Intelligent measuring is related to smart systems and automated measurements of weather conditions or solar irradiance, to help the manufacturer to optimize solar cell features. Technology should strive to minimize the impact of human activity upon the environment, developing greener solutions for energy consumption. In this paper, we propose a prototype for a wavelet based system, which should analyze several parameters collected by a photovoltaic system - using the Stationary Wavelet Transform. We aim to develop a general mathematical model, which should allow us to improve the manufacturing process of the solar modules. The Stationary Wavelet Transform and several mother wavelets families were tested. The most accurate results were obtained with the Daubechies mother wavelets.
Today, the emergence of new industrial values and the integration of advanced technologies have ushered in a significant evolution in industry through the implementation of Industry 4.0 strategies. As Taylor noted in 1911, a production system must be analyzed starting from its elementary processes. Building on this idea, this paper proposes a control system with artificial intelligence at a foundational level of production systems, specifically focusing on CNC machine tools. Two types of artificial intelligence control systems are proposed for manufacturing processes: one aimed at increasing processing accuracy and the other designed to reduce processing costs or enhance productivity
This paper presents the research and results obtained by authors regarding the modeling of the reactor of the fluid catalytic cracking unit (FCCU), using various machine learning techniques based on supervised learning algorithms Compared with the analytic mathematic modeling methods, machine learning techniques (ML) can solve the difficulties in FCC process modeling, such as strong correlation between variables, nonlinearities, the complexity of kinetic models, and feedstock complexity. The ML method establishes a mathematical correlation between the input and output variables of the process using industrial data. Five supervised learning machine methods are used to model the reactor: random forest regression (RFR), decision trees regression (DTR), k-nearest neighbors (KNN), support vector machines (SVM) and gradient boosting regression (GBR).
During the mastication process, the restorative materials are exposed to dynamic loading as tooth cusps and hard foods cyclically indent these materials. In this work, the indentation technique is used to investigate the mechanical behavior of G-aenial under various multi-cycling nanoindentation conditions and to study the mechanical properties, such as elastic modulus and hardness. First, static indentation tests were performed to assess the effect of the holding time on the mechanical behavior of G-aenial. Then, a series of dynamic indentation tests were conducted in which the number of indentation cycles varied from 6 to 30. The experimental results indicated that both the static indentation hardness and elastic modulus decrease with increasing holding time from 0 s to 30 s. Moreover, the elastic modulus and indentation hardness of the G-aenial composite decreased with the increasing number of cycle loadings, indicating that the restorative composite becomes more susceptible to fatigue damage.
In the project management of new product development, the cost estimates of a project are directly influenced by the scope of the project. In product development, the scope of a project is the finished product. Unless the new product is a combination of existing sub-components for which the costs are known, a decomposition of an unknown product is necessary in order to form the estimates. A quick and easy method for cost estimation will be investigated involving the use of Axiomatic Design method applied to a custom design milling machine. The results confirm the utility of the method in cost estimation of a new product.
The aim of the paper is to carry out experimental verifications regarding the elasto-plastic deformation of round, thin-walled pipes. The paper has two goals. The first goal was to create a clamping device for long pipes. The second goal was to carry out specific experiments with this device, which would verify the calculation relation obtained in a previous theoretical research. The paper presents aspects related to the design and manufacture of the device. The experiments carried out are also presented, through which the elasto-plastic deformation that occurs when processing holes in long, thin-walled pipes was highlighted, and compared to the theoretical relation previously established. The conclusions presented confirm the achievement of the proposed goals, through 100% original contribution.
In the given paper, the problems of describing the cavitation process by means of CFD methods are presented. The role of cavitation process study as an optimization criterion of centrifugal pump construction is also described. At the same time the theoretical part related to modeling the cavitation process in centrifugal pumps is discussed. At the end of the paper, the process of determining the NSPH value by means of CFD simulations and comparison of the results with the results of physical testing of a serial pump produced by CRIS Hermetic Pumps is presented.
This paper is a study through which robotic manufacturing cells can be modeled using hierarchical Petri nets. They are an efficient tool due to their ability to model dynamic processes. The subject addressed in the paper is that of modeling and improving the manufacturing workflow for the pipe bending process. Within the research carried out, three models are compared with Petri nets made in the CPN IDE program with the aim of improving the system. The first model represents the initial production flow. The second model improves the sequence of robot movements by introducing additional tasks to the robot serving the production process. The third model introduces a double clamping system, allowing the simultaneous manipulation of two pipes, which leads to a significant increase in productivity. The streamlining of the workflow and improvement of resource use are confirmed by the simulation results.
This study presents the numerical modeling of incremental plastic deformation using a hemispherical tool on AA6061 aluminum alloy. A finite element model was developed in ABAQUS, using the explicit method. The thermo-mechanical behavior of the material was defined by the Johnson-Cook law, the modeling of thermal phenomena was based on an adiabatic approach, and the simulation time was reduced by using mass scaling. Validation, based on comparing the simulation results with experimental deformation forces and temperatures, revealed similar trends, although some differences in the maximum values appeared due to modeling assumptions. The analysis of deformation forces, temperatures, 3D shape evolution and plastic equivalent strain provided key information on the SPIF process. Key points identified include the onset of plastic deformation, stabilization of forces, and temperature during the process.
The paper focuses on developing an integrative methodology to evaluate environmental, economic, and social sustainability of alternative solution design options. Core approach is to extend the classic method of Quality Function Deployment (QFD) towards a holistic integrated model to overall evaluate the different aspects of all three pillars of sustainability (economy, ecology and social) across the product life cycle. The methods identify key aspects and allow users to consider and weigh different life cycle aspects in a overall scoring scheme.
The technological advances emerging with Industry 5.0 and the difficulties of maintaining flexibility and competitiveness are pushing companies towards new work scenarios that involve the use of collaborative robots. However, such developments are throwing up new challenges in terms of occupational health/safety. the purpose of this paper is to present real case feedback and associated challenges related with collaborative robots as well as different tools and methods to face it. The presented results show that cobots are still at the "test level" and their design must overtake the internal contradiction in the human (variability)-robot (invariant) system. Different tools and methods are proposed such as VR or usage method. The new challenges need to be undertaken by considering new knowledge and preventive measures. This is vital for employee health and overall business success.
This study examines the fracture behavior of Single Edge Notched Bending (SENB) specimens produced using Digital Light Processing technology. The focus is on the effect of blending two UV resins (from pure White to pure Black) on fracture properties and specimen accuracy. The research also explores crack propagation mechanisms, along with the characteristics of SENB fracture surfaces. Findings indicate that the resin mixture significantly influences the investigated properties. Among the tested specimens, W75B25 mixture shows the highest mode I fracture toughness and fracture energy, while B100 exhibits the lowest. Additionally, W25B75 and W75B25 mixtures present the smallest dimensional errors, and B100 has the lowest mass. The specimens exhibit brittle behavior, with cracks initiating from the notch tip and no observed plastic deformation.
The article presents the approach of a taxonomy development with standards providing support to technologists and designers in manufacturing to better manage production processes. The approach is intended to consider the current manufacturing framework marked by the transition from Industry 4.0 to Industry 5.0; the taxonomy framework design approach consists of eight steps: (1) defining the scope, (2) conducting literature reviews, (3) collecting data, (4) conceptualizing the basic items, (5-8) documenting, classifying, validating, refining and maintaining the created database. Consulting various specialists during the testing and validation phase of the taxonomy highlighted the usefulness of the approach and its result in facilitating the process of navigating the complexity of standard aspects related to smart manufacturing, to boost innovation and compliance.
In the context of mass personalization, the customer is integrated into the product design process during the configuration phase. The paper presents different strategies and approaches to product configuration, the integration of product and process configurations, product configuration systems that satisfy customers, ensuring production efficiency, and mass customization. An attribute/function-based configurator is proposed that integrates process, product configuration, and process planning decisions. It was also proposed that an open architecture product configurator be developed to integrate both a customer interface and an application used by engineers involved in product design.
Ensuring the competitiveness of products and technologies requires addressing innovation activities. The didactic work of the authors of this paper has led to the observation that, under certain circumstances, a deep documentation activity when aiming to identify innovative solutions can turn into a psychological obstacle in terms of generating innovative solutions in a given time. One way to reduce such a consequence of deep documentation can be to postpone it, that is, resort to extensive documentation only after identifying at least one supposedly innovative solution. However, one cannot give up on deep documentation, which is necessary to validate the innovative nature of the identified solution and the possibilities of its use. Postponing deep documentation can be applied when there is a certain level of professional knowledge. Arguments and explanations have been formulated according to which, at least in certain situations, a postponement of deep documentation may be indicated.
This paper proposes a reconceptualization of artificial intelligence in design, aligned with the philosophical foundations of Industry 5.0-where innovation is guided by ethics, purpose, and human values. It introduces Unified Design Intelligence (UDI), a framework that integrates four AI roles - explorative, generative, cognitive, and discoverative - to support co-evolutionary design processes. Unlike the use of AI for predefined, task-bound operations, UDI enables AI to contribute to value-driven ideation, contradiction resolution, and context-aware innovation. The framework is validated through case studies in manufacturing, robotics, mobility, and product authentication. Results confirm UDI's capacity to align technological capabilities with societal relevance and ethical foresight, showing that the real shift in Industry 5.0 lies not in what AI can build - but in why and for whom we choose to build.
This paper explores the potential of using a neural network to position a robotic arm's end effector. Traditional methods rely on direct and inverse kinematics, but this study draws inspiration from human hand-eye coordination-how people learn their hand's workspace and reach for objects. In robotics, a similar approach could involve using a depth camera to detect and grasp objects directly. The neural network learns the relationship between joint angles and end-effector positions within the camera's field of view, with applications in manipulation, object handling, and collaborative robotics in dynamic environments. Simulations on a 3-DOF robotic arm tested 10 architectures, revealing feasibility for direct kinematics but challenges in inverse kinematics requiring further research.
This work was carried out as a continuation of previous studies related to the influence of positioning of gear-type or tubular parts. The authors considered expanding the investigation process by approaching in parallel the modified psychological experimental analysis, but also considering the economic, environmental and dimensional aspects and the surface quality for stepped cylindrical parts. To make the parts, a material was used that has as its basic structure poly lactic acid which is modified to be able to work at high printing speed. To establish the optimal constructive orientation that can influence the manufacturing process of the parts, the printing surface was considered. Based on the present study, it was observed that making the parts in a vertical position best satisfies the environmental and constructive criteria and was positioned in the same place at a short distance from the economic point of view from the horizontal printing position.
Laser engraving is an advanced technique for precise application of markings on the surface of molds, offering advantages such as high accuracy, wear resistance, execution speed and the ability to mark complex surfaces. This environmentally friendly method ensures good long-term readability without the use of chemicals. The study presents multi-objective optimization of engraving parameters, using experimental design and gray relational analysis to simultaneously improve surface roughness, engraving depth and processing time. By analyzing seven input parameters, the research identified the optimal settings and the factors with the greatest influence on the process, demonstrating the effectiveness of the method and opening new perspectives for improving the process performance.
The article describes the benefits of using the Six Sigma DMAIC cycle to enhance delivery timing to clients in the automotive industry. The DMAIC cycle tools were used at each stage, and each consecutive step was built on the results of the preceding one, with the goal of achieving a long-term solution to the examined issue through the deployment of corrective actions. The use of corrective actions resulted in improvements, eliminating the waste in the assessed organization. The root cause was a lack of information between the teams, which resulted in waste. The solution for this challenge was to strengthen collaboration with other teams while creating papers for upcoming tests based on their duration and difficulty.
