Separated cable-driven parallel robots are widely deployed in large-scale and high-dynamic applications due to their flexible configurations. However, structural parameter deviations, specifically anchor point shifts caused by prolonged operation or equipment reconfiguration, often compromise initial pose accuracy. To address this, this paper proposes a rapid initial pose calibration method. By utilizing incremental cable length changes under controlled platform perturbations, a nonlinear mapping model is formulated to resolve unknown anchor positions and platform poses. The method integrates the Improved Snow Ablation Optimizer with the Levenberg–Marquardt algorithm, employing a rolling iteration mechanism to simultaneously identify anchor point errors and the initial pose. Validation experiments on a custom-built prototype demonstrate a mean positioning error of 1.2 mm under static conditions. These results confirm the method’s high calibration accuracy, ensuring the geometric precision required for subsequent high-dynamic operations.
Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.
Bianca Trinkenreich, Fabio Calefato, Geir Hanssen
et al.
The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.
Helical springs are used for many mechanisms. Rectangular wire helical springs are used in machines that require large spring loads, such as press machines, die machines, injection molding machines, construction machines, and load testing machines. Design formulas for the rectangular wire helical springs were given by Liesecke. However, pitch angle of the helical spring is neglected in his formulas, and they are inconvenient because we have to read factors used in the formulas from graphs. And, Shimizu et al. derived a theoretical equation, but there are still differences between values calculated by the equations and the FEM analysis results although a trend is consistent. And, the practical design equations are desired to be simple. Therefore, in this paper, simple practical design equations of the spring constant and the maximum shear stress are derived by using a fractional expression to FEM results by focusing on that the displacement and the stress generated in the helical spring are mainly caused by a tortional moment to the spring wire. Errors of the spring constant equations to the FEM results are less than 3 percents and errors of the maximum shear stress equation to the FEM results are less than 3.5 percents. Therefore, these equations are very useful for the practical design of the rectangular wire helical springs.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
AbstractMechanical penetrations, as important pressure pipelines penetrating the reactor compartment, withstand high temperatures and pressures. The current complete design and verification process for mechanical penetrations. This article focuses on the problem of stress concentration and easy damage of the penetration components in the reactor compartment under high temperature and high pressure environment. Combining with the existing regulations of nuclear power plants and ships, finite element analysis method is used to analyze the stress of the penetration components under specific high temperature and high pressure and ship ultimate load coupling. At the same time, based on the simulation analysis results, the structural dimensions of the penetration components are optimized, and a mechanical penetration verification process is designed. The coupled thermal stress results of the penetration indicate that the stress of the penetration is too large at the tail of the sleeve, with the values of primary film stress Pm and primary bending stress Pb being 228.2 and 275.91 MPa, respectively. From this, it can be seemed that there is obvious stress concentration at the junction of the support ring and sleeve, as well as at the transition point of the insulation layer, which is the weakest area of the penetration.
AbstractFiber bundles are an important component of woven composite materials, and predicting the mechanical properties of fiber bundles can provide a basis for the study of the mechanical properties of woven composite materials. This paper establishes the micro representative volume element (RVE) model of composite materials, and obtains the equivalent elastic constant of yarn through the model homogenization theory and periodic boundary conditions. Strength prediction is performed through the VUMAT user subroutine of ABAQUS. This paper uses the maximum stress standards and Von Mises standards to predict the damage initiation of TC33 carbon fiber and epoxy resin matrix, respectively. Combined with the constant degradation method, the simulation of the damage behaviors of the micro model is achieved, and the equivalent strength of the fiber bundle is obtained. The effectiveness and correctness of this method are verified by comparing the numerical model results with the Chamis theoretical model results. The accurate prediction of mechanical properties and damage process of fiber bundles provides theoretical support for the analysis of mechanical properties of composites, and has guiding significance for the performance design of composite materials.
Rotating machinery is widely used in industrial applications, where its failure can lead to significant operational downtime and costly repairs. Traditional fault detection techniques, such as vibration analysis and statistical methods, are often insufficient for handling complex fault patterns and large datasets. This paper presents an exploration of machine learning (ML) algorithms for fault detection in rotating machinery. Specifically, it investigates how ML models can enhance predictive maintenance systems, detect anomalies, and classify faults in real-time. The paper reviews the application of various ML algorithms, including support vector machines (SVM), decision trees, neural networks, and ensemble methods. The benefits of using vibration signals, acoustic signals, and other sensor data for training ML models are discussed. Finally, case studies and future trends in the use of deep learning for fault detection in rotating machinery are also explored.
Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics.
A flaky test yields inconsistent results upon repetition, posing a significant challenge to software developers. An extensive study of their presence and characteristics has been done in classical computer software but not quantum computer software. In this paper, we outline challenges and potential solutions for the automated detection of flaky tests in bug reports of quantum software. We aim to raise awareness of flakiness in quantum software and encourage the software engineering community to work collaboratively to solve this emerging challenge.
The present study was carried out at Department of Farm Machinery and Power Engineering, Punjab Agricultural University, Ludhiana, India during May to December, 2017 to evaluate the effect of mechanical cleaning methods on mechanically harvested cotton. The treatments consisted of three cotton cleaning methods viz. Boll Crusher cum Seed-Cotton Extractor, Pre-cleaner and On-board cleaner (field cleaner). The comparative performance of these methods was evaluated in terms of cotton fibre quality parameters viz. span length, uniformity ratio, elongation (%), micronaire (%), fibre strength (g tex-1) and reflectance of cotton lint etc. The minimum trash content (seed cotton basis) was observed for boll crusher+pre-cleaner (5.17%) and maximum for on-board cleaner (21.4 %). The 2.5% span length for the manual was observed maximum (26.04 mm) and the minimum for boll crusher+pre-cleaner (23.83 mm). The uniformity ratio observed was minimum for manual (45.99) and maximum for boll crusher+pre-cleaner (47.29). The micronaire for the manual was observed as a minimum (3.97%) and maximum for boll crusher+pre-cleaner (3.57%). The fibre strength for manual (20.50 g tex-1) was maximum whereas it was minimum for boll crusher+pre-cleaner (19.36 g tex-1). The reflectance for manual (0.87) was observed as minimum and maximum for boll crusher+pre-cleaner (0.81). Based upon the fiber quality parameters of harvested cotton cleaned by boll crusher machine was of superior quality but it was inferior in quality for the boll crusher+pre-cleaner machine.
Franziska Scheibel, Wei Liu, Lukas Pfeuffer
et al.
A multi-stimuli cooling cycle can be used to increase the cyclic caloric performance of multicaloric materials like Ni-Mn-In Heusler alloys. However, the use of a uniaxial compressive stress as an additional external stimulus to a magnetic field requires good mechanical stability. Improvement of mechanical stability and strength by doping has been shown in several studies. However, doping is always accompanied by grain refinement and a change in transition temperature. This raises the question of the extent to which mechanical strength is related to grain refinement, transition temperature, or precipitates. This study shows a direct comparison between a single-phase Ni-Mn-Sn and a two-phase Gd-doped Ni-Mn-In alloy with the same transition temperature and grain size. It is shown that the excellent magnetocaloric properties of the Ni-Mn-In matrix are maintained with doping. The isothermal entropy change and adiabatic temperature change are reduced by only 15% in the two-phase Ni-Mn-In-Heusler alloy compared to the single-phase alloy, which is resulting from a slight increase in thermal hysteresis and the width of the transition. Due to the same grain size and transition temperature, this effect can be directly related to the precipitates. The introduction of Gd precipitates leads to a 100% improvement in mechanical strength, which is significantly lower than the improvement observed for Ni-Mn-In alloys with grain refinement and Gd precipitates. This reveals that a significant contribution to the improved mechanical stability in Gd-doped Heusler alloys is related to grain refinement.
Software companies have widely used online A/B testing to evaluate the impact of a new technology by offering it to groups of users and comparing it against the unmodified product. However, running online A/B testing needs not only efforts in design, implementation, and stakeholders' approval to be served in production but also several weeks to collect the data in iterations. To address these issues, a recently emerging topic, called "Offline A/B Testing", is getting increasing attention, intending to conduct the offline evaluation of new technologies by estimating historical logged data. Although this approach is promising due to lower implementation effort, faster turnaround time, and no potential user harm, for it to be effectively prioritized as requirements in practice, several limitations need to be addressed, including its discrepancy with online A/B test results, and lack of systematic updates on varying data and parameters. In response, in this vision paper, I introduce AutoOffAB, an idea to automatically run variants of offline A/B testing against recent logging and update the offline evaluation results, which are used to make decisions on requirements more reliably and systematically.
Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic datasets emerge as a viable alternative. However, practitioners are often uncertain about generating high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. This study aims to fill this knowledge gap by proposing comprehensive guidelines for generating, annotating, and validating synthetic datasets. The trade-offs and methods associated with each of these aspects are elaborated upon. Further, the practical implications of these guidelines are illustrated through the creation of a turbo-compressors dataset. The study underscores the importance of thoughtful sampling methods to ensure the appropriate size, diversity, utility, and realism of a dataset. It also highlights that design diversity does not equate to performance diversity or realism. By employing test sets that represent uniform, real, or task-specific samples, the influence of sample size and sampling strategy is scrutinized. Overall, this paper offers valuable insights for researchers intending to create and publish synthetic datasets for engineering design, thereby paving the way for more effective applications of AI advancements in the field. The code and data for the dataset and methods are made publicly accessible at https://github.com/cyrilpic/radcomp .
Cubic SrVO _3 perovskite oxide is an attractive candidate for high-temperature energy applications due to its favorable features such as multiple oxidation state cations, high structural and thermal stabilities, ability to accommodate a large number of oxygen vacancies, and cost-effectiveness. Herein, the temperature-dependent reduction properties of SrVO _3 have been studied using accurate first-principles calculations to reveal the effects of oxygen vacancies and temperature on the reduction potential of SrVO _3− _δ , δ = 0–0.125. The reduction potential of SrVO _3− _δ was found to be significantly impacted by increasing oxygen vacancy concentration and temperature. Analysis of the electronic and vibrational properties of SrVO _3− _δ for differing δ revealed the origin of this reduction behavior. The electronic structure analysis shows that the reduction of SrVO _3− _δ upon oxygen vacancy formation is highly localized to the neighboring V ^4+ t _2g states in the vicinity of the oxygen defect, irrespective of δ . A comparison of the vibrational density of states of defect-free and reduced SrVO _3 demonstrated that the ionic contributions to the phonon density of states, and hence to the thermal contributions to the SrVO _3− _δ lattices, were significantly altered by the introduction of oxygen vacancies, which ultimately impacted the temperature-dependent reduction behavior of SrVO _3− _δ .
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Hatem H. Almasaeid, Abdelmajeed Alkasassbeh, B. Yasin
Abstract A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with R2 of 0.9286.
With the systematization, complexity, and intelligence attributes of modern machinery, a large number of rolling bearings are used in various mechanical equipment. As the core component of the mechanical equipment, the health state of the rolling bearing is of vital importance as its failure can affect the normal operation of the equipment and threaten the safety of people's lives and property. At the same time, with various current testing equipment used in machine operation and maintenance, a large amount of data has been collected and used to predict the health status of the bearings. In this context, we analyzed the rolling bearing fault mechanism and its diagnosis techniques, then proposed the method to do the rolling bearing fault diagnosis by using deep learning. The bearing fault diagnosis model is proposed based on deep learning. The model contributes to the sustainable use of rolling bearings and machinery accident prevention.
W. Barbosa, Felipe C. Gouvea, A. R. F. A. Martins
et al.
Through the expansion of Industry 4.0, the Oil & Gas industry in the world is undergoing a major transformation, so that the formalization of a process chain for the manufacture of spare-parts becomes increasingly necessary. This work aims to create work patterns using the concepts of industry 4.0 applied to the Oil & Gas industry, through the study of several work- pieces of this area. All spare-parts were used to create a chain of manufacturing processes. From there, they were recreated through different digital or hybrid manufacturing techniques. Several points such as geometry, type of acquisition of geometry, types of raw materials, types of manufacturing technology and machinery were addressed. Mechanical tests were carried out at different stages of the process. The results obtained formed a basis for strategies aiming solving problems of the studied spare-parts, using hybrid and additive manufacturing techniques, combined with the concepts of Industry 4.0. The created protocol was a descriptive and detailed standardization of the production chain process. The evaluation of the processes, justifications and solutions was applicable for each demand, generating a virtual catalog of spare-parts and that fed a cyclical model of experiences that continually update the database itself.
Iago Oliveira, Philip Cardiff, Carlos E. Baccin
et al.
Numerical simulations have been extensively used in the past two decades for the study of intracranial aneurysms (IAs), a dangerous disease that occurs in the arteries that reach the brain. They may affect up to 10 % of the world's population, with up to 50 % mortality rate, in case of rupture. Physically, the blood flow inside IAs should be modeled as a fluid-solid interaction problem. However, the large majority of those works have focused on the hemodynamics of the intra-aneurysmal flow, while ignoring the wall tissue's mechanical response entirely, through rigid-wall modeling, or using limited modeling assumptions for the tissue mechanics. One of the explanations is the scarce data on the properties of IAs walls, thus limiting the use of better modeling options. Unfortunately, this situation is still the case, thus our present study investigates the effect of different modeling approaches to simulate the motion of an IA. We used three hyperelastic laws and two different ways of modeling the wall thickness and tissue mechanical properties -- one assumed that both were uniform while the other accounted for the heterogeneity of the wall by using a "hemodynamics-driven" approach in which both thickness and material constants varied spatially with the cardiac-cycle-averaged hemodynamics. Pulsatile numerical simulations, with patient-specific vascular geometries harboring IAs, were carried out using the one-way fluid-solid interaction solution strategy, in which the blood flow is solved and applied as the driving force of the wall motion. We found that different wall morphology models yield smaller absolute differences in the mechanical response than different hyperelastic laws. Furthermore, the stretch levels of IAs walls were more sensitive to the hyperelastic and material constants than the stress. These findings could be used to guide modeling decisions on IA simulations.