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The aim of this study is to make a contribution towards reducing the deflections of silicon wafers. The deformation of silicon wafers used in the manufacture of electronic micro-components is one of the most common problems encountered by industrialists during manufacturing. Stack warping is typically produced during the process of depositing thin layers on a substrate. This is due to the thermal-mechanical stresses caused by the difference between the thermal expansion coefficients of the materials. Reducing wafer deformation is essential to increase reliability and improve quality. In this paper, the authors propose an approach based on minimal geometrical modifications to reduce the deformation of a silicon wafer coated with two thin layers. Numerical finite element models have been developed to evaluate the impact of geometrical modifications on warping amplitude. Finite element models have been validated compared with experimental models. The results obtained are encouraging and clearly show a considerable reduction in wafer deformation.

Reducing wafer deformation is essential to increase reliability and improve quality. In this paper, the authors propose an approach based on minimal geometrical modifications to reduce the deformation of a silicon wafer coated with two thin layers. Numerical finite element models have been developed to evaluate the impact of geometrical modifications on warping amplitude. Finite element models have been validated compared with experimental models.

The results obtained are encouraging and clearly show a considerable reduction in wafer deformation.

This paper describes the influence of geometric modification on wafer deformation. The work show also the cruciality of stress reduction in the purpose to obtain less wafer deformation.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

The rock drill’s drill tail experiences high-frequency fretting simultaneously in the rotational and axial directions. Due to the complex working characteristics and the low viscosity of the water medium, the pure water seal is susceptible to damage and failure. The purpose of this paper is to enhance the water seal’s performance.

The Y-shaped seal ring is modeled and simulated using orthogonal testing. Through analysis of the impact of various seal section parameters on sealing performance, the maximum contact stress and maximum Von Mises stress are selected as indicators of sealing effectiveness.

The maximum contact stress is proportional to lip thickness and chamfer length but inversely proportional to lip length. Meanwhile, the maximum Von Mises stress is directly influenced by lip depth and the included angle of the lip and drill tail but is inversely proportional to the lip thickness. The enhanced Y-shaped water seal sees reductions of 15% and 45% in maximum contact stress and maximum Von Mises stress, respectively.

This paper used analytical method and model that is helpful for design of the water seal’s structure in complex working characteristics and the low viscosity of the water medium.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0366/

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

This study aims to investigate the rarefaction effects on flow and thermal performances of an equivalent sand-grain roughness model for aerodynamic thrust bearing.

In this study, a model of gas lubrication thrust bearing was established by modifying the wall roughness and considering rarefaction effect. The flow and lubrication characteristics of gas film were discussed based on the equivalent sand roughness model and rarefaction effect.

The boundary slip and the surface roughness effect lead to a decrease in gas film pressure and temperature, with a maximum decrease of 39.2% and 8.4%, respectively. The vortex effect present in the gas film is closely linked to the gas film’s pressure. Slip flow decreases the vortex effect, and an increase in roughness results in the development of slip flow. The increase of roughness leads to a decrease for the static and thermal characteristics.

This work uses the rarefaction effect and the equivalent sand roughness model to investigate the lubrication characteristics of gas thrust bearing. The results help to guide the selection of the surface roughness of rotor and bearing, so as to fully control the rarefaction effect and make use of it.

The purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry.

The procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine.

The geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size.

Although large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence.

The presented method of compensation is applicable to layers of any shape obtained in any AM process.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

This paper aims to present the approximate limit pressure solutions for thin-walled shape-imperfect 90° pipe bends. Limit pressure was determined by finite element (FE) limit analysis with the consideration of small geometry change effects.

The limit pressure of 90° pipe bends with ovality and thinning has been evaluated by geometric linear FE approach. Internal pressure was applied to the inner surface of the FE pipe bend models. When von-Mises stress equals or just exceeds the yield strength of the material, the corresponding pressure was considered as the limit pressure for all models. The current FE methodology was evaluated by the theoretical solution which has been published in the literature.

Ovality and thinning produced a significant effect on thin-walled pipe bends. The ovality weakened pipe bend performance at any constant thinning, while thinning improved the performance of the bend portion at any constant ovality. The limit pressure of pipe bends under internal pressure increased with an increase in the bend ratio and decreased with an increase in the pipe ratio. With a simultaneous increment in bend radius and reduction in wall thickness, there was a reduction in limit pressure. A new closed-form empirical solution was proposed to evaluate limit pressure, which was validated with published experimental data.

The influences of structural deformation (ovality and thinning) in the limit pressure analysis of 90° pipe bends have not been investigated and reported.