efficiency between various components in the chiller, and a coordination method is needed to control them. Based on this, this paper proposes an association rule algorithm to optimize the operation performance of the chiller, and calculate the temperature difference between the compressor, condenser and evaporation tower. Then, the material and structure of the components are integrated to control the power, water volume, and fan to maximize the heat dissipation effect of the chiller. The results show that the compressor and evaporation tower is (10.82%, 3.12%, 0.28%), but under the intervention of the association rule algorithm, the temperature change of the unit is relatively small (14.71%, 15.23%, 14.52%); The thermal decay rate is the highest among the condenser -> evaporation tower (1.23), while under the intervention of the correlation rule algorithm, the thermal attenuation of the condenser and evaporation tower is low (2.071, 0.195); Under the intervention of the association rule algorithm, the energy consumption ratio (80.81%), maximum heat dissipation (1024 KJ/s), minimum heat dissipation point between units (20), and the minimum gain of units (30.02%) of the chiller were significantly better than the previous algorithms (P<0.25, R2=0.32), so the correlation rules effectively optimized the chiller and dynamically improved its performance. Therefore, the association rule algorithm can improve the chiller's operation performance.
A device for real-time monitoring of 3D printer feeding systems was designed and evaluated. The device detects faults by measuring filament flow using rotary encoders at two points in the system: one measures the actual amount of filament fed, and the other measures the expected amount. The percentage discrepancy between these two measurements is then calculated; a significant discrepancy indicates an anomaly in the feeding system that requires correction. The device can be used with any type of 3D printer that employs fused filament fabrication (FFF) technology. The main faults detected are those that prevent the filament from flowing freely, such as low extrusion temperature, heating element failure, roller misalignment, or worn components. The system alerts the user to correct the problem, pause printing, or continue if the fault does not affect model quality. To validate the device's functionality, the monitoring system was installed on a commercial 3D printer, where several faults were intentionally induced. The system successfully detected common failures such as filament runout, nozzle clogging, and extruder temperature faults, confirming its effectiveness even though its diagnostic capability covers a wider range of possible anomalies.
The digital transformation of the insurance sector is advancing through hybrid architectures that integrate distributed ledgers, the Internet of Things (IoT), and artificial intelligence (AI). This paper proposes a hybrid IoT-DLT architecture for parametric insurance systems operating in environments with variable connectivity. The architecture is designed to ensure data verifiability, operational resilience, and regulatory compliance with the General Data Protection Regulation (GDPR) and the Digital Operational Resilience Act (DORA). The approach is validated through a maritime cold-chain monitoring use case for fishing fleets, based on onboard IoT sensors and smart contracts. Simulation results show that the proposed multi-sensor consensus mechanism enables accurate detection of thermal breaches while significantly reducing the number of blockchain transactions by reserving on-chain registration for critical events only. The proposed approach supports distributed, auditable, and operational insurance systems even under intermittent connectivity conditions.
