Hasil untuk "Mechanical engineering and machinery"

Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo et al.

Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Chia-Chin Chuang, Zheng-Wei Lin Chi, Tzu-Chien Kuo et al.

Structural aging and environmental changes associated with long-term operation can substantially modify the thermal behavior of machine tools, diminishing the accuracy of existing thermal compensation models. Traditional neural network approaches typically necessitate time-consuming and inefficient retraining from scratch for practical adaptation. To address this limitation, this study proposes a parameter-based transfer learning technique to enhance model adaptability under evolving machine tool operating conditions. The method establishes a composite fine-tuning architecture by adding hidden layers and selectively freezing neural network parameters, enabling the rapid adaptation of the pretrained model to new thermal characteristics using limited data. A full-factorial experimental design identified the optimal configuration, comprising (i) structural expansion via an LSTM layer inserted after the hidden layers; (ii) a strategy freezing parameters in all layers; and (iii) training under the selected optimal condition (C9), which reflects machine tool characteristics and environmental temperature variations. The baseline model achieved an RMSE of 3.88 µm. Traditional retraining using the complete dataset and retraining only on C9 yielded RMSE values of 3.21 and 3.84 µm, respectively. In contrast, the optimized transfer learning model trained on C9 achieved an RMSE of 3.47 µm. Experimental results demonstrate that the proposed strategy converges with limited data, reducing the number of datasets from 18 to nine and significantly shortening training time from 18 h 20 min to 30 s. This approach offers an effective solution for sustainable model maintenance and expedited industrial deployment.

Xiangyang Zheng, Yancai Xiao, Xinran Li

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis.

Nguyen Khanh Cuong, Nguyen Ngoc-Long, Ho Tien Dung et al.

Type of the article: Research Article AbstractFirms in emerging markets are increasingly compelled to implement digital transformation strategies in response to rapid technological disruption and intensifying global competition. However, the impact of such transformation on organizational performance remains underexplored, particularly in resource-constrained contexts. This study aims to assess how digital orientation and digital capacity influence the implementation of digital transformation, and how digital transformation, in turn, affects organizational agility as well as financial and non-financial performance. Data were collected through a survey of senior managers – those directly responsible for leading digital transformation strategies – at 518 mechanical engineering enterprises in Vietnam, conducted between August and November 2024. The research model was tested using partial least squares structural equation modeling (PLS-SEM). The results reveal that digital orientation (β = 0.585, p < 0.001) and digital capacity (β = 0.240, p < 0.001) significantly promote the adoption of digital transformation. Subsequently, digital transformation exerts a strong positive influence on organizational agility (β = 0.815, p < 0.001). In turn, organizational agility significantly enhances financial performance (β = 0.795, R² = 0.632) and non-financial performance (β = 0.536, R² = 0.287). These findings provide empirical evidence that digital transformation efforts can create practical value when they are grounded in well-aligned internal capabilities. The study contributes to clarifying how enterprises in emerging economies can align digital investments with organizational strengths to improve performance amid volatile environments.

Jinxiu Qu, Yumei Bai, Jiayan Wu et al.

With the increasingly urgent demand on the reliability of mechanical equipment, in the process of production and service, it is of vital importance to apply precise and efficient crack damage detection on the critical structures. To overcome the shortcomings of existing damage detection methods and meet the urgent needs of engineering practice, by analyzing acoustic signal, a novel machinery structural crack damage detection method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and sensitive intrinsic mode function (IMF) fuzzy entropy is proposed in this paper. Firstly, redundant second-generation wavelet denoising strategy based on neighborhood correlation is applied on the raw acoustic signal. Then, the pre-denoised acoustic signal is decomposed by ICEEMDAN to obtain a set of IMFs, and the fuzzy entropies of the first eight IMFs are calculated to reflect the structural crack damage states. Finally, with distance evaluation technique, the most sensitive IMF fuzzy entropy is selected and defined as the damage index to assess the structural crack damage levels. Effectiveness of the proposed method is validated by two case studies as for the crack damage detection of different machinery structures. The results show that the defined damage index is not only sensitive to the occurrence of structural crack damage, but also decreases obviously with the increasing damage level and is not affected by damage location.

Qiang Lin, Xulang Zhou, Wei Hong et al.

Krupa Yevhenii, R. Demchuk

An in-depth analysis of current computer-aided design (CAD) and systems engineering analysis (CAE) software is presented, focusing on SolidWorks and ANSYS. Particular attention is paid to their use in the design and analysis of hydraulic machines, where these tools play an essential role in the development of turbines, pumps, and other components. SolidWorks stands out as the leading tool for creating 3D models of hydraulic assemblies, allowing engineers to optimize designs and reduce hydraulic losses early in the design process. In addition, SolidWorks offers a user-friendly interface and powerful modeling capabilities, allowing you to perform fundamental analyses in a hydraulic simulation environment. A review of the widely used ANSYS program, recognized as a tool for performing complex engineering analyses covering a wide range of physical phenomena, including thermal, mechanical, electromagnetic, and hydrodynamic processes, is performed. The multiphysics capabilities of ANSYS allow engineers to model complex interactions of physical phenomena in a single simulation environment, which is especially important for tasks such as designing power plants or hydro turbines. A comprehensive review of simulation features, including Finite Element Analysis (FEA) and flow modeling, is performed to provide early detection of design problems. The application of ANSYS is proposed for the in-depth analysis of hydrodynamic phenomena occurring in turbines during their operation, which allows the optimization of the geometry of the blades and reduces the risk of cavitation. Furthermore, it is emphasized that integrating both software packages creates a powerful toolkit for engineers, allowing them to combine design and analysis in a single workflow. It is concluded that effective use of SolidWorks and ANSYS can significantly improve the quality of hydraulic machine development, reducing design time and increasing reliability. The article also provides practical examples of the use of these programs in real projects, demonstrating their effectiveness and impact on engineering solutions in the field of mechanical engineering.

Chang Su, Hao Huang, Zhongliang Wei et al.

This paper investigates the application of digital twin technology in the teaching of mining machinery. Digital twin technology, an innovative approach that has significantly transformed engineering education and practice, creates dynamic digital replicas of physical entities in virtual environments. This allows for the simulation, prediction, and optimization of system performance without the need for physical intervention. In mechanical engineering education, digital twins provide students with real-time, interactive learning experiences, enabling a more intuitive understanding of complex mechanical structures. Students can conduct experiments and make modifications within a virtual setting, enhancing both learning efficiency and reducing experimental costs. Furthermore, this technology offers a scalable and replicable model for the future of engineering education.

M. Hasanlu

Testbeds are essential structures in industrial labs for conducting machine testing, where the geometry and material properties play a critical role. These tests often use rotating equipment, such as turbines and turbo-compressors (TC). In order to achieve the best possible conditions, testbeds are created using ANSYS Workbench, which incorporates four mechanical procedures: modal, static, harmonic, and transient analyses. Simulations are used to reproduce attributes such as natural frequency, safety factor, and the highest and lowest levels of mechanical stress. This paper describes a quick way to check if the structure of a turbo-compressor is vibrated, by looking at things like its natural frequency, safety factor, and maximum mechanical stress. This study covers unknown factors and uncertainties by analyzing the operational states of the compressor. To achieve this, a reliability structure model and engineering methods like modal analysis for controlling vibrations, structural analysis to ensure the rotor rotates steadily, transient structural analysis to determine the appropriate startup conditions, and harmonic response analysis to determine how speeds change over time, are used to prevent to natural frequencies from interacting with operational frequencies, finally, transient analysis demonstrates initial shock and vibrations that result maximum stress.

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Christian Tamantini, Christian Tamantini, Kevin Patrice Langlois et al.

E. Blanc‐Betes, N. Gomez‐Casanovas, C. J. Bernacchi et al.

ABSTRACT The expansion of sugarcane onto land currently occupied by improved (IMP) and semi‐native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground‐based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3‐year rotation. With 2.3‐ and 5.1‐fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3‐year rotation (903 ± 335 gC m−2 year−1) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m−2 year−1) but declined in subsequent years to an average 599 ± 90 gC m−2 year−1—well below those of IMP (933 ± 140 gC m−2 year−1) and SN (759 ± 114 gC m−2 year−1) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root‐dominated in pastures to litter‐dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

Sarah Aquino, Joyce Anne Galamgam, E. Eslava et al.

The study quantified nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂) emissions from key agricultural machineries used in rice farming systems across the tropical lowland regions, particularly Cagayan Valley. Fuel consumption and machinery data were obtained from the Department of Agriculture – Regional Agricultural Engineering Division (DA-RAED). Emissions were estimated using IPCC Inventory Software (2006 Guidelines, Tier 1- 2 methodologies), with disaggregation by machinery, gas, and province. Total emissions from the agricultural machinery under study amounted to 143.66 Gg, dominated by hand tractors (130.71 Gg; 90.99%), followed by four-wheel drive tractors (8.37 Gg; 5.83%), rice combine harvesters (4.37 Gg; 3.04%), mechanical rice transplanters (0.10259 Gg; 0.07%), and precision seeders (0.09703 Gg 0.07%). At the provincial level of tropical lowland settings, Isabela and Cagayan recorded the highest emissions (77.75 Gg; 53.04% and 66.03 Gg; 45.02%, respectively), while Nueva Vizcaya and Quirino accounted for the lowest (1.80 Gg; 1.23% and 1.06 Gg; 0.72%, respectively). These findings highlight the environmental impact of mechanization in rice farming and emphasize the need for climate-smart strategies, including improved energy efficiency, adoption of low-emission technologies, and integration of renewable energy sources.

O. Dranko, Aleksander Rezchikov

Structural shifts in the economy in the context of macroeconomic instability determine the promising profile of technologies and products. This paper considers a model for identifying and forecasting fast-growing organizations using the mechanical engineering industry as an example. The threshold of average annual revenue growth of 50% in current prices is used as a criterion for identifying fast-growing organizations. Big data analysis methods are used to identify fast-growing organizations within the segment under consideration. 1.8 thousand fast-growing organizations in the Russian industry with revenues exceeding $\mathbf{1 0 0}$ million rubles have been identified. An assessment of their growth using a sigmoid (logistic curve) shows a significant growth potential of $\mathbf{1 5 0 \%}$. The parameters of the logistic curve are identified using the least squares method. The data source is open data from the electronic Government of Russia, primarily from the Russian Federal Tax Service.

Paulo Flores, H. Lankarani

This work aims at discussing the implementation of a design project to promote the teaching-learning process of kinematics and dynamics of machinery to early undergraduate students as part of the engineering curriculum. For that, the ping-pong ball launcher challenge is chosen as a practical example, in which students, organized in groups of five, must design and develop a mechanical system capable of launching ping-pong balls. In order to make the design project interesting and exciting for students, the manufacturing and construction of physical prototype is also required. The solutions developed by each group of students must incorporate a planar mechanism and should be able to throw a ping-pong ball into a very precise target. For economic and safety reasons, the mechanical systems designed can only be actuated by gravity. There are no specific theoretical pre-requisites for this project. The fundamental topics addressed in this pedagogical activity are kinematics, dynamics, analysis and synthesis of mechanisms, selection of mechanical components and basic manufacturing and assembly of machines. The implementation of this design project embraces several different pedagogical dimensions, such as lectures, laboratory activities, deliverables, written reports, oral presentations and written exams. This work discusses the impact of the proposed design project as an effective tools for teaching kinematics and dynamics of machinery in a non-traditional manner.

Hongpeng Xiao, Zhe Cheng, Zhitao Xing et al.

Rotating machinery is one of the most common and critical types of machinery in engineering. This type of machinery often operates under harsh conditions for extended periods, which makes it prone to failure during use. Therefore, condition monitoring and fault diagnosis can help address issues promptly and reduce the risk of accidents in rotating machinery. Currently, vibration signals are the predominant single modality used for mechanical fault diagnosis. However, unimodal approaches often lack robustness and struggle to provide reliable diagnostics in practical industrial environments. Complementary data sources can provide unique insights into different aspects of physical degradation. To address these limitations, this paper proposes a multimodal domain generalization diagnostic method based on vibration and acoustic signals. First, the method extracts and fuses features using a feature extraction and fusion model. Then, it employs ensemble learning to achieve transfer diagnosis across different operating conditions. This approach effectively resolves issues of insufficient robustness in single-modal diagnosis and diagnosis under varying operating conditions.

Baris Yigin, Metin Çelik

In recent years, advanced methods and smart solutions have been investigated for the safe, secure, and environmentally friendly operation of ships. Since data acquisition capabilities have improved, data processing has become of great importance for ship operators. In this study, we introduce a novel approach to ship machinery monitoring, employing generative adversarial networks (GANs) augmented with failure mode and effect analysis (FMEA), to address a spectrum of failure modes in diesel generators. GANs are emerging unsupervised deep learning models known for their ability to generate realistic samples that are used to amplify a number of failures within training datasets. Our model specifically targets critical failure modes, such as mechanical wear and tear on turbochargers and fuel injection system failures, which can have environmental effects, providing a comprehensive framework for anomaly detection. By integrating FMEA into our GAN model, we do not stop at detecting these failures; we also enable timely interventions and improvements in operational efficiency in the maritime industry. This methodology not only boosts the reliability of diesel generators, but also sets a precedent for prescriptive maintenance approaches in the maritime industry. The model was demonstrated with real-time data, including 33 features, gathered from a diesel generator installed on a 310,000 DWT oil tanker. The developed algorithm provides high-accuracy results, achieving 83.13% accuracy. The final model demonstrates a precision score of 36.91%, a recall score of 83.47%, and an F1 score of 51.18%. The model strikes a balance between precision and recall in order to eliminate operational drift and enables potential early action in identified positive cases. This study contributes to managing operational excellence in tanker ship fleets. Furthermore, this study could be expanded to enhance the current functionalities of engine health management software products.

Hui Zhang, Weiming Che, Youren Cao et al.