This Paper presents the development of a flexible wire bending machine designed to overcome limitations associated with traditional counterparts. Traditionally, wire bending machines are often designed for specific purposes, necessitate complex coding, are prohibitively expensive, or are...

Computer Numerical Control (CNC) machines are widely used in different processes, such as milling, turning, drilling, etc., due to their high accuracy, rapidity, and repeatability. While these machines are fully controlled using G-code, the manual setup between the cutting tools and the initial stock can be time-consuming and requires skilled and experienced operators. This study utilizes artificial intelligence, supported by Deep Learning and image processing techniques, to automatically set up the machine by computing the distance between the tool and the workpiece. Firstly, a You Only Look Once (YOLO V4) algorithm has been developed via MATLAB programming specifically for the recognition of tools and workpieces. This algorithm has been trained using 1700 images, which are captured by a Rapoo C260 Webam, in the machine configuration environment for both the tools and workpieces. After recognizing the tool and workpiece, the algorithm provides information in terms of coordinates to specify where these objects are located within the image by drawing bounding boxes around them. Because the edges of the bounding boxes do not accurately depict the actual edges of the tool or the workpiece, the implementation of image processing techniques is necessary to correct these differences and determine the precise distance between the tool and the workpiece. Finally, an automatic G-code correction is generated to adjust the existing G-code, resulting in an automatic machining setup. The proposed methodology has been implemented and evaluated on a CNC turning machine, and it showed promising results in terms of reducing the required machining setup time

Mobile robots play a crucial role in Industry 4.0, particularly in dynamic and unstructured environments where moving obstacles present significant challenges. This study applies the YOLOv5 object detection algorithm to enhance robotic perception and obstacle avoidance. The primary objective is to improve the accuracy and speed of object detection in real-time scenarios, ensuring safer and more efficient navigation for robots. The research contribution lies in developing a lightweight YOLOv5 model optimised for robotic applications, capable of detecting objects with high accuracy. The model was trained on a diverse dataset of 10,700 images, including static and dynamic objects such as chairs, fans, fire extinguishers, and humans, captured under various conditions and orientations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The proposed model achieved a mean average precision (mAP) of 0.73 at a confidence threshold of 0.374, demonstrating superior performance compared to the YOLOv4 model in terms of accuracy and processing speed. Notably, the model excelled in detecting static objects such as chairs, achieving a perfect recognition rate of 1.00, while encountering challenges with moving objects such as humans due to motion blur and rapid changes in body posture. These findings highlight the model’s potential for real-time applications in industrial and unstructured environments. In conclusion, this study demonstrates that the enhanced YOLOv5 model significantly improves object detection and collision avoidance capabilities in robotic systems.

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process.

Materials and parameters for conventional arc welding with the welding torch in vertical (PA) position are well known and investigated. However, apart from the ideal PA-position, not much is known about a deposition in so called ‘forced’ positions. This is becoming increasingly important in the case of particularly large components, as these can efficiently neither be clamped nor moved on a rotating and tilting table. More flexibility is achieved when the welding gun is moved as a tool on curvilinear paths to build up complex parts without necessary part movement. This link to the component surface means to leave the ideal vertical position. To make that possible, optimal parameters must be found for each angular position to enable high-quality and reliable build-up welding. In this work, a fuzzy logic-based system is designed based on set of experiments in single and double-layer weld beads structure using mild steel wire to predict the bead widths and heights for three different positions (Horizontal, Rising, and Falling torch movement). A comparison between the fuzzy and experimental values is studied. The averages of width mean error between experimental and fuzzy logic model values for the three positions were (1.3%) and (0.85%) for the single and double-layer weld beads, respectively. Averages of width mean error for the bead heights were (0.97%) and (0.73%) for the two structures. The current proposed study demonstrates a good agreement between the predicted fuzzy values and the experimental outcomes for the bead width and height.

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