The absence of standardized evaluation methodologies for single-layer dimensional accuracy significantly hinders the broader implementation of direct ink writing (DIW) technology. Addressing the critical need for precision non-contact assessment in DIW fabrication, this study develops a novel machine vision-based framework for dimensional accuracy evaluation. The methodology encompasses three key phases: (1) establishment of an optimized hardware configuration with integrated image processing algorithms; (2) comprehensive investigation of camera calibration protocols, advanced image preprocessing techniques, and high-precision contour extraction methods; and (3) development of an iterative closest point (ICP) algorithm-enhanced evaluation system. The experimental results demonstrate that our machine vision system achieves 0.04 mm × 0.04 mm spatial resolution with the ICP convergence threshold optimized to 0.001 mm. The proposed method shows an 80% improvement in measurement accuracy (0.001 mm) compared to conventional approaches. Process parameter optimization experiments validated the system’s effectiveness, showing at least 76.3% enhancement in printed layer dimensional accuracy. This non-contact evaluation solution establishes a robust framework for quantitative quality control in DIW applications, providing critical insights for process optimization and standardization efforts in additive manufacturing.

Deep-sea cages are highly susceptible to biofouling due to long-term seawater immersion, which promotes the attachment and growth of marine organisms on nets, significantly reducing fish survival. To address this issue, this study explores the use of low-pressure abrasive water jets (LPAWJs) for cage fouling removal through numerical simulation. Based on a Box-Behnken response surface design, nozzle inlet pressure X₁, nozzle outlet diameter X₂, and target distance X₃ were selected as optimization parameters. The peak jet impact force Z₁, stable jet impact force Z₂, peak abrasive water jet velocity Z₃, and peak abrasive particle velocity Z₄ were chosen as evaluation indicators to characterize the jet’s instantaneous impact ability, sustained action ability, and dynamic particle behavior. Using the entropy method, weights for each indicator were determined, and the jet’s overall removal capability was calculated. A regression model was developed by integrating numerical simulation with the response surface methodology (RSM), and the optimal parameter combination was identified as X₁ = 4.5 MPa, X₂ = 10 mm, and X₃ = 205.396 mm. Compared with the poorest experimental condition (Condition 1), the jet’s overall removal capability obtained under the optimal parameter combination increases by 101.35%. Experimental validation further confirms that the optimized parameters yield the best oyster-removal performance of the low-pressure abrasive jet, with the average removal rate improving by 100.55% relative to Condition 1. The methodology and results of this study provide a theoretical foundation and technical reference for the design and optimization of automated net-cleaning systems or net-cleaning robots equipped with low-pressure abrasive jets. By integrating the proposed model and operating parameters, future robotic systems will be able to predict and dynamically adjust jet conditions according to fouling characteristics, thereby improving the efficiency, cost-effectiveness, and sustainability of maintenance operations in marine aquaculture.

Deep-sea cages are highly susceptible to biofouling due to long-term seawater immersion, which promotes the attachment and growth of marine organisms on nets, significantly reducing fish survival. To address this issue, this study explores the use of low-pressure abrasive-water jets (LPAWJ) for cage fouling removal through numerical simulation. Based on a Box-Behnken response surface design, nozzle inlet pressure X1, nozzle outlet diameter X2, and target distance X3 were selected as optimization parameters. The peak jet impact force Z1, stable jet impact force Z2, peak abrasive-water jet velocity Z3, and peak abrasive particle velocity Z4 were chosen as evaluation indicators to characterize the jet’s instantaneous impact ability, sustained action ability, and dynamic particle behavior. Using the entropy method, weights for each indicator were determined, and the jet’s overall removal capability was calculated. A regression model was developed by integrating numerical simulation with the response surface methodology (RSM), and the optimal parameter combination was identified as X1 = 4.5 MPa, X2 = 10 mm, and X3 = 205.396 mm. Compared with the poorest experimental condition (Condition 1), the jet’s overall removal capability obtained under the optimal parameter combination increases by 101.35%. Experimental validation further confirms that the optimized parameters yield the best oyster-removal performance of the low-pressure abrasive jet, with the average removal rate improving by 100.55% relative to Condition 1. The methodology and results of this study provide a theoretical foundation and technical reference for the design and optimization of automated net-cleaning systems or net-cleaning robots equipped with low-pressure abrasive jets. By integrating the proposed model and operating parameters, future robotic systems will be able to predict and dynamically adjust jet conditions according to fouling characteristics, thereby improving the efficiency, cost-effectiveness, and sustainability of maintenance operations in marine aquaculture.

This study presents a pulley-buoy-accelerated linear wave power generation system and verifies its feasibility and effectiveness through experimental research. Compared with traditional wave power generation systems that rely on three-stage energy conversion, the proposed system eliminates intermediate energy transfer and conversion links, enabling direct extraction of electrical energy from wave-induced motion. Additionally, by incorporating a pulley assembly, the system amplifies the buoy’s motion speed. This enhancement boosts the power output of the linear generator and improves the system’s overall wave energy conversion efficiency. Under laboratory conditions, a small-scale prototype of the system and a swing-type wave generator were constructed. Experimental tests were conducted to examine how three key factors influence the system’s power generation performance: the number of stator coils, wave conditions (wave height and wavelength), and buoy size. The results indicate that three measures can effectively improve both the wave energy conversion efficiency and power generation performance of the pulley-buoy-accelerated system: increasing the number of stator coils, increasing wave height and wavelength, and moderately enlarging the buoy size. These findings offer valuable insights for the practical application and efficient operation of wave power generation systems.