Search Results (2,210)

Mechatronics developed under the influence of the Third Industrial Revolution and was a discipline that provided methods and tools for the development of industrial robots, advanced machine tools, mobile phones, and automobiles, among other sophisticated products. With the emergence of Industry 4.0 in 2011, mechatronics has become indispensable, as traditional production systems are being transformed into cyber-physical systems (CPS), some of which are composed of sophisticated technologies such as Digital Twins (DT) and sophisticated robots, among others. In 2020, the Fifth Industrial Revolution began, giving rise to so-called Human Cyber-Physical Systems (HCPS) and promoting the use of Cobots in industries. Because today’s industrial world is influenced by three active industrial revolutions and two transitions, it is possible to find machines and production systems that were designed with different principles and for different purposes, making it necessary to propose a classification that allows each system to be located according to the premises of its respective industrial revolution. This article analyzes the evolution of mechatronics and proposes a classification of machines and production systems based on the premises of each industrial revolution. The objective is to determine the influence of mechatronics on the different types of machines that exist today and analyze its implications. Full article

This study investigates the technological and chemical causes of early zinc-coating degradation on cold-formed steel sections produced from DX51D+Z140 galvanized coils. Commercially manufactured products exhibiting early corrosion symptoms were used in this study. The entire processing route, which included strip preparation, cold rolling, hot-dip galvanizing, passivation, multi-roll forming, storage, and transportation to customers, was analyzed with respect to the residual surface chemistry and process-related deviations that affect the coating integrity. Thirty-three specimens were examined using electromagnetic measurements of coating thickness. Statistical analysis based on the Cochran’s and Fisher’s criteria confirmed that the increased variability in zinc coating thickness is associated with a higher susceptibility to localized corrosion. Surface and chemical analysis revealed chloride contamination on the outer surface, absence of detectable Cr(VI) residues indicative of insufficient passivation, iron oxide inclusions beneath the zinc coating originating from the strip preparation, traces of organic emulsion residues impairing wetting and adhesion, and micro-defects related to deformation during roll forming. Early zinc coating degradation was shown to result from the cumulative action of multiple technological (surface damage during rolling, variation in the coating thickness) and environmental (moisture during storage and transportation) parameters. On the basis of the obtained results, a methodology was proposed to prevent steel product corrosion in industrial conditions. Full article

This study investigates the aerodynamics of Romanian engineer Aurel Persu’s car through wind tunnel experiments involving force measurements, Particle Image Velocimetry (PIV), and CFD simulations. Tests using scale models revealed significant flow separation behind the cabin. The measured drag coefficient is C_D = 0.364 at 33 m/s, showing moderate sensitivity to Reynolds number. CFD simulations using the unsteady STAR CCM+ solver with a k-ω SST turbulence model produced a slightly lower drag coefficient (C_D = 0.353) due to delayed separation. The good agreement between experimental and numerical results validates the modeling approach and highlights aerodynamic limitations around the front and roof. Despite these limitations, the model achieved aerodynamic performance that was exceptional for its time and remained competitive with mainstream production vehicles well into the latter half of the 20th century. Full article

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence. Full article

This paper examines whether information-theoretic complexity measures enhance industry-group return forecasting and portfolio construction within a machine-learning framework. Using daily data for 25 U.S. GICS industry groups spanning more than three decades, we augment gradient-boosted decision tree models with Shannon entropy and fuzzy entropy computed from recent return dynamics. Models are estimated at weekly, monthly, and quarterly horizons using a strictly causal rolling-window design and translated into two economically interpretable allocation rules, a maximum-profit strategy and a minimum-risk strategy. Results show that the top performing strategy, the weekly maximum-profit model augmented with Shannon entropy, achieves an accumulated return exceeding 30,000%, substantially outperforming both the baseline model and the fuzzy-entropy variant. On monthly and quarterly horizons, entropy and fuzzy entropy generate smaller but robust improvements by maintaining lower volatility and better downside protection. Industry allocations display stable and economically interpretable patterns, profit-oriented strategies concentrate primarily in cyclical and growth-sensitive industries such as semiconductors, automobiles, technology hardware, banks, and energy, while minimum-risk strategies consistently favor defensive industries including utilities, food, beverage and tobacco, real estate, and consumer staples. Overall, the results demonstrate that entropy-based complexity measures improve both economic performance and interpretability, yielding industry-rotation strategies that are simultaneously more profitable, more stable, and more transparent. Full article

Nowadays, the automotive industry is primarily driven by the CO₂ policy that targets net zero carbon emissions by 2035 from passenger cars and commercial vehicles. The main path to achieve this goal is the implementation of electric powertrains with the energy stored in batteries, as the case for battery electric vehicles (BEV). However, this technology still faces some difficulties in terms of energy density, overall weight, charging time, and vehicle autonomy. From the other point of view, fuel cell electric vehicles (FCEV) offer the same advantages as BEV in terms of CO₂ reduction, providing better autonomy and lower refueling time. The energy demand by the electric powertrain strongly depends on the vehicle driving conditions as it directly affects energy consumption. In that context, the article aims to study the electrical energy demand of an ultra-energy efficient vehicle intended for a Shell eco-marathon competition in order to minimize hydrogen consumption. The study was carried out over a single lap on the racing track in Nogaro, France while applying the race rules from the competition in 2023. It includes a numerical evaluation of the vehicle resistance forces in different driving strategies and experimental validation on the propulsion test bench. Full article

Whether the New Energy Vehicle Promotion Policy (NEVPP) enhances supply chain resilience is pivotal to China’s green transition and global industrial security. Using data on A-share listed automobile manufacturers from 2012 to 2024, this study employs a multi-period difference-in-differences approach to identify the policy’s impact. Results show that NEVPP significantly strengthens supply chain resilience, and the findings remain robust across alternative specifications. Mechanism analysis reveals that the policy raises managerial attention, eases financing constraints, and stimulates technological innovation, thereby enhancing resilience through managerial, financial, and technological channels. Heterogeneity analysis by ownership, geography, R&D intensity, analyst coverage, and institutional ownership shows that the effect is stronger for state-owned enterprises, firms in central and western regions, low-R&D firms, those without analyst coverage, those with high analyst attention, and firms with low institutional ownership. This study provides firm-level evidence on the economic consequences of NEVPP, advances understanding of industrial policy and corporate resilience, and offers policy implications for supporting the global energy transition and safeguarding supply chain stability. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

With the rapid development of the automotive industry, particularly the year-on-year growth in sales of new energy vehicles, automobile outer panel materials have shown a trend toward high-strength lightweight solutions. Regarding steel for outer panels, existing research has paid less attention to the UF steel that has entered the market in recent years. Moreover, studies on the similarities and differences in deformation behavior among various outer panel steels are lacking. In this study, room-temperature tensile tests at 5% and 8% strain were conducted in accordance with the stamping deformation range on commonly used ultra-low carbon automotive outer panel steels of comparable strength grades, namely, UF340, HC180BD, and DX53D+Z. Prior to deformation, the three materials exhibited similar texture components, predominantly characterized by the γ-fiber texture beneficial for deep drawing, and their room-temperature tensile deformation behaviors were fundamentally identical. After transverse tensile deformation, the textures concentrated towards {111}<112> texture. After 8% deformation, UF340 demonstrated a more rapid stress increase and a higher degree of work hardening. This phenomenon is attributed to the presence of the precipitate free zone (PFZ) near grain boundaries in the UF340, which facilitates the continuous generation of dislocations at grain boundaries during deformation, leading to a rapid increase in dislocation density within the grains. Consequently, this induces accelerated work hardening under small-strain conditions. This mechanism enables UF steels to achieve a strength level comparable to that of bake-hardened (BH) steels, exhibiting a significant performance advantage. Full article

In recent years, artificial intelligence technology has developed rapidly, and the automobile industry has launched autonomous driving systems. However, autonomous driving systems installed in unmanned vehicles still have room to be strengthened in terms of cybersecurity. Many potential attacks may lead to traffic accidents and expose passengers to danger. We explored two potential attacks against autonomous driving systems: stroboscopic attacks and colored light illumination attacks, and analyzed the impact of these attacks on the accuracy of traffic sign recognition based on deep learning models, such as convolutional neural networks (CNNs) and You Only Look Once (YOLO)v5. We used the German Traffic Sign Recognition Benchmark dataset to train CNN and YOLOv5 to establish a machine learning model, and then conducted various attacks on traffic signs, including the following: LED strobe, various colors of LED light illumination and other attacks. By setting up an experimental environment, we tested how LED lights with different flashing frequencies and light color changes affect the recognition accuracy of the machine learning model. From the experimental results, we found that, compared to YOLOv5, CNN has better resilience in resisting the above attacks. In addition, different attack methods will interfere with the original machine learning model to some extent, affecting the ability of self-driving cars to recognize traffic signs. This may cause the self-driving system to fail to detect the presence of traffic signs, or make incorrect decisions about identification results. Full article

Public transport still uses vehicles powered by fossil fuels. Replacing the fleet with zero-emission vehicles will take many years. During this period, it is still necessary to carry out work aimed at reducing energy consumption and thus the emission of toxic substances into the atmosphere. An important part of this work is the study of the relationship between energy demand of buses with different power plants and urban traffic conditions. These conditions include traffic intensity, average and maximum speeds, and number of stops. The VSP (Vehicle-Specific Power) model is useful in research on this relationship. In this article, such research was carried out using data from public bus monitoring and data provided by the city authorities of Rzeszów. In the first stage, a VSP model was created and tuned for three buses with different power plants operating on selected routes. Then, as a result of a large number of simulation processes, the impact of the average speed on the energy demand was determined. The results of the conducted research can be used in the modernization or planning of public transport networks and the modification of road infrastructure. All these activities should contribute to reducing energy consumption and environmental pollution. Full article

The steering linkage represents a key subsystem of any automobile, playing a direct role in vehicle handling, driving safety, and overall comfort. Within this mechanism, the tie rod and tie rod end are crucial for transmitting steering forces from the gear to the wheel hub. A typical issue that gradually develops in these components is the clearance appearing in the spherical joint, caused by wear, corrosion, and repeated operational stresses. Even small clearances can noticeably reduce stiffness and natural frequencies, making the system more sensitive to vibration and premature failure. In this work, the effect of spherical joint clearance on the dynamic behavior of the tie rod-tie rod end assembly was analyzed through numerical simulation combined with experimental observation. Three-dimensional CAD models were meshed with tetrahedral elements and subjected to modal analysis under several clearance conditions, while boundary constraints were set to replicate real operating conditions. Experimental measurements on a dedicated test rig were used to assess joint clearance and wear in service parts. The results indicate a strong nonlinear relationship between clearance magnitude and modal response, with PTFE bushing degradation identified as the main source of clearance. These findings link the evolution of clearance to the change in vibration characteristics, providing useful insight for diagnostic approaches and predictive maintenance aimed at improving steering reliability and vehicle safety. Full article

Driven by Industry 5.0, efficient obstacle avoidance of robotic arms in dynamic environments is a key bottleneck for human–robot collaboration in smart manufacturing. Traditional path planning methods such as Rapidly-exploring Random Tree and artificial potential field work stably in static settings but exhibit flaws including path oscillation and poor real-time performance under dynamic obstacles. Deep reinforcement learning adapts to environmental changes but is limited by low sample efficiency and high computational costs, failing industrial demands. This study proposes a collaborative framework integrating improved Rapidly-exploring Random Tree Star and Deep reinforcement learning. It uses Rapidly-exploring Random Tree Star to guide Deep reinforcement learning’s strategy exploration, reducing invalid sampling by 62%, and leverages Deep reinforcement learning’s global optimization to enhance dynamic obstacle prediction. The framework achieves a task success rate of 93.8%, surpassing traditional Rapidly-exploring Random Tree Star by 21.5%, with an average path length of 1.97 m and system energy consumption of 12.6 kWh. Experiments demonstrate superior performance in extreme dynamic scenarios, including a 94.7% success rate in multi-robot collaboration. Industrial cases confirm improvements in automobile manufacturing assembly cycle time to 8.4 s per task, yield rate to 98.7%, and reductions in energy consumption by 34% and human intervention by 85.6%, providing a reliable dynamic obstacle avoidance solution for Industry 5.0 applications. Full article

The increasing atmospheric carbon dioxide (CO₂) emissions are widely recognized as the primary driving force behind the phenomenon of global warming. Considering environmental concerns and the depletion of fossil fuel reserves, the use of electric vehicles (EVs) in transportation has emerged as one of the most promising technological alternatives to conventional gasoline-powered cars. Compared to their gasoline counterparts, EVs significantly reduce the costs associated with air pollution and mitigate adverse effects on human health. Owing to these characteristics, EVs have become one of the key components of the transition toward a sustainable future, while also steering the transformation of the global automotive industry. This transition is reshaping the structure of the global automobile industry. Many countries aim to achieve their greenhouse gas reduction targets by promoting the adoption of EVs. This study aims to empirically examine the effects of electric vehicles on CO₂ emissions in 15 high-income countries during the period 2010–2023, highlighting both short- and long-term environmental impacts. The analysis also considers economic and socio-demographic variables such as gross domestic product (GDP), urbanization, and fossil fuel consumption. The findings indicate that the share of EVs significantly reduces CO₂ emissions, whereas sales have a short-term increasing effect. Full article

Over recent decades, planners in the U.S. have increasingly adopted mixed-use projects to reduce automobile dependency and strengthen local community identity, although results remain inconsistent across cities. Urban health and fitness outcomes are shaped by complex interactions between the built environment, socioeconomic factors, and demographic characteristics. This study introduces a Health and Fitness Index (HFI) for 28,758 U.S. ZIP codes, derived from normalized measures of walkability, healthcare facility density, and carbon emissions, to assess spatial disparities in health-supportive environments. Using four modeling approaches—lasso regression, multiple linear regression, decision trees, and k-nearest neighbor classifiers—we evaluated the predictive importance of 15 urban and socioeconomic variables. Multiple linear regression produced the strongest generalization performance (R² = 0.60, RMSE = 0.04). Key positive predictors included occupied housing units, business density, land-use mix, household income, and racial diversity, while income inequality and population density were negatively associated with health outcomes. This study evaluates five statistical formulations (Metropolis Hybrid Models) that incorporate different combinations of walkability, land-use mix, environmental variables, and socioeconomic indicators to test whether relationships between urban form and socioeconomic conditions remain consistent under different variable combinations. In cross-sectional multivariate regression, although mixed-use development in high-density areas is strongly associated with healthcare facilities, these areas tend to serve younger and more racially diverse populations. Decision tree feature importance rankings and clustering profiles highlight structural inequalities across regions, suggesting that enhancing business diversity, land-use integration, and income equity could significantly improve health-supportive urban design. This research provides a data-driven framework for urban planners to identify underserved neighborhoods and develop targeted interventions that promote walkability, accessibility to health infrastructure, and sustainability. It contributes to the growing literature on urban health analytics, integrating machine learning, spatial clustering, and multidimensional urban indicators to advance equitable and resilient city planning. Full article