As Industry 4.0 continues to transform the landscape of modern manufacturing, the integration of intelligent robotic systems has emerged as a pivotal factor in enhancing efficiency, flexibility, and overall productivity. The Integration of intelligent robotic systems within the framework of Industry 4.0 represents a transformative shift in advanced manufacturing systems. The integration of intelligent robotic systems in Industry 4.0 has significantly reduced production costs while simultaneously improving product quality. The intelligent decision-making capabilities of robotic systems in Industry 4.0 have played a pivotal role in minimizing downtime in order to enhance productivity in process of part manufacturing. Intelligent robotic systems in Industry 4.0 has not only increased production efficiency but has also contributed to a more sustainable and eco-friendly manufacturing environment through optimized resource utilization. This review explores the key aspects, benefits, and challenges associated with the deployment of intelligent robotic systems in Industry 4.0. The review analyze the cutting-edge advancements in artificial intelligence, machine learning, and sensor technologies that contribute to the evolution of intelligent robotic systems in Industry 4.0. The discussion extends to emerging trends in intelligent robotic systems including digital twin, blockchain, Internet of Things, artificial intelligent and the integration of advanced analytics for real-time decision support systems. Challenges and considerations surrounding the implementation of intelligent robotic systems in Industry 4.0 are thoroughly examined, ranging from technical hurdles to ethical and societal implications. Finally, the review concludes with a forward-looking perspective on the future trajectory of intelligent robotic systems in Industry 4.0. As a result, the study can provide a roadmap for researchers and industry professionals to navigate the evolving landscape of intelligent robotics in the era of Industry 4.0.

Electroplating at the micro and nano scales is an electrochemical deposition technique, regarded as one additive manufacturing process, to achieve the preparation and surface modification of nano devices. The present review mainly discussed two key inflcuing factors including microelectrode probe manufacturing (the length of tapered needle tipe and diameter of microelectrode tip) and the distance automation control in the micro and nano dimension between microelectrode and substrate. In addition, the mechanism and application of electrochemical etching for different microelectrode tips and automation control systems are focused. Finally, the challenges and prospect in electroplating at micro and nano scales were discussed.

Powder metallurgy is a promising method for gamma titanium aluminides (γ-TiAl) low-pressure turbine blade manufacturing as it generates better mechanical properties. However, the powder metallurgy γ-TiAl has an uneven deformation during the pressing process, making it difficult to align the workpiece to the right position during the machining process. To solve this problem, a structured light measurement-driven adaptive machining method is proposed in this paper for the low-pressure turbine blades with powder metallurgy γ-TiAl. The point cloud of the powder metallurgy workpiece is firstly obtained with structured light measurement. Then, the feature point matching method is proposed for coarse registration of the point cloud of the semi-product with the blade design model. Afterwards, a weighted iterative closest point (ICP) algorithm is applied for fine registration of the position of the point cloud to distribute the machining allowance evenly for better machining quality and efficiency. The experiments show that the proposed method can effectively improve the allocation accuracy and allocation results.

Robotic milling processing has become an important means of advanced manufacturing technology. However, the limited machining accuracy restricts the development of robotic milling processing technology. Errors prediction and compensation are effective means to improve robot accuracy. This paper presents a combined statistical principles and machine learning model that achieves high robot milling errors prediction accuracy, called PSO-ARIMA. It is an Auto-regressive Integrated Moving Average (ARIMA) model with milling force correction that has been optimized by the Particle Swarm Optimization (PSO). Compared to the other five existing algorithms, the proposed method has the highest prediction accuracy. The maximum MAE for pose errors prediction in the four validation tasks is only 0.021 mm and 0.011°, which meets the actual application requirements. It can efficiently and accurately accomplish online prediction of errors to improve the accuracy of robotic milling.

In the conventional mask electrodeposition process, the planar nature of the anode leads to a rapid flow of a large amount of deposition solution over the confined microzone, resulting in difficulties in mass transfer within the microzone and poor quality of microstructure formation. To fabricate high-quality metal microstructures, this study proposes a dynamic electrodeposition method that utilizes a trapezoidal anode to enhance the mass transfer capability within the confined microzone, based on the concept of moving masks. Under the influence of the trapezoidal anode, there is a sudden change in the velocity of the deposition solution above the confined microzone, inducing turbulence within the microzone and enhancing internal mass transfer capability. Both simulation and experimental results validate the feasibility of this method. With the trapezoidal anode, the flatness of the cross-sectional profile of the metal microstructure is improved by 45.2%, and the deposition rate is increased by 44%. Subsequently, process parameters were optimized through orthogonal experiments. Utilizing the optimized parameters, high-quality metal microstructures with a diameter of 200 μm and a height of 360 μm were dynamically deposited. These results demonstrate that the use of a trapezoidal anode to enhance mass transfer within the microzone effectively improves the deposition quality and rate of metal microstructures, providing a practical solution to the difficulties associated with mass transfer in confined microzones.

In medical rehabilitation, point clouds algorithms, as practical tools for 3D model analysis, possess significant advantages in orthoses processing and design. In this paper, an orthosis point clouds classification network with downsampling and data augmentation modules was proposed to classify a large number of the 3D orthoses with complex surfaces before inputting them into an expert template library which uses the previous orthoses to help the customized orthosis design for new patients. Initially, the effects of three types of the basic network were investigated to obtain the optimum basic network. Then, two kinds of data augmentation modules and four kinds of down-sampling modules were respectively added to the optimum basic network in order to obtain the best comprehensive network. The experimental results show that the basic classification with appropriate down sampling and data augmenta3tion methods can effectively address the time-consuming and low accuracy of the existing networks and reduce the orthosis classification time by 12.83% and improves the classification accuracy by 4.29% on average.