Search Results (15)

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

In modern manufacturing, achieving high-speed laser cutting requires advanced robotic trajectory planning for smoothness and collision avoidance. Poorly optimized motion can cause frequent velocity changes, leading to mechanical vibrations that shorten machine service life. This study presents an innovative trajectory optimization approach for laser cutting machines equipped with a redundant standoff axis. A B-spline-based analytical model formulates rotational axes trajectories as quadratic programming problems to minimize jerk (the rate of acceleration change) under machining accuracy and kinematic constraints. Additionally, an M path, represented by the wrist center’s trajectory, refines translational axes by adjusting the standoff axis through a similar optimization model, thereby reducing mechanical stress. Collision avoidance is ensured through a concurrent iterative optimization process, considering the feasible domains of representative 3D geometric tool orientations. Simulation experiments on a complex B-pillar workpiece demonstrate the framework’s effectiveness, clearly indicating significant reductions in jerk and improved trajectory smoothness for both rotational and translational axes compared with conventional methods and a prior approach. This work advances high-speed machining capabilities by offering a novel, robust solution that leverages redundant structures to further improve trajectory smoothness and reliability in demanding industrial applications. Full article

Multi-island cavities are common and complex features in structural parts of the aerospace, energy, and power fields. The processing is hindered by low programming efficiency and a strong dependence on the experience of process engineers. In response to these challenges, this paper proposes a highly efficient and robust contour-parallel tool path planning method aimed at improving the rough machining efficiency and quality of multi-island cavities. The method decomposes the complex cavity into multiple sub-regions based on angular geometric features. Subsequently, a closed boundary is formed by connecting the islands with the outer contour using the bridge algorithm. On this base, the method applies rule-based criteria to assess the validity of offset intersections and extracts valid closed loops through point tracing, effectively mitigating both local and global interferences. This approach guarantees the generation of smooth and stable contour-parallel tool paths. The tool path experiments on multiple multi-island cavities demonstrate that the proposed method is capable of automatically generating continuous, interference-free, and residue-free machining paths, thus significantly enhancing machining efficiency and surface quality. Full article

Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data. Full article

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques. Full article

Floods are a natural phenomenon that cause damage to structures and property as well as negatively affect human life. Assessing the extent, speed, power, and depth of flooding has always been a challenge for water resource planners. This research developed a hydraulic simulation model for the Cubanicay and Bélico urban rivers embedded in the city of Santa Clara, Cuba. The methodology was based on a one-dimensional model of the Hydrological Engineering River Analysis System (HEC-RAS) and GIS-based methods. The HEC-RAS model (Beta) and three modeling flood tests for scenarios of 1% (100 years), 2% (50 years), and 10% (10 years) of probability for hydrometeorological events were analyzed. Bank lines, flow path lines, and cross-section cut lines were extracted from Digital Elevation Models. Manning’s roughness coefficients were considered for the channel morphology and soil typology. The flood Beta model results were accurate with a difference of ±0.10 m considering the water footprint found in the field. The results showed that the areas near the control section 2 + 87 presented a high risk of flooding. The flood limit map for urban areas could be an important tool for researchers, planners, and local governments for risk assessment and to develop evacuation plans and flood mitigation strategies in order to reduce human and economic losses during a flood. Full article

In order to improve the automatic level for laser-cladding repair of high value industrial equipment, such as polycrystalline diamond compact bit (PDC bit) applied in oil industry, a universal scheme of integrated automatic system for repairing is proposed in this paper, and the basic functional modules together with the executing order according to which each module runs are defined. There are two main technical points, i.e.,inspection and repairing, that need to be realized for such integrated automatic system. Therefore, according to the proposed scheme and the existing instruments, a dual-robot system, which includes two KUKA industrial robots, is adopted as the technological implementation, where one robot is used to carry a 3D scanner to reconstruct the PDC bit to realize inspection while the other is used to hold the laser to melt the special powder flowing to the damaged region of the bit to complete cladding. To realize automatic running of the whole integrated system, a hand-eye calibration method, namely three-point calibration, is then proposed, by which coordinates of point cloud of the damaged PDC bit detected by 3D scanner can be transformed to those of the coordinate system of the robot with the laser, so that the cladding path planned via cutting slice of the damaged region of the PDC bit in the upper computer software, the key of the integrated system developed by QT programming tool, can be tracked by laser head and then the damaged part of the PDC bit can be repaired. Finally, a laser-cladding experiment for repairing PDC bit is carried out and the feasibility of the proposed scheme of the integrated automatic system and the effectiveness of the dual-robot system implemented via KUKA robots are verified, According to existing literature, no papers about such integrated system for automatic laser cladding repair have been published. Full article

This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public–private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance. Full article

While curved surface microstructures have wide applications in optical components and devices, how to achieve high machining accuracy of the microstructures is crucial for their applications. In the present work, we fabricate sinusoidal modulation microstructures on a curved copper surface by ultra-precision diamond cutting, with the combination of a rotary B-axis and a fast tool servo system. Specifically, tool path planning, together with the consideration of a curved, sinusoidal surface meshing and tool tip arc segmentation compensation, is carried out. Preliminary cutting experiments are firstly carried out on a homemade four-axis ultra-precision lathe, which demonstrates the advantages of additionally applying the rotary B-axis in suppressing burr formations and over-cutting phenomenon over the sole utilization of the fast tool servo system. Subsequent experiments are carried out to evaluate the effects of feed rate and the number of sampling points on the machining accuracy of the microstructures under the combination of a rotary B-axis and a fast tool servo system. With the optimized machining parameters, sinusoidal modulation microstructures, which have a wavelength of 700.6 μm, a peak-to-valley of 18.7 μm, a surface roughness of 18.9 nm and a deviation of profile tolerance of 4.326 μm, are successfully fabricated on a curved copper surface with a face radius of 10 mm and a curvature radius of 500 mm. Full article

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures. Full article

Aero-engine blades are manufactured by electroforming process with electrodes. The blade electrode is usually machined with five-axis micromilling to get required profile roughness. Tool path planning parameters, such as cutting step and tool tilt angle, have a significant effect on the profile roughness of the micro-fillet of blade electrode. In this paper, the scallop height model of blade electrode micro-fillet processed by ball-end milling cutter was proposed. Effects of cutting step and tool tilt angle the machined micro-fillet profile roughness were predicted with the proposed scallop height model. The cutting step and tool tilt angle were then optimised to ensure the contour precision of the micro-fillet shape requirement. Finally, the tool path planning was generated and the machining strategy was validated through milling experiments. It was also found that the profile roughness was deteriorated due to size effect when the cutting step decreased to a certain value. Full article

Thin-walled parts made of aluminum alloy are mostly used as structural elements in the aerospace, automobile, and military industries due to good homogeneity, corrosion resistance, and the excellent ratio between mechanical properties and mass. Manufacturing of these parts is mainly performed by removing a large volume of material, so it is necessary to choose quality machining parameters that will achieve high productivity and satisfactory quality and accuracy of machining. Using the Taguchi methodology, an experimental plan is created and realized. Based on its results and comparative analysis of multi-criteria decision making (MCDM) methods, optimal levels of machining parameters in high-speed milling of thin-walled parts made of aluminum alloy Al7075 are selected. The varying input parameters are wall thickness, cutting parameters, and tool path strategies. The output parameters are productivity, surface quality, dimensional accuracy, the accuracy of forms and surface position, representing the optimization criteria. Selection of the optimal machining parameter levels and their ranking is realized using 14 MCDM methods. Afterward, the obtained results are compared using correlation analysis. At the output, integrative decisions were made on selecting the optimal level and rank of alternative levels of machining parameters. Full article

In flexible manufacturing systems, fast feedback from simulation solutions is required for effective tool path planning and parameter optimization. In the particular sub-domain of laser heating/cutting of thin rectangular plates, current state-of-the-art methods include frequency-domain (spectral) analytic solutions that greatly reduce the required computational time in comparison to industry standard finite element based approaches. However, these spectral solutions have not been presented previously in terms of Fourier methods and Fast Fourier Transform (FFT) implementations. This manuscript presents four different schemes that translate the problem of laser heating of rectangular plates into equivalent FFT problems. The presented schemes make use of the FFT algorithm to reduce the computational time complexity of the problem from $\mathcal{O}\left(M^2{N}^2\right)$ to $\mathcal{O}(MNlog(MN\left)\right)$ (with $M\times N$ being the discretization size of the plate). The test results show that the implemented schemes outperform previous non-FFT approaches both in CPU and GPU hardware, resulting in $100\times$ faster runs. Future work addresses thermal/stress analysis, non-rectangular geometries and non-linear interactions (such as material melting/ablation, convection and radiation heat transfer). Full article

It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail. Full article

Interactive multi-beam laser machining simulation is crucial in the context of tool path planning and optimization of laser machining parameters. Current simulation approaches for heat transfer analysis (1) rely on numerical Finite Element methods (or any of its variants), non-suitable for interactive applications; and (2) require the multiple laser beams to be completely synchronized in trajectories, parameters and time frames. To overcome this limitation, this manuscript presents an algorithm for interactive simulation of the transient temperature field on the sheet metal. Contrary to standard numerical methods, our algorithm is based on an analytic solution in the frequency domain, allowing arbitrary time/space discretizations without loss of precision and non-monotonic retrieval of the temperature history. In addition, the method allows complete asynchronous laser beams with independent trajectories, parameters and time frames. Our implementation in a GPU device allows simulations at interactive rates even for a large amount of simultaneous laser beams. The presented method is already integrated into an interactive simulation environment for sheet cutting. Ongoing work addresses thermal stress coupling and laser ablation. Full article