This paper presents the application of non-contact 3D measurement in industry, with a particular focus on the nspection of an automotive cross member using the portable Creaform HandySCAN BLACK+™|Elite scanner. The paper covers the theoretical foundations and methods of non-contact measurement, a detailed description of the selected device, a comparison with alternative measurement systems, and its practical application on a real example. Special attention was given to the preparation of the object, the scanning process, and the data processing within the Creaform.OS software suite. The obtained results were compared with the CAD model of the cross member, and the generated measurement report enabled the detection of deviations and evaluation of the part’s quality. The analysis demonstrated that non-contact 3D measurement significantly accelerates quality control processes, reduces the likelihood of measurement control error, and enables the digitalization of complex geometries with a high level of accuracy.
The adoption of artificial intelligence (AI) in organizations is often fuelled by promises of improved efficiency and innovation, while its practical value and operational impact remain unclear. This paper presents a research framework for analysing the added value of generative AI (GenAI) in logistics organizations, with a focus on “difficult to automate” tasks and processes. It combines literature-based model development, semi-structured expert interviews, field studies, and user-cantered experiments to examine how GenAI affects task performance, user behaviour, and organizational structures. It integrates qualitative insights with experimental and modelling approaches to support a systematic assessment of value creation, efficiency gains, usability, user acceptance, and organizational impact. Preliminary results from expert interviews indicate that GenAI is primarily used for cognitive support enabling time savings in manual tasks, and less for process automatization. Eventually, the presented framework will provide a structured basis for the development of evidence-based roadmaps for user-centred AI adoption in logistics.
This paper presents the final system-of-systems architecture of the PARSEC parcel screening concept and its integrated simulation–optimization framework. Following modifications in the technological configuration, including the introduction of computed tomography and revised decision logic, the analytical and simulation models were updated accordingly. A probabilistic formulation of system-level detection and false alarm rates was developed, explicitly incorporating the P/I shielded branch. A mixed-integer linear programming model was formulated to select optimal ROC operating points under security and capacity constraints and solved in MATLAB. The proposed framework provides a structured methodology for evaluating detection performance and operational feasibility in high-throughput parcel environments.
The quality and reliability of the production process in the automotive industry can significantly affect the market reputation and overall business performance of the entire company. In this paper, a Multi-Criteria Decision-Making (MCDM) model is defined for determining the priority of preventive and detection actions, i.e., a guideline that can be used to follow the prioritization of these actions. To model uncertainty and imprecision in expert evaluations, the model integrates Single-Valued Neutrosophic Sets (SVNSs) with the Single-Valued Neutrosophic Weighted Averaging Operator (SVNWAO) and the ELimination Et Choix Traduisant la REalité (ELECTRE) method. SVNWAO is used for determining the weights of criteria, while ELECTRE is applied for ranking the considered actions. The proposed methodology is applied in a case study conducted in a company that is a tier-1 supplier in the automotive industry. In cooperation with company experts, a total of 12 specific management actions and 6 criteria were identified according to which these actions were evaluated. The research results showed that Poka-Yoke by design is the most reliable action, providing long-term efficiency, followed by Update of standardized work instructions, Additional operator training, and Increased inspection frequency. The proposed model, as well as the results of this research, provide practitioners with a robust and objective tool for prioritizing actions, as well as for allocating resources in a complex and dynamic industrial environment.
Machine Learning (ML) methods have been increasingly applied in engineering systems for predictive analytics and decision support. However, their implementation in real-time applications is often limited by computational complexity, memory requirements, and limited interpretability. This study proposes a novel probabilistic ML framework based on nonhomogeneous Markov chains, in which the system state space and transition probability matrix are continuously adapted as new observations become available. The proposed Markov Chain Machine Learning (MCML) methodology was developed by defining adaptive state transitions and evaluating the corresponding limiting probability distributions using both an analytical solution and a modal superposition method (MSM) to reduce computational requirements. The framework was implemented and validated using two physically different cases: purse seiner operational dynamics and high-altitude wind velocity prediction. The obtained results demonstrated excellent consistency between the analytical and MSM approaches, with the mean absolute errors of 0.00424 km/h for the sailing speed and 0.0001 m/s for the wind velocity predictions. The proposed methodology significantly reduces computational complexity while preserving prediction accuracy and probabilistic interpretability, thus providing an efficient framework for real-time engineering applications such as intelligent monitoring, predictive maintenance, and digital twins.
Continuously variable transmission is a type of transmission that can change torque continuously without dividing into gears like a mechanical transmission. This transmission is equipped on modern vehicles, so it requires the transmission to ensure the longevity and durability of internal components. Therefore, this article focuses on analysing the stress, deformation and safety factors of the gearbox pulley corresponding to each manufacturing material. The 3D model with the basic parameters of the pulley is built using SolidWorks software. The finite element method is applied to analyse using Ansys. Based on the simulation results, it shows that when compared with other materials, Stainless Steel still ensures durability and stress, so the study recommends that Stainless Steel be used as a material to manufacture gearbox pulleys in factories.
Traditional electrical generation methods are expensive with disturbing ecological effects and excessive maintenance costs. So, combat with contemporary scenario, alternative sources of energy that are environmental friendly are adopted. This research is marked by an efficient, economical ON-Grid Photovoltaic (PV) system installation in Lodhran City, in the South Asian country Pakistan, to solve this forthcoming issue. Lodhran is a place where the intensity of the sun’s power is quite high. The Global Diurnal Horizontal (GDH) sun insolation at the proposed location is 1828 kWh/m2, having gross sun insolation is 5 kWh per m2 per day whilst the gross temperature is 27.45° Centigrade. In this paper, the variant potential deficits, decrements prices in photo voltaic module, inverter efficiency, and temperature variation are given dire consideration and calculated by the PVSyst software. By the smart installation of solar panels, the industry can save 1003-ton coal per day i.e. equivalent to planting 50,000 trees over a whole lifespan. From the software, the numerical value of the performance ratio reaches 68.8%, and the total units generated per year is 1758 MWh. The performance parameters of the grid-tied system can be enhanced by the modeling, analysis, operation, and maintenance of grid-tied networks for different sites near the model city Lodhran in southern Punjab.
The demand for robust and reliable electric vehicle (EV) charging infrastructure has accelerated the exploration of advanced charging systems utilizing multiple energy sources. In this context, a dual-input EV charger powered by Wireless Power Transfer (WPT) and Photovoltaic (PV) sources is proposed in this paper. The charger system employs a Proportional-Integral (PI) controller to regulate the output voltage by comparing it with the required battery charging voltage. Given the nonlinear and time-varying nature of the input sources, tuning the controller using conventional methods proves inadequate. Therefore, a metaheuristic-based optimization technique, known as the JAYA algorithm, is utilized to determine the optimal PI controller parameters for improved voltage regulation. The closed-loop system performance is verified through MATLAB/SIMULINK simulations, and further validated using a laboratory-developed prototype controlled via an FPGA platform. The results demonstrate that the JAYA-tuned controller significantly enhances transient performance and voltage stability under various input fluctuations and load disturbances. The practical implementation confirms the effectiveness of the proposed system, offering a viable solution for next-generation EV charging applications.
The main aim of this paper was to determine the optimal design of a sand mould using computer simulation and statistical optimisation techniques. Simulations of the sand-casting process were performed using NovaFlow&Solid software. The geometry of the mould and the type of sand used were varied according to the Taguchi L18 design of experiments. Output variables used to assess process quality and efficiency were total casting time, volume shrinkage, and melt efficiency. Taguchi-based grey relational analysis was used to identify the optimal process parameters. The combination of input parameters represents the optimal solution for process simulation resulting in minimal total casting time, minimal volume shrinkage and maximum melt efficiency. The proposed methodology provides an effective framework for simplifying the casting process and enhancing the reliability of gravity-cast components.
With its abundant solar energy resources, India holds immense potential to integrate solar power into its renewable energy goals. This study evaluates the technical feasibility and performance of grid-connected rooftop solar photovoltaic (PV) systems in Mysuru, Karnataka, using PVsyst software across seven sites. The analysis spans academic, industrial, and residential sectors, highlighting discrepancies between projected and actual energy outputs. Sites 3, 5, and 6 show room for improvement through better shading management, equipment upgrades, and maintenance, while high-performing sites like Site 7 offer capacity expansion opportunities. A survey of 2,000 participants identified cost and awareness as adoption barriers. Recommendations focus on education, infrastructure, and supportive policies to promote sustainable energy.
This paper presents the comprehensive solution of demand side response and enhanced power quality in a standalone distributed generation system using modified parallel structured signal decomposition control algorithm (MPSSD). It is observed that in most of the conventional frequency locked loop (FLL) based algorithm dual-reduced order generalized integrator-frequency locked loop (DROGI FLL), The estimation of fundamental frequency positive and negative sequence components of load current in the alphabeta frame is primarily required to generate the source reference current for solving the power quality and demand response issues. However, modified parallel structured signal decomposition control algorithm (MPSSD) estimates the said both components directly without involvement of alphabeta frame transformation. Therefore, computational burden and estimation time requirement is reduced. As a result, the faster and high accurate dynamic response of proposed MPSSD control algorithm is achieved under transient behavior of distributed generation system. The main objectives in this paper is to provide harmonics free source current, load balancing under different demand side response conditions and reactive power control in solar-wind based distributed power generation system. This task is achieved by operating the voltage source converter through the switching patterns generated by MPSSD control algorithm. The entire system is built in MATLAB Simulink and results of various operating conditions are observed and found satisfactory.
This paper aims to introduce an innovative approach involving lattice implementation within the context of Pythagorean Fuzzy Soft Sets. It demonstrates several properties of this approach. The principal objective of this research is to amalgamate the Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methodologies, thus creating a comprehensive Multiple Criteria Decision-Making (MCDM) framework. This integration not only presents a new perspective in mathematical computing but also has intriguing principles. Within this integrated framework, alternative rankings are determined using the WASPAS approach, while the SWARA method is employed to generate criteria weights. The applicability of our proposed strategy is substantiated by addressing an MCDM challenge in the context of Education 5.0. The effectiveness and accessibility of this approach are evaluated by comparing its outcomes with those of previously established techniques.
Due to their excellent corrosion resistance, light weight, hardenability, and high recyclability, aluminum alloys have become a staple in the automotive industry, particularly in the manufacturing of car bodies. However, the Portevin–Le Chatelier (PLC) effect, which manifests in certain aluminum alloys during plastic deformation, poses significant challenges. This phenomenon can lead to undesirable visual and structural defects, thereby limiting the broader application of these alloys in the automotive sector. Understanding the PLC effect is crucial for enhancing the usability of aluminum alloys in vehicle production. This paper deals with the PLC phenomenon, exploring the various parameters that influence its occurrence during plastic processing. By investigating these factors, the aim is to provide a better insight on this phenomenon.
Object detection plays a crucial role in enhancing mobile augmented reality (MAR) applications, but the computational limitations of mobile devices and the dynamic real-world environments pose significant challenges. This study proposes a novel framework that integrates Knowledge Distillation (KD) and Unsupervised Domain Adaptation (UDA) to address these issues. KD transfers knowledge from a resource-heavy "teacher" model to a lightweight "student" model optimized for mobile deployment, while UDA enables the student model to adapt to real-world conditions without labeled data. Our framework uses YOLOv5 models, where the student model, YOLOv5n, learns from the teacher model, YOLOv5 small, improving precision and maintaining efficiency. Experiments on the VOC2007 and COCO datasets show that our SKD-UDA net achieves 78.2% mAP at IoU 0.5 and 50.8% mAP at IoU 0.5:0.95, outperforming the baseline YOLOv5n by 5.5% and 5.7%, respectively, without increasing the model size (1.9 MB). This approach enhances accuracy and computational efficiency, making it ideal for MAR applications. Our contributions advance object detection in MAR, improving user interaction by increasing detection accuracy, inference speed, and seamless integration of digital and physical environments.
Due to their excellent corrosion resistance, lightweight, hardenability, and high recyclability, aluminum alloys have become a staple in the automotive industry, particularly in manufacturing car bodies. However, the Portevin-Le Chatelier (PLC) effect, which manifests in certain aluminum alloys during plastic deformation, poses significant challenges. This phenomenon can lead to undesirable visual and structural defects, thereby limiting the broader application of these alloys in the automotive sector. Understanding the PLC effect is crucial for enhancing the usability of aluminum alloys in vehicle production. This paper addresses the PLC phenomenon, exploring the various parameters that influence its occurrence during plastic processing. By investigating these factors, the aim is to provide better insight into this phenomenon.
Non-Intrusive Load Monitoring (NILM) plays a vital role in energy efficiency by disaggregating appliance-level consumption from aggregated household energy data. A conventional Temporal Convolutional Networks (TCNs) doesn't have classification and regression sub-networks. This study explores the use of TCNs for parallel appliance classification and load prediction, addressing challenges like overlapping energy signatures and long-term temporal dependencies. The TCNs, with their dilated causal convolutions and efficient parallel processing, are well-suited for NILM applications, offering improved scalability and accuracy over traditional machine learning and recurrent neural network (RNN) approaches. The proposed framework utilizes multi-task learning to classify active appliances and predict their energy consumption simultaneously, reducing computational overhead and enhancing system adaptability. Experiments on publicly available datasets REDD, UK-DALE, demonstrate the TCN model's superior performance, achieving higher classification accuracy, improved load prediction fidelity, and robustness under noisy conditions. The lightweight and scalable architecture ensures suitability for real-world deployment, including smart grid systems and residential monitoring.
The application of single-phase grid-connected inverter systems is growing day by day, particularly in residential solarpowergen-eration. The systems are becoming popular as the rooftop solar PV solution of choice, due to the least infrastructure needs and lower installation expenses. Solar power generation is a variable process in terms of solar irradiation and ambient temperature. For maximum energy extraction from the PV modules, efficient Maximum Power Point Tracking (MPPT) techniques are vital. In this paper, the design, simulation, and performance analysis of a single-phase grid-connected solar photovoltaic (PV) inverter system using an Adaptive Network-Based Fuzzy Inference System (ANFIS) based MPPT technique is discussed. Conventional MPPT techniques, such as the Perturb and Observe (P&O) algorithm, are plagued with oscillations and sub-optimal tracking efficiency. As a counter measure, in this paper, an innovative 2.1 kW inverter system using ANFIS-based MPPT is developed to offer improved stability and efficiency in power extraction. The proposed system includes a solar PV array, a DC-DC boost converter designed with constant DC link voltage, and an H-bridge singlephase inverter synchronized with the grid through a Phase-Locked Loop (PLL) based control strategy. ANFIS controller is trained over vast datasets representing solar irradiance and temperature variations to develop an optimal reference voltage for the boost converter. MATLAB Simulink is utilized for simulation and comparison of the ANFIS-basedMPPT performance with the conventional P&O technique. From the findings, it is clear that the AN-FIS technique significantly reduces the oscillations in the PV panel voltage, current, and power, and hence saves energy losses and increases overall system efficiency. Under standard environmental condition and steady-state operation, the ANFIS-controlled system shows a stable PV panel voltage and an increase of 5.6% in grid-injected active power as compared with the P&O method. The system also shows improved performance with varying irradiance, affirming the adaptability of the ANFIS controller. The results indicate that use of ANFIS with grid-connected solar PV inverters can potentially improve performance significantly, opening the way for more efficient and reliable renewable energy integration into the contemporary power grid.
This paper studies the radiated interference of the near electric field (E-Field) generated by a series chopper, particularly in the main components of the converter: the IR2110 driver, IRF740 MOSFET, and MUR460 diode, using Rohde & Schwarz probes in the frequency domain (FD) by using a spectrum analyzer. The chopper is supplied by a 20 V DC source, and the control signal is a square wave. First, we determine the electrical diagram of converter DC/DC using LTspice software, then implement it on a PCB, in order to quantify the electric field on all the electronic components on all the components. Then the impact of variation the frequency of the commutation the MOSFET was determinated. The following frequencies were used while keeping the duty cycle fixed: 5 kHz, 10 kHz, and 145.3 kHz. Then, we varied the duty cycle, selecting the following values: 75%, 50%, and 25%, while keeping the frequency fixed at 10 kHz. The variation of the series chopper control signal parameters (frequency and duty cycle) is managed by an Arduino program. The results demonstrate that probes with larger surface areas capture a stronger field than those with smaller surface areas. future work, we will study the near magnetic field, and all the parameters that influence magnetic emissions
In aluminium and its alloys, the efficacy of diffusion bonding and extrusion-based additive manufacturing (AM) is greatly limited by the presence of aluminium oxide (Al2O3) on the surfaces to be bonded. In the present work, a novel fast diffusion bonding technique is introduced, in which the surfaces to be bonded are mechanically scraped using an abrasive file immediately before bonding to remove or break the surface Al2O3 layer and reduce surface roughness so that the holding time can be reduced. In the present work, the new fast diffusion bonding technique has been experimentally investigated by bonding similar half-dogbone specimens of aluminium alloys AA7075-T6 and AA6061 to determine the effects of the bonding temperature, bonding pressure and holding time on the strength and properties of the resulting bonded specimens. Bonding curves, showing the pressure required for successful bonding as a function of temperature and holding time, have been constructed. Using the novel technique has been found to increase the maximum strength in tension of bonded specimens of AA7075-T6 and AA6061 by around 12% and 11%, respectively, compared with the conventional method. Metallographic examination has shown that the novel technique aids in eliminating residual microvoids from the bonding interfaces, providing further evidence that the new technique is effective in promoting good-quality bonding. The present work has demonstrated the feasibility of the novel diffusion bonding technique and opens up the possibility for application of the technique in extrusion-based AM to give a hybrid-AM process, allowing high-quality bonding between successively printed layers.
Object detection plays a crucial role in enhancing mobile augmented reality (MAR) applications, but the computational limitations of mobile devices and the dynamic real-world environments pose significant challenges. Current literature often falls short in proposing solutions that maintain both high object detection accuracy and computational efficiency on mobile platforms. This study proposes a novel framework that integrates Adaptation (UDA) to address these issues. KD transfers knowledge from a resource-heavy "teacher" model to a lightweight "student" model optimized for mobile deployment, while UDA enables the student model to adapt to real-world conditions without labeled data. Our framework uses YOLOv5 models, where the student model, YOLOv5n, learns from the teacher model, YOLOv5 small, improving precision and maintaining efficiency. Experiments on the VOC2007 and COCO datasets show that our SKD-UDA net achieves 78.2% mAP at IoU 0.5 and 50.8% mAP at IoU 0.5:0.95, outperforming the baseline YOLOv5n by 5.5% and 5.7%, respectively, without increasing the model size (1.9 MB). This approach enhances accuracy and computational efficiency, making it ideal for MAR applications. Our contributions advance object detection in MAR, improving user interaction by increasing detection accuracy, inference speed, and seamless integration of digital and physical environments.
