The study investigates the combined effects of material selection, manufacturing constraints, and a dynamic stress constraint function on the resulting material distribution achieved through a structural optimization process, while ensuring full compliance with the relevant European assessment standards for railway bogie. A high-fidelity finite element model of the complete bogie system was developed to accurately reproduce the operational loads and the structural interactions between the motor support and its surrounding components. The proposed methodology integrates topology optimization within a manufacturability-oriented framework, enabling a systematic evaluation of the influence of material properties, draw direction, and minimum feature size on the optimized configuration. In this context, an adaptive stress coefficient, derived from the performance of the original component, was introduced and proved effective in improving both the material distribution and the resulting stress levels of the optimized design. The results demonstrate that the combined consideration of material selection, manufacturing constraints, and adaptive stress control leads to a structurally efficient and production-feasible design. Three different materials were tested, showing consistent stress distributions and mass savings across all cases. The innovative optimized configuration achieved over 16% mass reduction while maintaining admissible stress levels. The proposed approach provides a generalizable and standard-compliant framework for future applications of topology optimization in railway engineering.
Mechanical engineering and machinery, Machine design and drawing
R. Różycki, Dorota Agnieszka Solarska, Grzegorz Waligóra
The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO2 emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future.
PurposeCryptocurrency markets are gaining popularity, with over 23,000 cryptocurrencies in 2023 and a total market valuation of 870.81 billion USD in 2023. With its increasing popularity, cryptocurrencies are also susceptible to volatility. Predicting the price with the least fallacy or more accuracy has become the need of the hour as it significantly influences investment decisions.Design/methodology/approachThis study aims to create a dynamic forecasting model using the ensemble method and test the forecasting accuracy of top 15 cryptocurrencies’ prices. Statistical and econometric model prediction accuracy is examined after hyper tuning the parameters. Drawing inferences from the statistical model, an ensemble model using machine learning (ML) algorithms is developed using gradient-boosted regressor (GBR), random forest regressor (RFR), support vector regression (SVR) and multi-layer perceptron (MLP). Validation curves are utilized to optimize model parameters and boost prediction accuracy.FindingsIt is found that when the price movement exhibits autocorrelation, the autoregressive integrated moving average (ARIMA) model and the ensemble model performed better. ARIMA, simple linear regression (SLR), random forest (RF), decision tree (DT), gradient boosting (GB) and multi-model regression (MLR) ensemble models performed well with coins, showing that trends, seasonality and historical price patterns are prominent. Furthermore, the MLR approach produces more accurate predictions for coins with higher volatility and irregular price patterns.Research limitations/implicationsAlthough the dataset includes crisis period data, anomalies or outliers are yet to be explicitly excluded from the analysis. The models employed in this study still demonstrate high accuracy in predicting cryptocurrency prices despite these outliers, suggesting that the models are robust enough to handle unexpected fluctuations or extreme events in the market. However, the lack of specific analysis on the impact of outliers on model performance is a limitation of the study, as it needs to fully explore the resilience of the forecasting models under adverse market conditions.Practical implicationsThe present study contributes to the body of literature on ensemble methods in forecasting crypto price in general, potentially influencing future studies on price forecasting. The study motivates the researchers on empirical testing of our framework on various asset classes. As a result, on the prediction ability of ensemble model, the study will significantly influence the decision-making process of traders and investors. The research benefits the traders and investors to effectively develop a model to forecast cryptocurrency price. The findings highlight the potential of ensemble model in predicting high volatile cryptocurrencies and other financial assets. Investors can design the investment strategies and asset allocation decisions by understanding the relationship between market trends and consumer behavior. Investors can enhance portfolio performance and mitigate risk by incorporating these insights into their decision-making processes. Policymakers can use this information to design more effective regulations and policies promoting economic stability and consumer welfare. The study emphasizes the need for using diversified model to understand the market dynamics and improving trading strategies.Originality/valueThis research, to the best of our knowledge, is the first to use the above models to develop an ensemble model on the data for which the outliers have not been adjusted, and the model still outperformed the other statistical, econometric, ML and deep learning (DL) models.
Abstract With the rapid development of science and technology, various types of industrial robots occupy an important role in industrial production. Therefore, the performance testing of industrial robots is very important. In response to the low accuracy in performance testing of industrial robots operating under extreme working conditions using traditional testing devices, a performance testing device for industrial robots is designed using a dedicated servo motor model and programmable logic controller. To test the proposed testing device, comparative experiments are conducted. The results showed that when the temperature varied between − 40 ℃ and 80 ℃, the motor operating speed of the proposed device was around − 2,993 r/min, which was better than the comparative device. It was not significantly affected by temperature. Under over-speed conditions of 1.0 times, 1.2 times, 1.4 times, and 1.6 times, the motor temperatures of the proposed device after operating 20 min were 36 ℃, 40 ℃, 45 ℃, and 53 ℃, respectively, which were significantly lower than those of the comparative device. In summary, the proposed industrial robot performance testing device based on ECMA servo motor and PLC can maintain a stable state under extreme conditions, providing an appropriate idea for the performance testing of industrial robots and ensuring a guarantee for the design of industrial robots.
The paper primarily focuses on the control strategy of an electric dump truck equipped with an M100 methanol range extender. In response to the significant adverse impact of the constant power control strategy on the lifespan of power batteries and the large rotational speed fluctuations of range extenders under the power-following control strategy, a constant-speed and variable-torque range extender control strategy based on the rule-based control strategy is proposed. This strategy enables power following within the range of 70 kW to 130 kW and fixed-point operation at 50 kW and 150 kW. Through co-simulation using AVL Cruise and MATLAB R2022b/Simulink, the results indicate that under the China Heavy-duty Commercial Vehicle Test Cycle-Dynamic (CHTC-D), with an average vehicle speed of 23.19 km/h, the constant-speed and variable-torque range extender control strategy achieves a higher methanol saving rate compared to both the constant power control strategy and the power-following control strategy, thereby demonstrating better fuel economy. The methanol consumption per 100 km for the dump truck using the constant power control strategy, the power-following control strategy, and the constant-speed and variable-torque control strategy are 62.89 L, 64.49 L, and 62.53 L, respectively. Compared with the same type of diesel range-extended electric dump truck, its fuel usage cost has a significant advantage.
Mechanical engineering and machinery, Machine design and drawing
Barouch Giechaskiel, Christian Ferrarese, Theodoros Grigoratos
et al.
Tyre wear is a major contributor to global microplastic pollution, affecting air, soil, water, and wildlife as well as human health. In the European Union (EU), the latest Euro 7 regulation foresees the introduction of tyre abrasion limits covering all tyre categories, referring to two testing methods (convoy on road or laboratory drum) developed by the United Nations (UN) Economic Commission for Europe (UNECE) World Forum for Harmonization of Vehicle Regulations (WP.29). In this study, we applied the convoy method adopted by the UNECE Working Group on Noise and Tyres (GRBP) as part of the UN Regulation 117 on tyre performance parameters. The method has been developed by the Task Force on Tyre Abrasion (TFTA) of the UNECE and involves vehicles driving on public roads for about 8000 km. Candidate and reference tyres are fitted in a convoy of up to four vehicles, and an abrasion index for each candidate tyre is determined as a ratio of the abrasion of the candidate and reference tyres. In our tests, in addition to the abrasion rate, we measured the tread depth reduction and defined a service life index (i.e., total mileage potential) without the need of a different methodology. The results from six summer and nine winter C1 class passenger car tyres of various sizes showed a wide range of abrasion rates and service life values. We also compared our results with values reported in the literature and on websites. The conclusions of this study are expected to support the ongoing discussion on limit setting for C1 tyres and the definition of a service life index.
Mechanical engineering and machinery, Machine design and drawing
This study applied the DMAIC (Define, Measure, Analyze, Improve, Control) methodology to improve the operational efficiency of a facility’s bottle cleaning process. The Define Phase identified critical issues, including a high rework rate (15%), inconsistent cleaning quality (84% efficiency), and excessive water and detergent consumption. The Measure Phase established baseline data to quantify these inefficiencies. Root causes were identified in the Analyze Phase using a Fishbone Diagram and Failure Mode and Effects Analysis (FMEA), highlighting manual detergent dosing, unstable wash temperatures, lack of standard operating procedures (SOPs), and inconsistent employee performance as major contributors. During the Improve Phase, remedies that were specific to the problem were put into place. These included automating the delivery of detergent and controlling the temperature, adding real-time monitoring, updating standard operating procedures (SOPs), and giving employees more training. Because of this, the cleaning efficiency went up to 92%, the rework rate went down to 7.5%, and the inspection pass rate went up to 92%. Resource usage decreased significantly – detergent by 23% and water by 20% – resulting in annual cost savings of $23,404.24. These gains were sustained through the Control Phase via continuous monitoring, regular audits, and periodic staff retraining. The project shows that DMAIC may help improve operations and lower costs in a way that lasts. It gives other plants a model to follow if they want to improve their manufacturing processes while making them better for the environment and quality. Combining automation, data collection, analysis, and a standardized workflow within a framework for improvement led to rapid gains and long-term process control.
Machine design and drawing, Engineering machinery, tools, and implements
Walid Alqaisi, Mostafa Soliman, Ahmed Badawi
et al.
Abstract This paper presents a quadrotor detection and tracking system that uses a single camera and the Speeded Up Robust Features (SURF) algorithm to extract the image structure and estimate the target's position. The goal is to develop an independent system capable of detecting a followed object’s motion without prior knowledge of its trajectory, without requiring communication with the target, and without relying on external sensors. Feedback Linearization (FL) combined with sliding mode control is used to ensure target tracking. The control system avoids the complex nonlinear control solutions and the highly coupled dynamic behavior of the quadrotor. Nonlinear uncertain disturbances are overcome by using Time Delay Estimation (TDE) of the disturbance.
Sugiri Sugiri, Mochamad Bruri Triyono, Yosef Budiman
et al.
Modern automotive design has increasingly embraced plastics for bumper construction; however, it can lead to material degradation. To overcome these limitations, the automotive industry is turning to fiber–resin material, namely carbon–epoxy composites. Our research focuses on determining the effects of fiber orientation and angle alignment on the structural stress of the car bumper, examining the hybrid material (carbon–epoxy reinforced by CFRP) in static structural tests, and performing dynamic impact tests at various speeds, applying the Tsai–Wu criterion as a basic failure model. However, Tsai–Wu’s failure in numerical analysis highlights the limitation of not being able to experimentally distinguish between failure modes and their interaction coefficients. To address this issue, we employ ANSYS<sup>®</sup> 2024 R1 with a Fortran program, which enables more accurate estimation of failure behavior, resulting in an average error of 13.19%. To identify research gaps, machine learning (ML) plays a vital role in predicting parameter values and assessing data normality using various algorithms. By combining ML and FEA simulations, the result shows strong data performance. Bridging from 2 mm mesh sizing of 50% carbon–epoxy woven/50% CFRP laminate in 6 mm thickness at 0° orientation shows the most distributed shear stresses and deformation, which converged toward stable values. For comprehensive research, total deformation was included in ML analysis as a second target to build a multivariate analysis. Overall, Random Forest (RF) is the best-performing model, indicating superior robustness for modeling shear stress and total deformation.
Mechanical engineering and machinery, Machine design and drawing
Pamon Pumas, Maliwan Puangmanee, Pimpawat Teeratitayangkul
et al.
This study examines the behavioral and social factors influencing household waste separation practices in Keelek Subdistrict Municipality, Chiang Mai Province, Thailand. Drawing on survey data and a mixed‐methods approach that integrates correlation analysis, principal component analysis, and a two‐stage machine‐learning pipeline—further validated by confirmatory structural equation modeling of Attitude → Intention → Behavior and mapped onto an established nudge taxonomy—the research identifies the most influential predictors of separation behavior. These include routine organic waste sorting, behavioral intention, emotional commitment, and the perceived influence of community members and local authorities. Among the tested models, Gradient Boosting Regression yielded the highest predictive accuracy (R2 = 0.782; MAE = 0.331), underscoring its ability to capture complex non-linear behavioral patterns more effectively than traditional approaches. By uniting behavioral theory, community-derived insights, and predictive analytics, this work advances a novel, transferable framework for municipal planning. It offers practical, ESG/SDG–aligned strategies—such as habit-based, peer-supported nudges and AI-powered monitoring systems—that local governments can adopt to design evidence-based waste policies. Focusing on a semi-urban context often overlooked in the literature, this study fills a critical methodological gap and charts a replicable pathway for scaling behaviorally informed waste-management interventions.
Simon van Wageningen, Tamara Mchedlidze, Alexandru C. Telea
Graph drawings are commonly used to visualize relational data. User understanding and performance are linked to the quality of such drawings, which is measured by quality metrics. The tacit knowledge in the graph drawing community about these quality metrics is that they are not always able to accurately capture the quality of graph drawings. In particular, such metrics may rate drawings with very poor quality as very good. In this work we make this tacit knowledge explicit by showing that we can modify existing graph drawings into arbitrary target shapes while keeping one or more quality metrics almost identical. This supports the claim that more advanced quality metrics are needed to capture the 'goodness' of a graph drawing and that we cannot confidently rely on the value of a single (or several) certain quality metrics.
We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the FLM is the first architecture able to represent a multidimensional Fourier series with a complete set of basis functions in separable form, doing so by using a standard Multilayer Perceptron-like architecture. A one-to-one correspondence between the Fourier coefficients and amplitudes and phase-shifts is demonstrated, allowing for the translation between a full, separable basis form and the cosine phase-shifted one. Additionally, we evaluate the performance of FLMs on several scientific computing problems, including benchmark Partial Differential Equations (PDEs) and a family of Optimal Control Problems (OCPs). Computational experiments show that the performance of FLMs is comparable, and often superior, to that of established architectures like SIREN and vanilla feedforward NNs.
This paper presents the design and outcomes of SketchPath, a system that uses hand-drawn toolpaths to design for clay 3D printing. Drawing, as a direct manipulation technique, allows artists to design with the expressiveness of CAM-based tools without needing to work with a numerical system or constrained system. SketchPath works to provide artists with direct control over the outcomes of their form by not abstracting away machine operations or constraining the kinds of artifacts that can be produced. Artifacts produced with SketchPath emerge at a unique intersection of manual qualities and machine precision, creating works that blend handmade and machine aesthetics. In interactions with our system, ceramicists without a background in CAD/CAM were able to produce more complex forms with limited training, suggesting the future of CAM-based fabrication design can take on a wider range of modalities.
Abstract This paper describes a graph-based SLAM approach using wall detection and floor plan constraints without relying on loop closure. In SLAM, loop closure is widely used to address cumulative errors. Although loop closure helps maintain the map’s relative consistency, it does not ensure the accuracy of absolute positions. Therefore, we focus on floor plans that accurately depict the environmental geometry and propose a SLAM method that leverages this information. However, floor plans do not depict semi-static objects such as bookshelves and other fixtures. Thus, our study aims to build accurate maps based on floor plans and represent actual environments. The proposed method achieves this goal by integrating wall detection and floor plan constraints within the framework of graph-based SLAM. We evaluated the proposed method based on qualitative assessments of mapping results and quantitative evaluations of robot trajectories and processing time. Experiments were conducted using datasets obtained from both simulation and real-world environments. The results demonstrate that the proposed method can build a map with accurate absolute positions in a low processing time by leveraging wall detection and floor plan constraints.
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment, in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.
Zulkifli Mansor, Addie Irawan, Mohammad Fadhil Abas
This comprehensive review delves into the realm of bipedal wheel-legged robots, focusing on their design, control, and applications in assistive technology and disaster mitigation. Drawing insights from various fields such as robotics, computer science, and biomechanics, it offers a holistic understanding of these robots' stability, adaptability, and efficiency. The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. Emphasizing the need for adaptable, terrain-aware control algorithms, the review explores the untapped potential of machine learning and soft robotics in enhancing performance across diverse operational scenarios. It highlights the advantages of hybrid models combining legged and wheeled mobility while stressing the importance of refining control frameworks, trajectory planning, and human-robot interactions. The concept of integrating soft and compliant mechanisms for improved adaptability and resilience is introduced. Identifying gaps in current research, the review suggests future directions for investigation in the fields of robotics and control engineering, addressing the evolution and terrain adaptability of bipedal wheel-legged robots, control, stability, and locomotion, as well as integrated sensory and perception systems, microcontrollers, cutting-edge technology, and future design and control directions.
Luke Stark, Syed Mustafa Ali, Stephanie Dick
et al.
Drawing on prior work in the history and philosophy of statistics, I argue that in many cases analyses powered by artificial-intelligence (AI) techniques such as machine learning (ML) are fundamentally ‘conjectural’: reliant on ex post facto abductive logics often misinterpreted in contemporary machine-learning systems as reliably reproducible truth. Here I relate what Carlo Ginzburg calls ‘the conjectural sciences’ as a historical category to their contemporary instantiation in machine learning and the practice of ‘automated conjecture’. I observe how the automation of physiognomic and phrenological concepts are exemplary of the ways in which discredited conjectural pseudosciences are being revived by today's AI research. Finally, I argue that the conceptual distinction between ‘conjectural’ and ‘empirical’ science can help support contemporary efforts to regulate the design and use of AI systems by providing conceptual and historical justification for the non-development of certain classes of systems intended to automate inference.
This paper proposes a control strategy for active lateral secondary suspension that uses preview data. Based on a derived analytical model, a model predictive controller (MPC) is implemented. The influence of the track irregularities upon carbody lateral dynamics is considered explicitly. The controller developed is applied to a full-scale rail vehicle model. Ride comfort is evaluated according to EN 12299. Multibody simulations show that there is a significant increase in continuous ride comfort on poor-quality tracks.
Mechanical engineering and machinery, Machine design and drawing
Carla Binucci, Aaron Büngener, Giuseppe Di Battista
et al.
The study of nonplanar drawings of graphs with restricted crossing configurations is a well-established topic in graph drawing, often referred to as beyond-planar graph drawing. One of the most studied types of drawings in this area are the $k$-planar drawings $(k \geq 1)$, where each edge cannot cross more than $k$ times. We generalize $k$-planar drawings, by introducing the new family of min-$k$-planar drawings. In a min-$k$-planar drawing edges can cross an arbitrary number of times, but for any two crossing edges, one of the two must have no more than $k$ crossings. We prove a general upper bound on the number of edges of min-$k$-planar drawings, a finer upper bound for $k=3$, and tight upper bounds for $k=1,2$. Also, we study the inclusion relations between min-$k$-planar graphs (i.e., graphs admitting min-$k$-planar drawings) and $k$-planar graphs. In our setting we only allow simple drawings, that is, any two edges cross at most once, no two adjacent edges cross, and no three edges intersect at a common crossing point.