Incremental Analysis Research Articles

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

#2628 A haemodialysis patient-centred model of care with a multidisciplinary programme: what is its clinical and health efficiency impact?

Abstract Background and Aims Chronic kidney disease (CKD) constitutes a public health concern due to its notable morbidity & mortality and associated costs. The Patient Care Coordination program (PCC) provides comprehensive and personalized care to haemodialysis (HD) patients. This study evaluated the cost-effectiveness of PCC compared to standard of care (SoC). Method The study included patients in 9 HD clinics (5 PCC, 4 SoC) in Catalonia, Spain, in 2020-2021. A comparative effectiveness evaluation was conducted to compare change in Individual Patient Performance Score (IPPS, score 0-100, evaluates parameters such as vascular access, adequacy of HD, anaemia, mineral bone disease, arterial hypertension, nutrition/hydration status) and number of hospitalisations of PCC patients vs SoC during the first year in the program. Instrumental variables (IV) regression analysis was performed to address potential confounding; number of interactions with program specialists was a valid IV to estimate degree of PCC use. An incremental cost-effectiveness analysis (iCEA) through microsimulation was performed using the effectiveness data from the previous step, population data from the renal patient registry of Catalonia, and the cost of hospitalisations obtained from the Public Health System. Results 127 patients in PCC and 363 patients in SoC were included. The degree of PCC use had a positive impact on IPPS variation during the first year of the program (p = 0.012). In the iCEA the PCC program resulted in a mean gain of 3.4 additional IPPS points, a reduction of 0.2 hospitalisations per patient, and reduced mean costs of €1,265 per patient in the first year compared to SoC, resulting to be a dominant alternative (Fig. 1). Conclusion The holistic and interdisciplinary approach to the patient has further highlighted the relevance of programme use and not only belonging to the programme in achieving clinical goals. The PCC program represents thus a dominant alternative, with greater effectiveness and total cost savings that could sum up to €5.5 million per year in the region due to reduced hospitalizations. This approach may be an alternative to address the challenges of CKD and optimise the allocation of healthcare resources.

Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)

Abstract. In a cycling system where data assimilation (DA) and model simulation are executed consecutively, the model forecasts initialized from the analysis (or data assimilation) can be systematically affected by dynamic imbalances generated during the analysis process. The high-frequency noise arising from the imbalances in the initial conditions can impose constraints on computational stability and efficiency during subsequent model simulations and can potentially become the low-frequency waves of physical significance. To mitigate these initial imbalances, the incremental analysis update (IAU) has long been utilized in the cycling context. This study introduces our recent implementation of the IAU in the Model for Prediction Across Scales – Atmospheric (MPAS-A) coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system called MPAS-Workflow. During the integration of the compressible nonhydrostatic equations in MPAS-A, analysis increments are distributed over a predefined time window (e.g., 6 h) as fractional forcing at each time step. In a real case study with the assimilation of all conventional and satellite radiance observations every 6 h for 1 month, starting from mid-April 2018, model forecasts with the IAU show that the initial noise illustrated by surface pressure tendency becomes well constrained throughout the forecast lead times, enhancing the system reliability. The month-long cycling with the assimilation of real observations demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system. Along with the comparison between the forecasts with and without the IAU, several aspects regarding the implementation in MPAS–JEDI are discussed. Corresponding updates have been incorporated into the MPAS-A model (originally based on version 7.1), which is now publicly available in MPAS–JEDI and MPAS-Workflow version 2.0.0.

Diagnostics for Imbalance on the Convective Scale

Abstract The analyses produced by a data assimilation system may be unbalanced, that is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flowon this scale is strongly divergent and non-hydrostatic. In this studywe compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1. Latent Heat Nudging (LHN), 2. Local Ensemble Transform Kalman Filter (LETKF), and 3. LETKF in combination with incremental analysis updates (IAU). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and theWTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure, but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance andWTG departures are similar, and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.

Computational study of the influence of microstructure on fracture behavior near crack-tip field in nacre-like materials

The small-scale failure process around a plane strain mode I crack in nacre-like materials is analyzed. The displacement increments of boundary nodes are specified by the analytical solutions based on the orthotropic linear elasticity to conduct an incremental finite element analysis. We set up a structural model composed of staggered arranged brittle aragonite tablets and cohesive zones within a process window surrounding the crack tip to investigate the toughness as well as the deformation mechanism in the fracture process zone. The results obtained by the parametric studies of different geometries and mechanical properties in the microstructure of the materials show that the aspect of failure in the near-tip fields can be categorized into four distinct fracture mechanisms. These mechanisms significantly affect the fracture toughness with differences of hundreds of times. We show a failure-mechanism map in which these fracture mechanisms can be divided into regions of geometry and mechanical properties in microstructure. Findings obtained in this study about the toughening mechanisms give us the useful knowledge to design novel materials with specific microstructures to control fracture mechanisms.

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Development and Psychometric Evaluation of a Novel Measure of Nicotine E-cigarette Withdrawal for Use With Adolescents and Young Adults.

Nicotine withdrawal is a well-established construct that prompts continued nicotine product use and contributes to failed cessation efforts. Despite ongoing public health concerns about nicotine e-cigarette use in adolescents and young adults (AYAs), no psychometrically sound measure of nicotine e-cigarette withdrawal exists for this population. A mixed methods approach comprising a literature review to identify existing nicotine withdrawal items; subject matter expert feedback on existing items and novel item generation; cognitive interviews assessing the measure's instructions, items, and response options; and a large quantitative validation survey (N = 997) was employed to develop the novel retrospective measure of nicotine e-cigarette withdrawal. A 25-item solution comprising four subscales-negative affect, negative physical symptoms, craving, and appetite/food-and an overall withdrawal score was supported. Internal reliability was excellent (mean alpha = 0.91), and scalar measurement invariance was reached for all subgroups tested (eg, sex, age, exclusive e-cigarette use vs. dual tobacco product use, and daily vs. non-daily nicotine vaping). Overall withdrawal and its subscales evidenced concurrent validity with time to first vape in the morning, e-cigarette dependence, and previous vaping cessation attempts, although not each subscale was associated with each outcome. Importantly, cross-sectional incremental validity analyses indicated that retrospective withdrawal accounted for variance in each outcome above and beyond e-cigarette dependence. The novel retrospective AYA Nicotine E-cigarette Withdrawal Scale (AYA NEWS) evidenced strong psychometric properties for use in this population. Future research can determine whether the AYA NEWS can be used to assess acute e-cigarette nicotine withdrawal. While e-cigarette use remains the most prevalent form of nicotine product use among AYAs, there are limited options for psychometrically sound measures of e-cigarette-related constructs in this population. Withdrawal from nicotine is associated with failed cessation attempts and continued use of nicotine-containing products, making it a critical construct within tobacco-related research. This study outlines the development and preliminary psychometric evaluation of a novel, retrospective measure of nicotine e-cigarette withdrawal. Study findings support using the Adolescent and Young Adult Nicotine E-cigarette Withdrawal Scale (NEWS) to assess retrospective withdrawal from nicotine e-cigarettes in young people.

Assessing the potential impact of assimilating total surface current velocities in the Met Office’s global ocean forecasting system

Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office’s global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-square-errors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system.

Efficient word segmentation is preserved in older adult readers: Evidence from eye movements during Chinese reading.

College-aged readers use efficient strategies to segment and recognize words in naturally unspaced Chinese text. Whether this capability changes across the adult lifespan is unknown, although segmenting words in unspaced text may be challenging for older readers due to visual and cognitive declines in older age, including poorer parafoveal processing of upcoming characters. Accordingly, we conducted two eye movement experiments to test for age differences in word segmentation, each with 48 young (18-30 years) and 36 older (65+ years) native Chinese readers. Following Zhou and Li (2021), we focused on the processing of "incremental" three-character words, like (meaning "kindergartens"), which contain an embedded two-character word (e.g., , meaning "children"). In Experiment 1, either the three-character word or its embedded word was presented as the target word in sentence contexts where the three-character word always was plausible, and the embedded word was either plausible or implausible. Both age groups produced similar plausibility effects, suggesting age constancy in accessing the embedded word early during ambiguity processing before ultimately assigning an incremental word analysis. Experiment 2 provided further evidence that both younger and older readers access the embedded word early during ambiguity processing, but rapidly select the appropriate (incremental) word. Crucially, the findings suggest that word segmentation strategies do not differ with age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychometric Properties of the Metacognitions About Online Gaming Scale in the Chinese Population and Its Relationship With Internet Gaming Disorder: Cross-Sectional Study.

Metacognitions about online gaming have been shown to be correlated with Internet Gaming Disorder (IGD). Knowledge of metacognitions about online gaming can help to understand IGD. The Metacognitions about Online Gaming Scale (MOGS) is a reliable and valid tool to measure specific metacognitions about online gaming in both adults and adolescents, which is lacking in China. This study was conducted to assess the psychometric properties of the Chinese version of the MOGS (C-MOGS) and its relationship with IGD in the Chinese population. A total of 772 Chinese individuals (age: mean 21.70, SD 8.81 years; age range: 13-57 years; 458/772, 59.3% male) completed a web-based questionnaire survey, including the C-MOGS and a battery of validated scales measuring IGD, gaming motives, depression, and anxiety. Through exploratory and confirmatory factor analyses, the 3-factor structure was confirmed to have adequate model fit and internal consistency reliability (Cronbach α≥.799, Guttman split-half coefficients≥0.754). Concurrent validity of the C-MOGS was supported by its correlations with IGD (P<.001), gaming motives (P<.001), depression (P<.001), and anxiety (P<.001). Furthermore, the incremental validity analysis showed that the C-MOGS predicted 13% of the variance in IGD while controlling for gender, age, weekly gaming hours, gaming motives, depression, and anxiety. This study provides evidence that the psychometric properties of the C-MOGS are appropriate and emphasizes its positive association with IGD. The C-MOGS is a reliable and valid instrument for mental health workers to assess metacognitions about online gaming in the Chinese population.

Online two-dimensional filter for anti-interference aging features extraction to accurately predict the battery health

Reliable and precise extraction of features plays a critical role in predicting the State of Health (SOH) of lithium batteries. Nevertheless, noise-tainted measurement signal can cause the distortion of differential curves, consequently compromising the accuracy of feature extraction. This paper develops an online two-dimensional filtering framework by fusing the Adaptive Taylor Kalman filter (ATKF) and the Improved Gaussian filter (IGF), applying it to both incremental capacity analysis and differential thermal voltammetry methods. Firstly, the Taylor series is harnessed to construct a state model for the noise-affected measurement signal. Then, an adaptive algorithm is introduced to diminish the dependence on human judgment when configuring variances. Finally, the IGF filters the curve from the two-dimensions of time and period. Experimental results demonstrate that the two-dimensional filtering method exhibits robust noise mitigation capabilities, enhancing the stability of feature extraction while maintaining system causality. Furthermore, this method has strong robustness to battery inconsistency and temperature uncertainty. When applying the proposed filter to extract aging features from battery data provided by CALCE and Oxford datasets, the correlation coefficient between the extracted aging features and SOH increases by 34.75% and 10.71%, respectively. Additionally, the RMSE of the SOH prediction results decreases by 60.57% and 59.46%, respectively.

Aging in First and Second Life of G/LFP 18650 Cells: Diagnosis and Evolution of the State of Health of the Cell and the Negative Electrode under Cycling

Second-life applications for lithium-ion batteries offer the industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. An accurate prognosis of the remaining useful life (RUL) is essential for ensuring effective second-life operation. Diagnosis is a necessary step for the establishment of a reliable prognosis, based on the aging modes involved in a cell. This paper introduces a method for characterizing specific aging phenomenon in Graphite/Lithium Iron Phosphate (G/LFP) cells. This method aims to identify aging related to the loss of active material at the negative electrode (LAMNE). The identification and tracking of the state of health (SoH) are based on Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) peak-tracking techniques. The remaining capacity of the electrode is thus evaluated based on these diagnostic results, using a model derived from half-cell electrode characterization. The method is used on a G/LFP cell in the format 18650, with a nominal capacity of 1.1 Ah, aged from its pristine state to 40% of state of health.

Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

PurposePrefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.Design/methodology/approachA model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.FindingsThe IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.Originality/valueThe sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

ФОРМИРОВАНИЕ ИНФОРМАЦИИ О ПОВЕДЕНИИ ЗАТРАТ И ИСПОЛЬЗОВАНИЕ ПОКАЗАТЕЛЯ МАРЖИНАЛЬНОЙ ПРИБЫЛИ ДЛЯ ОЦЕНКИ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ

The article considers the problem of accounting and analytical provision of operational managerial decisions made in the conditions of constantly changing market. It justifies a necessity of assessment of consequences of managerial decisions through their influence on behavior of expenditures and financial results of activity of economic entity. A critical analysis of the solution of the task of cost separation and establishment of their functional dependence on the volume of activities is resulted. The factors that influence the structure of expenditures have been analyzed. The article considers possibilities of using CVP-analysis for calculation of consequences of decisions taken by management, as well as incremental analysis of profit. The recommendations on the organization of analytical accounting of expenditures with a view to assessing their behavior and relationship with the dynamics of business activity are given. The possibilities of using the margin profit indicator to assess the economic feasibility of the decisions are given. The shortcomings of the use of the considered management accounting tools were highlighted.

A step-by-step guide to ageing octopus

Global octopus catch has doubled over the past four decades and is likely to grow in importance as many key fisheries continue to decline. Lack of age data is a critical limitation in assessing the resource status of octopus. Over the past ~30 years, studies have investigated various methods to age octopus, with some methods better suited to certain species than others. However, only a small number of researchers have the hands-on knowledge to execute these methods in the laboratory. Here, we present the first step-by-step guide to ageing octopus, as well as a decision tool, which should enable readers to carry out the ageing process and make an informed decision on the most suitable method for their species. We provide guidance on age validation, increment analysis of both beaks and stylets, materials needed, as well as avenues for further research. We hope this guide will provide a starting point for researchers new to octopus ageing, and for those working with octopus species that have never been aged before. We also encourage researchers to use this guide as a forum for open discussion to support the ongoing development of effective octopus ageing methods.

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

A Lithium-Ion Battery Remaining Useful Life Prediction Method with A New Algorithm Based on Incremental Capacity Analysis

Safe use of lithium-ion batteries requires accurately assessing state of charge (SoC), state of health (SOH) and capacity estimation techniques. Due to numerous charge and discharge cycles, lithium ion batteries undergo a degradation process during their use leading to failures, accidents, and fire. Traditional ICA/DVA methods have been used to overcome these issues, but they are subject to changes in battery resistance and polarization processes during battery aging. Evaluation of the SoC as a function of incremental capacity is proposed in this work to overcome this problem. This article used a new algorithm to perform, through simulations carried out with Matlab® software, incremental capacity analysis for a preventive estimate of remaining useful life (RUL). In addition, the comparison between IC curves and the SoC here used fully represents the relationship between the IC values and the internal parameters of the battery. The validity of the proposed algorithm against the phenomenon of battery aging was evaluated based on experimental data from NASA's PCoE research center.

Towards Long-Term Monitoring of Commercial Lithium-Ion Batteries Enabled by Externally Affixed Fiber Sensors and Strain-Based Prognostic Strategies

Real-time monitoring of both continuous and spontaneous degradation in lithium-ion batteries is challenging due to the limited number of quantitative metrics available during cycling. In this regard, improved sensing approaches enabled by sensors of high accuracy, precision, and durability are key to achieving comprehensive state estimation and meeting rigorous safety standards. In this work, external temperature and strain monitoring in commercial Li-ion button cells was carried out using tandem pairs of polymer-based and silica-based optical fiber Bragg grating sensors. The decoupled data revealed that the sensors can reliably track strain and temperature evolution for over 500 cycles, as evidenced by periodic patterns with no sign of sensor degradation or loss of fidelity. Moreover, monitoring the strain signal enabled early detection of an anomalous cell over ∼60 cycles ahead of an electrochemical signature and abrupt drop in capacity, suggesting that mechanical sensing data may offer unique benefits in some cases. Detailed mechanical monitoring via incremental strain analysis suggests a parallel path toward understanding cell degradation mechanisms, regardless of whether they are continuous or discrete in nature. The accuracy and durability of such a package-level optical fiber sensing platform offers a promising pathway for developing robust real-time battery health monitoring techniques and prognostic strategies.

Interactive Abstract Interpretation with Demanded Summarization

We consider the problem of making expressive, interactive static analyzers compositional . Such a technique could help bring the power of server-based static analyses to integrated development environments (IDEs), updating their results live as the code is modified. Compositionality is key for this scenario, as it enables reuse of already-computed analysis results for unmodified code. Previous techniques for interactive static analysis either lack compositionality, cannot express arbitrary abstract domains, or are not from-scratch consistent. We present demanded summarization, the first algorithm for incremental compositional analysis in arbitrary abstract domains that guarantees from-scratch consistency. Our approach analyzes individual procedures using a recent technique for demanded analysis, computing summaries on demand for procedure calls. A dynamically updated summary dependency graph enables precise result invalidation after program edits, and the algorithm is carefully designed to guarantee from-scratch-consistent results after edits, even in the presence of recursion and in arbitrary abstract domains. We formalize our technique and prove soundness, termination, and from-scratch consistency. An experimental evaluation of a prototype implementation on synthetic and real-world program edits provides evidence for the feasibility of this theoretical framework, showing potential for major performance benefits over non-demanded compositional analyses.

Crystal plasticity enhanced direct cyclic analysis of cyclic behaviour of LPBF-manufactured AISI 316L

The fatigue performance of Laser Powder Bed Fusion manufactured 316L stainless steel is critical for cyclic loading applications. Additive manufacturing of 316L produces a characteristic microstructure consisting of grains with a fine cellular structure within. This microstructure shows a comparatively high fatigue strength combined with a high defect tolerance. The purpose of this paper is to numerically analyse the local response of this microstructure to cyclic loading and, in particular, the joint action between lack of fusion defects and the surrounding grain structure. The traditional incremental analysis method for polycrystalline alloys is computationally expensive. Therefore, a direct cyclic analysis combined with a phenomenological crystal plasticity model is used in this work to predict the stable cyclic response of the material, resulting in a much lower computational cost. Three statistically equivalent representative volume elements are generated. The considered crystal plasticity parameters are calibrated using experimental tensile stress–strain curves. The comparison with incremental analysis demonstrates the accuracy and efficiency of the proposed method. The shakedown limit, based on the maximum load that allows the convergent cyclic response, significantly depends on the local grain orientation around lack of fusion defects, revealing a reason for the large scatter in the fatigue strength.