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The purpose of this paper is to present the results of acoustic emission (AE) and ultrasonic inspection of two H₂S storage tanks carried out in a heavy water plant, in order to characterize point type defects observed during earlier ultrasonic inspection and to ensure that these defects are not growing during hydrotesting of the tanks.

Using multiple AE sensors and AE source location methodology, the entire tank could be covered to detect and locate any dynamic sources of AE associated with local plastic deformation and/or growing discontinuities from any part of the tank during the hydrotest. For confirmation of the results obtained by AE, ultrasonic inspection on the tanks and on virgin plates from which the tanks were manufactured, were carried out.

The AE signals generated during first pressurisation are attributed to the micro yielding of the material of the tanks. A few scattered AE events were observed at a few locations during the hydrotesting of the tanks and these are due to structural and rubbing noise. During hold periods and repressurising cycle of the hydrotesting, no detectable AE events were observed and this confirmed the absence of any growing discontinuity in the tanks during the hydrotesting. Ultrasonic inspection on the tanks and on virgin plates confirmed that the point type defects detected are manufacturing defects and not formed during service life.

The combined results from AE and ultrasonic techniques confirmed the structural integrity of the tanks and ensured their healthiness for continued operation.

The paper brings out the use of AE and ultrasonic techniques for monitoring hydrotesting of storage tanks of a heavy water plant. The storage tanks where point type defect indications were reported during previous ultrasonic inspection and whether these defects are growing during hydrotesting of the tanks or not, were required to be known before the tanks are put in to further service. AE signals collected during pressurising and repressurising cycles of the hydrotest and subsequent inspection by ultrasonic confirmed the vessels to be free from growing defects during the hydrotest and provided baseline data for future inspection.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

In recent years, additive manufacturing (AM) technology has been acknowledged as an efficient method for producing geometrical complex objects with a wide range of applications. However, dimensional inaccuracies and presence of defects hinder the broad adaption of AM procedures. These factors arouse concerns regarding the quality of the products produced with AM and the utilization of quality control (QC) techniques constitutes a must to further support this emerging technology. This paper aims to assist researchers to obtain a clear sight of what are the trends and what has been inspected so far concerning non-destructive testing (NDT) QC methods in AM.

In this paper, a survey on research advances on non-destructive QC procedures used in AM technology has been conducted. The paper is organized as follows: Section 2 discusses the existing NDT methods applied for the examination of the feedstock material, i.e. incoming quality control (IQC). Section 3 outlines the inspection methods for in situ QC, while Section 4 presents the methods of NDT applied after the manufacturing process i.e. outgoing QC methods. In Section 5, statistical QC methods used in AM technologies are documented. Future trends and challenges are included in Section 6 and conclusions are drawn in Section 7.

The primary scope of the study is to present the available and reliable NDT methods applied in every AM technology and all stages of the process. Most of the developed techniques so far are concentrated mainly in the inspection of the manufactured part during and post the AM process, compared to prior to the procedure. Moreover, material extrusion, direct energy deposition and powder bed processes are the focal points of the research in NDT methods applied in AM.

This literature review paper is the first to collect the latest and the most compatible techniques to evaluate the quality of parts produced by the main AM processes prior, during and after the manufacturing procedure.

The current maintenance management method has affected the efficiency of the building facility management at Polytechnics. Many issues such as poor service delivery, inadequate finance, poor maintenance planning and maintenance backlogs have emerged due to the usage of conventional method application (paper-based form and unsystematic database. The purpose of this paper is to review existing literature and case studies project of the technologies in maintenance management at Polytechnic, and subsequently to identify the challenge in improving the current maintenance management technologies.

The purpose of this paper is to review existing literature and case studies project of the technologies in maintenance management at Polytechnic, and subsequently to identify the challenges of information and communication technology (ICT) implementation for strategic defect diagnosis and decision-making in improving the current maintenance management technologies. Eight Polytechnics are selected on the basis of the major problems of using the conventional method in the comparison to investigate the maintenance management practices in each Polytechnic. There are around 32 Polytechnics in Malaysia and most are using conventional methods.

The findings reveal the need for a more sophisticated maintenance management system that provides guidelines for decision-making processes with the implementation of ICT. The interview results also reveal irregularities within the Malaysian Polytechnics’ maintenance management database. The system architecture and the information system prototype are presented to integrate the information database and maintenance management processes in improving the building diagnosis approach and decision-making process for managing building maintenance.

This new system is expected to become the successful technology in assisting the maintenance contractors, clients and developer for effective management of maintenance defects at Polytechnic.

This research proposes the study of the pathology in floor finishes, focussing on proactive maintenance strategies to promote the durability of these elements. A model to assess the floorings' performance levels was designed to support decision-making regarding maintenance activities to be carried out, in order to prevent the defects and extend the floors' service life.

The model to measure the floorings' level of performance was developed based on the literature and applied in fieldwork, focussed on visual inspections of floor finishes. Photographs were taken and standardized inspection forms were used to collect the data about the defects in the floors.

This study provides a simple classification model to assess floorings' performance levels, capable to define priorities and help decision-makers in adopting maintenance activities. The model was validated through a case study, which showed that occasionally the choice of the floor finishes is incorrectly made only based on aesthetic criteria and not taking into account the main criteria, i.e. the use of space.

This work contributes to a development of methodologies to assess floorings' performance levels, to study the behavior of different floor finishes, to propose maintenance strategies to improve their performance in service and adopt better solutions in the buildings' design phase.

Laser metal deposition (LMD) is an important additive manufacturing (AM) technology, but the metallurgical defects, such as cracks and porosities, produced in LMD process will seriously affect the mechanical properties of the parts. The purpose of this paper is to propose a novel in-process defects detection method based on infrared scanning for LMD.

The defects detection principle is that, after every three to five layers have been deposited, the optical head of a high-precision infrared two-color pyrometer is driven to scan the defects by measuring the abnormal temperature peaks on the deposited surface. The experiments for verifying the defects detection principle were carried out.

The relationship between the temperature peak value and the dimensions of the defect was analyzed based on the heat conduction theory and curves of temperature peak value versus crack width or diameter of porosity.

This method can effectively improve the detection accuracy and the defects can be precisely located, which can meet the requirement of laser targeting re-melting and elimination of the defects.

The purpose of this paper is to investigate the properties of the classical goodness of fit test statistics X², G², GM², and NM² in testing quality of process represented as the trinomial distribution with dip null hypothesis and to devise a control chart for the trinomial distribution with dip null hypothesis based on demerit control chart.

The research involves the linear form of the test statistics, the linear function of counts since the marginal distribution of the counts in any category is binomial or approximated Poisson, in which the uniformly minimum variance unbiased estimator is the linear function of counts. A control chart is used for monitoring student characteristics in Thailand. The control chart statistics based on an average of the demerit value computed for each student as a weighted average lead to a uniformly most powerful unbiased test marginally. The two‐sided control limits were obtained using percentile estimates of the empirical distribution of the averages of the demerit.

The demerit control chart of the weight set (1, 25, 50) shows a generally good performance, robust to direction of out‐of‐control, mostly outperforms the GM² and is recommended. The X², NM² are not recommended in view of inconsistency and bias. The performance of the demerit control chart of the weight set (1, 25, 50) does not dramatically change between both directions.

None of the multivariate control charts for counts presented in the literature deals with trinomial distribution representing the practical index of the quality of the production/process in which the classification of production outputs into three categories of “good”, “defective”, and “reworked” is common. The demerit‐based control chart presented here can be applied directly to this situation.

The research considers how to deal with the trinomial distribution with dip null hypothesis which no research study so far has presented. The study shows that the classical Pearson's X², Loglikelihood, modified Loglikelihood, and Neyman modified X² could fail to detect an “out‐of‐control”. This research provides an alternative control chart methodology based on demerit value with recommended weight set (1, 25, 50) for use in general.

Additive manufacturing (AM) processes are the integration of many different science and engineering-related disciplines, such as material metrology, design, process planning, in-situ and off-line measurements and controls. Major integration challenges arise because of the increasing complexity of AM systems and a lack of support among vendors for interoperability. The result is that data cannot be readily shared among the components of that system. In an attempt to better homogenization this data, this paper aims to provide a reference model for data sharing of the activities to be under-taken in the AM process, laser-based powder bed fusion (PBF).

The activity model identifies requirements for developing a process data model. The authors’ approach begins by formally decomposing the PBF processes using an activity-modeling methodology. The resulting activity model is a means to structure process-related PBF data and align that data with specific PBF sub-processes.

This model in this paper provides the means to understand the organization of process activities and sub-activities and the flows among them in AM PBF processes.

The model is for modeling AM activities and data associated with these activity. Data modeling is not included in this work.

After modeling the selected PBF process and its sub-processes as activities, the authors discuss requirements for developing the development of more advanced process data models. Such models will provide a common terminology and new process knowledge that improve data management from various stages in AM.

Fundamental challenges in sharing/reusing data among heterogeneous systems include the lack of common data structures, vocabulary management systems and data interoperability methods. In this paper, the authors investigate these challenges specifically as they relate to process information for PBF – how it is captured, represented, stored and accessed. To do this, they focus on using methodical, information-modeling techniques in the context of design, process planning, fabrication, inspection and quality control.

Radio frequency identification tags for passive sensing have attracted wide attention in the area of Internet of Things (IoT). Among them, some tags can sense the property change of objects without an integrated sensor, which is a new trend of passive sensing based on tag. The purpose of this paper is to review recent research on passive self-sensing tags (PSSTs).

The PSSTs reported in the past decade are classified in terms of sensing mode, composition and the ways of power supply. This paper presents operation principles of PSSTs and analyzes the characteristics of them. Moreover, the paper focuses on summarizing the latest sensing parameters of PSSTs and their matching equipment. Finally, some potential applications and challenges faced by this emerging technique are discussed.

PSST is suitable for long-term and large-scale monitoring compared to conventional sensors because it gets rid of the limitation of battery and has relatively low cost. Also, the static information of objects stored in different PSSTs can be identified by a single reader without touch.

This paper provides a detailed and timely review of the rapidly growing research in PSST.

COVID-19 has changed life as we know. Data are scarce and necessary for making decisions on fighting COVID-19. The purpose of this paper is to apply Six Sigma techniques on the current COVID-19 pandemic to distinguish between special cause and common cause variation. In the DMAIC structure, different approaches applied in three countries are compared.

For three countries the mortality is compared to the population to distinguish between special cause variation and common cause variation. This variation and the patterns in it are assessed to the countries' different approaches to COVID-19.

In the DMAIC problem-solving approach, patterns in the data are distinguished. The special cause variation is assessed to the special causes and approaches. The moment on which measures were taken has been essential, as well as policies on testing and distancing.

Cross-national data comparisons are a challenge as countries have different moments on which they register data on their population. Furthermore, different intervals are taken, varying from registering weekly to registering yearly. For the research, three countries with similar data registration and different approaches in fighting COVID-19 were taken.

This is the first study with Master Black Belts from different countries on the application of Six Sigma techniques and the DMAIC from the viewpoint of special cause variation on COVID-19.