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This paper aims to promote the integration of the relative position accuracy (RPA) measurement and evaluation in digital assembly process by adopting the model-based method. An integrated framework for RPA measurement is proposed based on a model-based definition (MBD) data set. The study also aims to promote the efficiency of inspection planning of RPA measurement by improving the reusability and configurability of the inspection planning.

The works have been carried out on three layers. In the data layer, an extended MBD data set is constructed to describe the objects and data for defining RPA measurement items; In definition layer, a model based and hierarchical structure for RPA item definition is constructed to support quick definition for RPA measurement items. In function layer, a toolset consisting three modules is constructed in a sequence from measurement planning to RPA value solving to visualized displaying again. Based on this framework, a prototype system is developed.

The paper provides an identified practice of model-based inspection. It suggests that MBD is valuable in promoting both the integration and efficiency of digital inspection.

The templates and constructed geometry objects given in this paper are still limited in a scenario of aircraft assembly. The integrity and universality of them still need follow-up works.

The paper includes implications for the model based digital inspection, the digital assembly and the extended application of MBD.

This paper expands the application of MBD in inspection and fulfils the need to promote the integration and efficiency of digital inspection in large-scale component assembly.

The purpose of this paper is to propose an approach, which is easy to implement, for estimating the thickness of the air layer that may separate metallic parts in some aeronautical assemblies, by using the eddy current method.

Based on an experimental study of the coupling of a magnetic cup core coil sensor with a metallic layered structure (consisting of first metal layer/air layer/second metal layer), which is confirmed by finite element modelling simulations, an inversion technique relying on a polynomial forward model of the coupling is proposed to estimate the air layer thickness. The least squares and the nonnegative least squares algorithms are applied and analysed to obtain the estimation results.

The choice of an appropriate inversion technique to optimize the estimation results is dependent on the signal-to-noise ratio of measured data. The obtained estimation error is smaller than a few percent, for both simulated and experimental data. The proposed approach can be used to estimate both the air layer thickness and the second metal layer thickness simultaneously/separately.

This model-based approach is easy to implement and available to all types of eddy current sensors.

This paper aims to present the verification process conducted to assess the functional correctness of the voting system. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) is the most important research institution in Argentina. It depends directly from Argentina’s President but its internal authorities are elected by around 8,000 research across the country. During 2011, the CONICET developed a Web voting system to replace the traditional mail-based process. In 2012 and 2014, CONICET conducted two Web election with no complaints from candidates and voters. Before moving the system into production, CONICET asked the authors to conduct a functional and security assessment of it.

This process is the result of integrating formal, semi-formal and informal verification activities from formal proof to code inspection and model-based testing.

Given the resources and time available, a reasonable level of confidence on the correctness of the application could be transmitted to senior management.

A formal specification of the requirements must be developed.

Formal methods and semi-formal activities are seldom applied to Web applications.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning.

This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result.

The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods.

This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production.

The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

The purpose of this paper is to propose a new inspecting algorithm for defect detection on PCB circuits.

PCB circuit images were processed by a radon transformation. A Radon histogram was formed and utilized to establish a texture directional characteristic similarity function. Then, a region of the image which contained the same texture directionality feature was segmented. Furthermore, a directionality estimation method is presented. As the circuit was damaged, the directionality was weakened correspondingly. According to principle, the concept of directional intensity was proposed and then used to measure directionality through analysis of the Radon histogram fluctuation. Finally, the defect was detected based on directional intensity.

The method has been applied to an inspecting system used in practice and it achieved a higher accuracy and efficiency in comparison with similar methods.

Although work on highly intensive PCB circuitry inspection and flaw detection is presented, defect classification was not involved although this is also a very important requirement of inspection.

The paper provides a new way to detect PCB circuitry defects based on texture directionality and proposes evaluating the similarity between image texture directionalities using a radon transformation to search the inspected area. As the inspected region was located, the concept of directional intensity was defined to measure texture directionality to identify defects. The new algorithm performs stably and efficiently and is fit for practical application.

Recently great interest has developed for a high speed, flexible machine vision system which can accurately determine solder paste and component placement for both process verification and quality control inspection. Present SMT inspection systems must cope with the unpredictable appearance of components and backgrounds and are often used only to determine presence and absence. This paper describes a new approach which combines greyscale data with a three‐dimensional map of the board under test. Originally this method was proposed as a robust technique for locating components in low contrast greyscale images. However, experience working with manufacturers and developers of placement equipment has shown that emerging SMT inspection requirements indicate the importance of three‐dimensional information. In addition to the detection of components and measurement of orientation, examples are shown of solder paste volume measurements, lead co‐planarity, and tombstone effect detection.

With the emergence of digital twins, the construction industry is looking toward improving the inspection and maintenance of all kinds of assets, such as bridges, roads and utilities. The purpose of this paper is to provide insights into how the development of an interactive digital twin creates a variety of interactions between users of this technology and assets to be monitored.

The development of a digital twin inspection model, focusing on the specific case of a sewage pumping station, is chosen as the subject of a case study. Through the development of this model, this study explores the various user–technology interactions that can be designed in a digital twin context.

Users interact with digital twins by following virtual instructions in a certain way, which creates a “quasi-other” relationship. A digital twin based on virtual reality (VR) also make users feel as if they are within the created VR of an inspection site, thereby immersing them in the VR environment. The design of a VR-based digital twin, which is determined by decisions made during the development process, shapes the context in which users interact with the technology and assets.

This study shows that a digital twin in construction practice may play different “actant” roles having different types of influences. Analyzing these actant roles and influences in terms of force and visibility adds a new perspective on the interaction between users and digital twins in construction and asset monitoring practice.

The purpose of this paper is to introduce analyst estimates and option pricing-based variables in modeling material accounting misstatements.

The paper uses a logistic regression model to analyze a comprehensive sample of AAER and non-AAER firms listed in the USA.

By applying a cross-sectional, sequence of time-series logistic regression models, the authors find better identifiers of ex ante risk of fraud than prediction models based on an inspection of abnormal accruals. These identifiers include the managed earnings (ME) component of a firm and the change in a firm’s option contracts’ implied volatility (IV) prior to an earnings announcement.

The empirical findings contribute to an understanding of earnings manipulation (fraud) and should be of value to auditors and regulatory bodies interested in identifying financial statement fraud, particularly the Securities and Exchange Commission, which has been improving its accounting quality model (AQM or Robocop) fraud detection tool for many years. The results contribute substantially to enhancing the current accounting literature by introducing two non-accrual-based measures that significantly enhance the predictive power of an accrual-based accounting misstatement prediction model.

This paper radically departs from relying on the assumption that the clearest and easiest pathway to detect fraud reporting ex ante is through an examination of accruals. Instead, the authors use a richer source of information about the possibility of a firm’s misstatement of its financial accounting numbers, namely, analyst estimates of ex post earnings and the IV from exchange-traded option contracts.