Search Results (9)

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Sustainable manufacturing requires modern intelligent approaches to monitoring products of the manufacturing process. An integral part of intelligent manufacturing is the measurement of geometric parameters of products, which allows diagnosing the state of the manufacturing process, optimizing it and predicting its further development. For these reasons, it is necessary to monitor the condition of measuring instruments, as decision-making is based on the data provided by them. Calibration intervals of measuring instruments are commonly defined using fixed time-based rules that are not explicitly linked to measurement uncertainty growth or conformity risk. This practice may lead to either unnecessary recalibration or an increased probability of using out-of-tolerance instruments. In this study, a Monte Carlo-based methodology for determining recalibration intervals is proposed, in which recalibration decisions are derived from the probabilistic evolution of measurement error over time. Measurement uncertainty is modeled as a time-dependent stochastic process combining calibration uncertainty, drift behavior, and repeatability. Monte Carlo simulation is used to propagate uncertainty and to estimate both the expanded uncertainty and the probability that the measurement error exceeds the maximum permissible error (MPE). The recalibration interval is defined as the earliest time at which this probability exceeds a predefined acceptable risk threshold. A numerical experiment using realistic synthetic data representative of a typical dimensional measuring instrument demonstrates that probability-based and uncertainty-based criteria may lead to substantially different recalibration intervals. The results confirm that risk-informed recalibration intervals provide a more transparent and metrologically justified alternative to fixed schedules while remaining fully compatible with ISO/IEC 17025 and GUM principles. The proposed approach is instrument-agnostic and readily applicable in calibration laboratories and industrial measurement systems. Full article

A spherical-cylindrical pressure hull is a new form of pressure-resistant structure that is distinguished from traditional large deep-sea equipment. The residual stresses and deformations introduced by out-of-tolerance welded joints pose a great threat to structural safety under deep-sea service conditions. In this paper, the angular joint of the spherical-cylindrical structure is optimized as a skirted butt joint, and the simulation method is employed to focus on the changes in stress and deformation in the two structural models before and after applying 20 MPa external pressure. The results identify that under hydrostatic pressure, the stress level in the skirt model decreases significantly compared to the residual stress of welding, while the stress in the fillet model increases slightly at the local location. After unloading, the structural stress and deformation return to the post-weld state. The effect of heat treatment on stress relief is very significant and can improve the bearing capacity of the structure. Full article

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. Full article

Current and future heavy-duty gas turbines (GTs) are being developed as an alternative or support to renewable energy sources (RESs). Therefore, GTs are subjected to several instances of being switched on and off; thus, the material fatigue limit can be reached in a short time. In such a scenario, possible out-of-tolerances (OoTs) in critical components must be considered. In this paper, OoTs related to critical parameters in the attachment geometry of a rotor blade are considered to estimate their impact on component life through a 2D finite element (FE) analysis. First, a mesh refinement is performed to obtain mesh-independent results; second, OoT geometries are simulated to determine stresses and strains at the blade attachment and disc groove. The mesh refinement process is critical to ensure model accuracy for both nominal and OoT geometries. The results show that OoTs can lead to important reductions in the life of the intended component, both on the blade and disc sides. These results could be useful in updating the maintenance plan for components and could be used for future insights, with further work extending the study to 3D geometry, for example, and evaluating the effect of other geometries. Full article

In the machining process for herringbone gears manufactured by numerical control gear-shaping machines, out-of-tolerance problems of symmetry error generally exist. This paper proposed a high-precision control of spatial symmetry error in the one-time forming machining for herringbone gear. To improve the machining symmetry accuracy and quality of herringbone gear, a mathematical model of measurement, evaluation and compensation for spatial symmetry error was established based on the least square method. Meanwhile, a new shaping machining method based on spatial symmetry error detection and compensation was proposed. The test results indicated that the proposed method can maintain symmetry within 0.02 mm. This study provided a novel spatial symmetry error detection and compensation machining method for herringbone gear that has advantages compared to traditional methods in terms of machining accuracy, efficiency, and continuous machining type. Full article

Many complex electromechanical assemblies that are essential to the vital function of certain products can be time-consuming to inspect to a sufficient level of certainty. Examples include subsystems of machine tools, robots, aircraft, and automobiles. Out-of-tolerance conditions can occur due to either random common-cause variability or undetected nonstandard deviations, such as those posed by debris from foreign objects. New methods need to be implemented to enable the utilization of detection technologies in ways that can significantly reduce inspection efforts. Some of the most informative three-dimensional image recognition methods may not be sufficiently reliable or versatile enough for a wide diversity of assemblies. It can also be an extensive process to train the recognition on all possible anomalies comprehensively enough for inspection certainty. This paper introduces a methodical technique to implement a semiautonomous inspection system and its algorithm, introduced in a prior publication, that can learn manufacturing inspection inference from image recognition capabilities. This fundamental capability accepts data inputs that can be obtained during the image recognition training process followed by machine learning of the likely results. The resulting intelligent insights can inform an inspector of the likelihood that an assembly scanned by image recognition technology will meet the manufacturing specifications. An experimental design is introduced to generate data that can train and test models with a realistic representation of manufacturing cases. A benchmark case study example is presented to enable comparison to models from manufacturing cases. The fundamental method is demonstrated using a realistic assembly manufacturing example. Recommendations are given to guide efforts to deploy this entire methodical technique comprehensively. Full article

Assembly precision optimization is an important means to ensure product accuracy, including two aspects: on the one hand, the relevant deviations of out-of-tolerance key characteristics are reduced to the design tolerance range; on the other hand, the deviation fluctuation range of key characteristics with a large process capability index (Cp) can be extended to achieve the balance between accuracy, process capacity, and production cost. By virtue of the accumulated experience, a fast solution can be provided for the out-of-tolerance problem. Therefore, a semantic-based assembly precision optimization method considering process capacity is proposed in this paper. By constructing an ontology model between Cp and optimization strategy, a reasonable assembly precision optimization strategy can be pushed based on product accuracy analysis results. Firstly, an assembly precision optimization semantic model is established by association between analysis results, out-of-tolerance key characteristics, assembly process, and tolerance adjustment defined with Web Ontology Language (OWL) assertions. Furtherly, according to different Cp corresponding to different assembly success rates, Semantics Web Rule Language (SWRL) rules based on Cp are constructed to the push optimization strategy. Finally, the effectiveness of the model is illustrated by an aircraft inner flap. Full article

High cost, unpredictable defects and out-of-tolerance rejections in final parts are preventing the complete deployment of Laser-based Powder Bed Fusion (LPBF) on an industrial scale. Repeatability, speed and right-first-time manufacturing require synergistic design approaches. In addition, post-build finishing operations of LPBF parts are the object of increasing attention to avoid the risk of bottlenecks in the machining step. An aluminum component for automotive application was redesigned through topology optimization and Design for Additive Manufacturing. Simulation of the build process allowed to choose the orientation and the support location for potential lowest deformation and residual stresses. Design for Finishing was adopted in order to facilitate the machining operations after additive construction. The optical dimensional check proved a good correspondence with the tolerances predicted by process simulation and confirmed part acceptability. A cost and time comparison versus CNC alone attested to the convenience of LPBF unless single parts had to be produced. Full article