The lifting tonnage of large prefabricated components is heavy, and the adverse condition of partial load hoisting often occurs, which is conducive to dangerous accidents. In order to improve the synchronization control precision of hydraulic cylinders in lifting equipment, a synchronous control strategy combining IGWO-BP-PID (improved gray wolf optimization and back propagation proportion integration differentiation) and state difference feedback is studied. Firstly, the hydraulic cylinders are divided into two groups in the longitudinal direction by analyzing the structure of the special lifting equipment and the hydraulic principle. The gray wolf position is updated in the GWO to achieve the optimization of BP-PID parameters through IGWO. Then, three controllers are used to analyze the system control, and the control effect of IGWO-BP-PID is verified. Finally, the synchronous control strategy combining IGWO-BP-PID and state differential feedback is adopted to jointly simulate the hydraulic cylinders in AMESim/Simulink, and this is compared and analyzed with the experimental data. The results show that the IGWO-BP-PID controller has no overshoot and a better control effect. Compared with conventional PID control, this proposed method shortens the oscillation adjustment time of the hydraulic cylinders, and the synchronization control accuracy is higher. The validity of the synchronization control strategy for lifting equipment is verified through a field test on lifting an off-loaded, large prefabricated component.

Aiming at the problems of various fault types, such as the great difference in fault knowledge expression and the weak fault causality reasoning ability in hydraulic systems of bridge erecting machines, which lead to a low accuracy of fault component location in hydraulic systems, a hydraulic system fault diagnosis method based on ontology Bayesian networks was proposed. Firstly, by analyzing the fault knowledge of the hydraulic system for a bridge erecting machine in detail, the fault ontology was formally defined, and the fault ontology model of the hydraulic system was constructed with probabilistic extension. Subsequently, the conversion rules for the ontology Bayesian network were established, based on which the automatic transformation from the ontology model to the Bayesian network model was realized by using the Jena API. This conversion process was facilitated by the maximum likelihood estimation algorithm, resulting in an optimal Bayesian network model for fault diagnosis. Finally, a certain model of the hydraulic system for a bridge erecting machine was investigated using this methodology, and the Netica simulation platform was employed to conduct diagnostic reasoning from observed fault phenomena to fault components. The experimental results demonstrate that this approach enhances the accuracy of fault diagnosis and can provide a reference for the fault diagnosis of construction machinery hydraulic systems.

Under a traditional design scheme, the design quantity of the main girder of the bridge-erecting machine is redundant and there are many consumables, which reduces the production efficiency, and existing optimization methods have the problem of low convergence accuracy. Therefore, this paper proposes an improved beluga whale optimization algorithm based on the quadratic interpolation strategy and carries out the lightweight design of the main girder of the bridge-erecting machine. By introducing the quadratic interpolation strategy, the algorithm is not easy to categorize into the local optimal solution in the later stage of optimization, but it has excellent global search ability. Ten test functions were used to evaluate and compare the effectiveness of the original beluga whale optimization algorithm, the improved beluga whale optimization algorithm, and three other prevalent optimization algorithms, focusing on their convergence characteristics. Then, a mechanical analysis was carried out on the bridge-erecting machine girder under real loading conditions. According to the design standard of the main girder of the bridge-erecting machine, under the conditions of meeting the requirements of strength, stiffness and stability, an optimization model was established, and the optimization of the main girder of the bridge-erecting machine was carried out. It was verified that compared with the initial girder weight of the bridge-erecting machine, the optimized girder weight was greatly reduced. The results show that the optimization effect is remarkable and the research has significant practical value.

This study aims to calculate the impact of the inlet valve geometry and spray angle on the performance of internal combustion engines using Computational Fluid Dynamics (CFD) analysis. CFD analysis was performed to explore the fuel flow dynamics within the combustion chamber at critical stages, considering factors such as swirl and tumble. The study investigates the role of the intake port's geometry and spray angles in creating squish and swirl, which is crucial for enhancing the combustion efficiency and overall engine performance. The analysis employs the Finite Volume Method (FVM), solved within the ANSYS Fluent software, utilizing the standard k-ε turbulence model. Design Modeler was used for the geometry design, and ANSYS Fluent facilitated the CFD analysis of the injection. Four distinct cases were explored to assess engine performance across various designs, examining parameters such as pressure, temperature, and velocity. These performance parameters were evaluated against the existing literature, enabling the identification of optimal configurations. The study identified optimal performance parameters based on the existing literature. The best design was further validated against existing designs under identical boundary conditions. The research demonstrates improved engine performance across all parameters compared to existing values in the literature. This suggests the efficacy of the proposed inlet valve geometry and spray angle configurations in increasing internal combustion engines' efficiency.

Artificial Intelligence (AI) is a branch of computer science that deals with the adoption of human behavioral elements in computer-based systems. Some of these systems support learning, understanding, making inferences and adaptability. In recent years, Artificial Intelligence has brought a rapid technological revolution to many different sectors of industry. It is a tool that can greatly influence the different stages of the design process. In this particular study, the goal is to integrate Artificial Intelligence into the design process of a product, and more specifically of a soccer shoe, using the additive manufacturing principles as an industrial production methodology. Different stages to be followed during the design of a product include mind mapping, digital sketches, Computer-Aided Design (C.A.D), rendering and prototyping facilities (digital and physical equipment). By integrating Artificial Intelligence tools into the traditional design process, designers can enhance the outcomes of their final products through the use of automation and control systems based on a holistic approach to industrial design. This paper introduces a distinct and innovative design framework that combines the benefits of AI digital applications with the expertise of the designer. Specifically, the proposed design methodology will be used to create a prototype of the designed soccer shoe using rapid prototyping tools for user feedback on its form. The conclusions of the study are that Artificial Intelligence is a tool that can be integrated and improve the design process of a product, while at the same time supporting designers’ creativity and innovation.

Air pressure sensors remain an expensive and dedicated brand of product. They are built in to some luxury vehicles, and the repairing process is also costly. The aim of this study is to develop a cost-effective air pressure sensor that can be used in domestic applications. The unit includes a pressure gauge, Light-Emitting Diode (LED), Light-Dependent Resistor (LDR), and a few circuit wires. The concept of a night sensor mode was used in the development. Three pressure levels were identified, namely, low, correct, and high, with values of 28psi, 32psi, and 35psi, respectively. Based on the pressure variations, LED and LDR were installed and connected. The device was linked to the tire pressure measurements. In practice, air pressure is measured normally. When the air pressure is decreasing, the LED fixed on the 28psi value by the pointer closes, the shadow created by the LDR is taken as the input, and the signal is given to the control panel that the tire pressure is low. Similarly, the other two pressures are also measured by the sensor unit. All the devices were fitted with heat sleeving at the required places to reduce the unit's errors. A power adapter is used to provide power to the system. The results obtained using the developed system can be used in automotive applications where tire pressure can be a major issue. An experimental setup was evaluated using an automatic tire inflation system. The temperature variation may lead to limitations, and accuracy issues may occur if the unit is not temperature-compensated or is not in a controlled environment. The further improvement would require the unit to be free of errors in terms of calibration and other extraneous factors.

Fused filament fabrication (FFF) is a 3D-printing technology in which melted thermoplastic filament is extruded through a nozzle on the building bed over the previously solidified layer. This machine enables the fabrication of highly customized and lightweight objects, which are useful in the electronics, biomedical, aerospace and automotive industries. However, FFF 3D-printing is yet not widely used in industry due to the high printing times required. To reduce the printing times, different methods have been applied by researchers, such as nozzle adjustments and the introduction of parallel robots in the FFF machine. We have also developed such a high-speed FFF 3D-printer in the Laboratory of Manufacturing Technology of the School of Mechanical Engineering of the National Technical University of Athens (NTUA). This machine is based on an advanced electromechanical system that allows precise nozzle movement and filament deposition. This novel machine allows us to achieve speeds up to 350[mm/s], while minimizing losses regarding the quality and mechanical strength of the fabricated object. The construction of this high-speed FFF 3D-printer has already been optimized and now the testing phase has begun. The aim of this study is to give an in-depth analysis of the development of this machine (hardware and software) and to investigate the effect of this FFF 3D-printer on the dimensional accuracy of the 3D-printed objects. Specifically, resolution holes with diameters of 4[mm], 3[mm], 2[mm], 1[mm] and 0.5[mm] were built with different printing speeds (150, 200, 250 and 350 [mm/s]) according to the ISO ASTM 52902-2021 standard and the measurements were obtained using a microscope. The results showed that the current FFF 3D-printer achieved acceptable dimensional accuracy (errors below 10%) even for the highest printing speeds. Moreover, the slight decrease in dimensional accuracy observed as the printing speed increases is probably, due to the amplification of oscillation and elasticity phenomena (elasticity of the belt-driven system) in the 3D-printer.

In view of the development of electric drives, they manage the operation of motors, in addition to ensuring the properties of the drive, and perform the function of monitoring the machine's technical condition. In the case of popular industrial applications induction motors, electrical circuit damages are more than half of all appearing faults. In connection with the above, the task of early detection of defects becomes a priority in drive systems. Increasingly, the diagnosis of electric motors uses artificial intelligence techniques, in particular, neural networks in the form of classic or deep structures. However, providing useful functions requires the development of many diagnostic patterns that carry information about the technical condition of the machine. Therefore, expanding the scope of the system to include new types of defects requires a reimplementation of the neural structure. The solution to the problem of the universality of features is the use of transfer learning. To demonstrate the advantages of transfer learning, a fault diagnostic system for stator windings and rotor cage bars of an induction motor was developed. The developed system was based on direct analysis of the axial flux signal, bypassing the classical methods of symptom extraction. Particularly noteworthy is the fact that the system can detect two types of defect based on the symptoms acquired for one type of defect. Verification was carried out in the steady and transient states for the full range of load torque. Analysis of the detection of rotor cage bar defects in the absence of load is of particular importance due to the absence of the motor slip parameter, which limits the use of classical diagnostic methods. In addition, thanks to the use of direct signal processing by a convolutional neural network, it was possible to repeatedly reduce the response time to an emerging defect.

Induction motors operate in difficult environments in the industry. Monitoring their performance in such circumstances is significant, as it can provide a reliable operation system to secure the production line. Recently, Artificial Intelligence techniques (AI) were applied to the condition monitoring and fault diagnosis systems in order to build an efficient classification model. This paper focuses on developing a new hybrid diagnosis model for fault classification. The development of this model provides a novel technique for the diagnosis of single and multiple induction motor faults. The aim is to find a new alternative source to extract automatic features from the motor parameters. Three deep learning networks including Visual Geometry Group 19 model (VGG-19), Residual Network 50 model (ReseNet-50), and EfficientNet-B0 model (EffieNet-B0) were applied to pre-train the suggested model. The use of these networks can also allow the attributes to be automatically extracted and associated with the decision-making part. The model's performance was assessed by calculating some evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and F1 Score. The evaluation of the proposed model was achieved by applying different types of motor data including stator current data and motor vibration data. In addition, Convolutional Neural Networks (CNNs) were applied as an image processing method to achieve the model features. The experimental results proved the robustness and capability of the proposed model for fault classification by combining the suggested networks. The suggested hybrid model achieved a high classification accuracy.

Modern high-value manufacturing relies heavily on solid carbide end mills for finishing critical components. The intricate geometry of these cutting tools plays a crucial role in machining success. Optimizing end mill design requires the precise definition of various parameters to ensure efficient machining. This research presents a novel CAD-based framework that empowers designers with the ability to create and optimize solid end mills. Tool parameters such as tool diameter, rake angle, helix angle, and the number of teeth are used as input variables from the designer to influence the tool overall design.

The framework facilitates the design of variable helix angle tools, renowned for their stability during machining operations. It leverages the automation capabilities offered by the Application Programming Interface (API) within the selected CAD platform. The solid geometry produced through the framework would allow for the accurate representation of cutting tools that can be further exploited in FEA and CFD simulations of machining applications. The API-based approach enables users to define specific criteria and achieve a comprehensive optimization of tool geometries. The resulting geometries are then compared with established industry standards from leading vendors, providing valuable validation. Moreover, the effect of key design variables on the geometry of the cutting tool is investigated.