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AbstractInternational fabrication codes and standards provide minimum distance criteria for proximity welds, although rigorous justification is lacking. These distances are either based on practical experience or mutual agreement and are often left to the engineering judgment of contractors, inspection engineers, etc., especially in cases of repair welds fabricated in proximity to existing welds. Previous studies have shown high tensile residual stresses and altered mechanical and microstructural properties between proximity welds. This article focuses on numerical and experimental quantification of residual stresses in the proximity region by X-ray diffraction (XRD) and finite element method (FEM) thermo-mechanical models. Specimens were machine welded, then repair welded at distances of 5–15 mm. A fair agreement in results was achieved between FEM and XRD. The most detrimental effect was observed at the weld root toe for the repair weld at 5 mm proximity, likely due to the high constraint and multiaxial stress state. These findings enable practitioners to propose technical justification and corrective actions while specifying minimum distance criteria for proximity welds.

Circular economy (CE) targets provide an economic model to focus on the efficient use of resources by minimization of the waste, reduction of the linear consumption of natural resources, long-term value creation, and making material consumption circular. The building and construction sector is responsible for significantly increasing the carbon footprint and linear material consumption. The identification and prioritization of end-of-life solutions during the decommission of the existing pre-cast concrete buildings enable the carbon footprint and linear material consumption to be alleviated. The CE targets enable end-of-life solutions for structural or non/structural components to be identified, while multi-criteria decision analysis enables their prioritization using a hierarchically structured decision model. A principal challenge, therefore, is to identify the assessment criteria for such a prioritization, aligning with the CE targets, and to develop an assessment framework. This manuscript demonstrates how to prioritize end-of-life solutions using the analytic hierarchy process (AHP). The decision hierarchy has been developed using the end-of-life solutions given in research findings within the structural and/or non-structural components. The decision hierarchy development, AHP prioritization, and sensitivity analysis enable practitioners to integrate the suggested approach with building information modeling (BIM). This enhances the potential for the effective integration of the CE economic model in the construction industry and to minimize the carbon footprint.

Maintaining minimum distance between repair and existing welds often becomes impractical due to the presence of compact layouts, original welds of branches, nozzles, etc. on offshore structural elements, pipelines, and piping. Although some international codes and standards provide criteria for maintaining a minimum distance between proximity welds, most of them lack technical justification in relation to their effect on the structural integrity of welded components. The development of residual stresses has a significant effect (i.e., a negative effect on tensile stresses and a positive effect on compressive stresses) on the integrity of the welds fabricated at proximity. Hence, it is important to investigate the residual stress distribution on welds fabricated at proximity, especially at a distance away from the weld toe. This study presents findings on the characterization of residual stresses by neutron diffraction at the proximity region between two girth welds. The two welds were fabricated at proximity, using two different welding procedure specifications and at three different distances, on a structural steel pipe, grade S355. The three different welding distances between weld toes were maintained at 5, 10, and 15 mm respectively. The neutron diffraction-based residual stress distributions were investigated at the POLDI neutron instrument at the Swiss spallation source SINQ in Switzerland. The axial, hoop, and radial components of the residual stresses were experimentally investigated between proximity welds. The findings revealed that residual axial stresses at a 5-mm proximity distance were increased beyond the yield strength of the structural steel, grade S355. The findings of this study enable practitioners to take remedial actions to minimize the residual stresses developed in girth welds fabricated at proximity. Also, the findings enable us to derive technical justification for maintaining a minimum distance and developing welding procedure for welds fabricated at proximity.

Maintaining the minimum allowable distance between proximity welds has always been considered a subject of debate among design engineers, welding engineers/inspectors and fabricators/engineering contractors. The scattered nature of guidelines available in welding codes and standards for maintaining the minimum allowable distance poses a significant challenge in the welding procedure and inspection criteria development process. This is especially critical for complex welded joints on submerged sections of offshore structures, in compact layouts/branched connections of topside piping components, and on topside structural joints (depending on the complexity). This manuscript presents the findings of an experimental study that was performed by fabricating two girth welds at proximity on an S355 steel tubular section with diameter of 219.1 mm and thickness of 8.18 mm. Proximity girth welds were fabricated on S355 tubular sections at three different distances between their weld toes, 5, 10, and 15 mm, respectively, using two different welding procedures. Welding procedure qualification record (WPQR) was performed, and all prescribed mechanical tests were recorded as per NORSOK M-101, a structural steel fabrication code. Although all results from the mechanical test met the minimum specified values as defend in the NORSOK code, the research findings revealed a noticeable difference in Charpy and hardness values for proximity region between adjacent welds. Considerable changes in final microstructure morphology were observed between proximity welds due to varying thermal cycles. These observations can form the basis for the future welding procedure qualification of critical welded joints, especially for proximity welds on critical welded joints of offshore structures and welds fabricated during replacement/repair procedures in compact piping layouts. Welding procedure qualification record (WPQR) for welds fabricated at proximity

The paper presents different techniques for digitizing grinding pins and discusses the use of digitalized pins and the results of measurements in technological process planning (TPP), focusing on the challenges of the digital era. It describes the potential of different measuring devices, taking into account the digitization of a real tool shape into virtual 2D and 3D models. The following methods for measuring grinding pins are presented in the study: contact and non-contact coordinate measurements – performed on coordinate measuring machines (CMM); optical measurements on microscopes (i.e. focus-variation technique); optical measurements using tool presetters; optical measurements with measuring arm; laser micrometer measurements; and laser triangulation sensor measurements. Moreover, the use of testers which are applied in contour measurements is analyzed. On the basis of the presented methods, taking into account their possibilities and limitations, we discuss how the obtained digital data can be used in the planning of technological processes.

Steel plates with openings are among the important ship structural components used in the ship’s hull to withstand the hydrostatic forces of the ocean, which cause sagging and hogging moments at the ship’s bottom. The existence of openings on plates can cause structural rupture, stress concentration and a decrease in ultimate strength. This research is aimed at investigating the influence of selected parameters on the ultimate capacity of steel plates with rectangular holes subjected to axial stress, using ANSYS finite element analysis (FEA) under its non-linear static structural programme. The main parameters investigated in this paper are the plate thickness, opening aspect ratio, number of openings, position of openings, and the boundary condition of the plate. The influence of these parameters on the stress of plates and their deformation was evaluated. The comparison of the numerical simulation with the well-established analytical method using the Navier solution and Roark’s Formulas showed a good agreement. Finite Element Analysis of Steel Plates with Rectangular Openings Subjected to Axial Stress

In typical fabricated structures, there are hundreds of joints, which acts as a potential location for fatigue cracking. Ageing and life extension (ALE) of such structures could be developed further by creating an optimal measure between the required quality level and fatigue performance defined in quality inspection code of welded joints. The quality system for welds is described in ISO 5817 standard, where acceptance criteria have been specified for different types of weld geometry imperfections. Challenge of establishing link between the fatigue performance and defined weld quality classes is not consistent in these inspection codes i.e. link between the design of weld having required fatigue strength and described fabrication quality as per acceptance criteria is missing. Furthermore, weld quality optimal assessment during the lifecycle of welded joints can sustainably improve their fatigue life by the adoption of good design practices. Fatigue loading is identified as one of the most important factors for degradation of complex welded structures, which are highly influenced by local and geometric features modifications at weld toe locations. In recent years, the International Institute of Welding (IIW) Commission XIII has recommended guidelines related to fatigue design and assessment of welded structures under which various fatigue life improvement techniques have become available lately. The focus of this manuscript is on recommendations specified by IIW on post weld fatigue life improvement of steel structures by High-frequency Mechanical Impact (HFMI) methods, a residual stress improvement technique. Residual stress improvement techniques contribute to fatigue strength increase by reducing harmful tensile residual stresses and by inducing beneficial compressive residual stresses at weld toe regions. These recommendations include major fatigue assessment based on nominal stress, structural stress, and effective notch stress methods. Major influencing factors namely thickness and size effects, influence of steel strength, loading effects, stress ratio are reviewed in detail for fatigue design class improvement of HFMI treated welds. This work has been carried out as part of a PhD research project, performed at the University of Stavanger, Norway. The research is fully funded by the Norwegian Ministry of Education.

Microenterprises provide more than 90% of the jobs in the Nordic Arctic region and are the main source of new job creation and innovation. Operations in this region can have many drawbacks related ...

Thin-walled cylindrical shell storage tanks are pressure vessels in which the walls of the vessel have a thickness that is much smaller than the overall size of the vessel. These types of structures have global applications in various industries, including oil refineries and petrochemical plants. However, these storage tanks are vulnerable to fire and explosions. Therefore, a parametric study using numerical simulation was carried out, considering the internal liquid level, wall thickness, material yield strength, constraint conditions, and blast intensity, with a diameter of 100 m and height of 22.5 m under different blast loads using the finite element analysis method. The thickness of the tank wall is varied as 10 mm, 20 mm, 30 mm, and 40 mm, while the fill level of internal fluid is varied as 25, 50, 75, and 100%. The blast simulation was conducted using LS-DYNA software. The numerical results are then compared with analytical results. The effects of blast intensity, standoff distance, wall thickness, and fill level of internal fluid on the structural behaviour of the storage tank were investigated and discussed.

This article focuses on the role and application of process parameters in technological process planning (TPP) and technological documentation (TD). The challenges and complexity involved in computer numerical control (CNC) machine tools and coordinate measuring machines (CMMs) programming have been taken into consideration with reference to the TD. The article presents the use of different programming platforms and implementation of them in technological process planning. The subtractive manufacturing and related measurements that are required during the TPP and TD phase have been taken into consideration. The findings and suggestions enable planners to incorporate the existing programming platforms and tools in the TPP and TD.

This manuscript demonstrates the selection of the best parameters’ combinations (BPCs) of the contact coordinate measurements of four different free-form surfaces (FFSs). The parameters: time of masking of measured points, scanning velocity, and distance between measured points were selected to investigate the BPCs. The levels of each parameter were estimated, based on the expert knowledge of the user of a coordinate measuring machine (CMM). The BPCs are the combinations that provide the form deviations closest to those calculated by using the reference parameters derived mainly from the recommendations of a CMM manufacturer. The selection of the BPCs was made, based on statistical analysis of the coordinate measurements’ results that had been obtained using the robust design approach (RDA). The measurements were taken for the selected FFSs by use of the coordinate measuring machine, ACCURA II, equipped with the VAST XT measuring probe.

Despite being considered the safest means to transport oil and gas, pipelines are susceptible to degradation. Pipeline integrity management (PIM) is implemented to lower the risk of failure due to degradation and to maintain the functionality and safety of pipelines. PIM consists of a set of activities for assessing the operational conditions of pipelines. These activities generate data with high volume, velocity, and variety, due to the length of a pipeline and the number of sensors and tools used to assess the pipeline's condition. This paper provides a comprehensive review in relation to the applications of machine learning (ML) in managing and processing data generated from PIM activities. ML applications in the elements of a PIM process (e.g., inspection, monitoring, and maintenance) are investigated. The aspects of ML techniques (i.e., type of input, pre-processing, learning algorithm, output and evaluation metric) applied in each element of PIM are examined. Current research challenges and future research opportunities in the application of ML in PIM are also discussed.

The machinery maintenance plays a significant role achieving the final quality characteristics of the manufactured products. As the concept of quality covers tangible (e.g. dimensional tolerances) and intangible (e.g. lead-time), the assessment of maintenance suppliers is vital in maintaining the quality of a manufactured product at an anticipated level. As the maintenance suppliers play a significant role in the context of the availability of spare parts and other materials needed for the effective realization of the maintenance process in order to assure the overall quality of the manufactured products, it is vital to achieve the aforementioned via minimization of machinery down time and maintenance backlogs. This paper illustrates the adaptation of empirical-knowledge-based development for the assessment of spare parts and maintenance related other material suppliers. That development has been used only in the manufacturing material suppliers’ assessment so far. The criteria used in the case study manufacturing firm for the material suppliers’ assessment, as well as other relevant criteria published in the literature, are taken into consideration in the current study. The paper also presents a development of a risk matrix for the assessment of maintenance suppliers, which is the core for the risk based assessments, prioritization and control process.

The aircraft maintenance processes play an important role in a safe operation of an aircraft. Maintenance services organizations take responsibility for the maintenance process and approve the airworthiness of an aircraft after undertaking the maintenance activities. International law determines the quality of aircraft maintenance processes by setting requirements concerning, among other, a quality management system, a safety management system and operators’ competences. As a consequence of the rising number of aircraft in operation, the volume of maintenance activities grows. However, the customers increasingly pose requirements concerning the minimization of the maintenance service lead time. In order to remain competitive, the maintenance service organizations have to reduce the lead time of their services. However, this objective in not easy to attain, since the complexity of aircraft maintenance operations require specific skills and pose a number of organisational and technical constraints to be respected during the maintenance process. In the paper, a mathematical programming model is developed in order to help decision makers in managing the operators’ skills during the operators assignment to the activities to be performed. In particular, the Hall’s marriage theorem is used to formalise complex restrictions of operators assignment to maintenance activities. The objective of the optimization problem is to minimize total makespan time. The model is applied to a case study.

The linear elastic fracture mechanics (LEFM) based remnant fatigue life (RFL) assessment of offshore pipeline is used to determine the inspection interval frequency of the aforementioned asset. One of the vital factors of the LEFM approach that determines the accuracy of the RFL estimate (and in turn of the inspection interval frequency) is the Stress Intensity Factor (SIF), which must be evaluated as accurately as possible. For simple crack geometries numerous closed-form equations available in various handbooks and industrial standards provide accurate SIF results. However, it is a common industry practice to utilize finite element method (FEM) for evaluating the SIF for the intricate crack geometries and the complex loading conditions. Although, FEM is known for its accurate SIF calculation, but due to its high computational expense and time-consumption, cycle-by-cycle SIF evaluation (required for the LEFM based RFL assessment) makes the aforementioned method quite laborious. Furthermore, using FEM to evaluate SIF for thousands of pipeline location (undergoing fatigue degradation) on an offshore platform seems to be impractical. Thus, in this manuscript authors have proposed a computationally inexpensive adaptive Gaussian process regression model (AGPRM) which may be utilized as an alternative to FEM for prediction of SIF to assess fatigue degradation in offshore pipeline. The training and testing data for AGPRM consists of 105 and 50 data points (load (L), crack depth (a), half-crack length (c) and SIF values), respectively. Latin Hypercube Sampling (LHS) is used to generate (L, a and c) values while SIF values are evaluated using FEM by carefully accounting for the discretization error emanating due to the finite mesh size in the FEM simulation. After the GPRM has been adaptively trained, it is used to predict the response of the 50 data points. On comparing the values of the SIF (obtained by AGPRM) with the SIF values obtained from FEM, the average residual percentage between the two is found to be 1.76%, thus indicating a good agreement between the AGPRM and FEM model. Furthermore, the time required to predict the SIF of 50 test points is reduced from 50 min (for FEM) to 12 s with the help of the proposed AGPRM, thus making RFL assessment less laborious and time consuming.

Manufacturing firms continuously strive to increase the efficiency and effectiveness in the maintenance management processes. Focus is placed on eliminating the unexpected failures which cause unnecessary costs and the production losses. Risk-based maintenance (RBM) strategies enable to address the above through the identification of probability and consequences of potential failures whilst providing a way for prioritisation of maintenance actions based on the risk of possible failures. Such prioritisations enable to identify the optimal maintenance strategy, intervals of maintenance tasks, and optimal level of spare parts inventory. However, the risk assessment activities are performed with the support of a risk matrix. Suboptimal classifications and/or prioritisations arise due to the inherent nature of the risk matrix. This is caused by the fact that there are no means to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. In this paper, a risk matrix is first developed in collaboration with one of the manufacturing firms in Poland. Then, it illustrates the use of fuzzy logic for minimisation of suboptimal prioritisation and/or classifications using a fuzzy inference system (FIS) together with illustrative membership functions and a rule base. Finally, an illustrative risk assessment is also demonstrated to illustrate the methodology.

Accurate prediction of the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and the often fatal consequences of fatigue failures. The work presented in this paper is an extension of the previous paper submitted to OMAE 2019. The main objective of this manuscript is to utilize Artificial Intelligence (AI) to predict fatigue strength, based on composition and process parameters, using the fatigue dataset for carbon and low alloy steel available from the National Institute of Material Science (NIMS) database, MatNavi. A deep learning framework Keras is used to build a Neural Network (NN), which is trained and tested on the data set obtained from MatNavi. The fatigue strength values estimated using NN are compared to the values predicted by the gradient boosting algorithm, which was the most accurate model in the OMAE 2019 paper. The comparison is done using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS). Thereafter, the trained NN model is used to make predictions of fatigue strength for the simulated data (1 million samples) of input parameters, which is then used to generate conditional probability tables for the Bayesian Network (BN). The main advantage of using BN over previously used machine learning algorithms is that BN can be used to make both forward and backward propagation during the Bayesian inference. A case study illustrating the applicability of the proposed approach is also presented. Furthermore, a dashboard is developed using PowerBI, which can be used by practicing engineers to estimate fatigue strength based on composition and process parameters.

The paper concerns the planning of complex technological processes (CTP). Nowadays, CTP are gaining new meaning in manufacturing environments. This is due to the increased number of innovative solutions having a crucial impact on the effectiveness of production. CTP can be defined as manufacturing approaches which are a blend of the traditional and verified manufacturing processes (TPs), such as traditional cutting, grinding or electro discharge machining, with modern technologies (emerging technologies, also known as complex processes (CPs)), which are under constant development, such as hybrid machining, 3D printing, micro and nanomachining, etc. The application of CPs in the process chain requires a calculation of risk and is usually acceptable in developed firms. Various research centers are looking for new applications of CPs and intensively studying their performance. New machine constructions, tools, manufacturing strategies and digital tools (DTs) are inter alia studied. As a result, some of the abovementioned developments reveal new perspectives for production performance. The paper discusses the role and place of these modern manufacturing techniques in CTP structures. It also points to and discusses a risk and reliability analysis. Moreover, teaching methodologies in the area of CTP are indicated, in order to formulate the proper approach of engineers within the CPs’ implementation phase. In this context, CTP may be supported by the use of digital tools (e.g. software tools).

Risk-based inspection (RBI) screening assessment is used to identify equipment that makes a significant contribution to the system's total risk of failure (RoF), so that the RBI detailed assessment can focus on analyzing higher-risk equipment. Due to its qualitative nature and high dependency on sound engineering judgment, screening assessment is vulnerable to human biases and errors, and thus subject to output variability and threatens the integrity of the assets. This paper attempts to tackle these challenges by utilizing a machine learning approach to conduct screening assessment. A case study using a dataset of RBI assessment for oil and gas production and processing units is provided, to illustrate the development of an intelligent system, based on a machine learning model for performing RBI screening assessment. The best performing model achieves accuracy and precision of 92.33% and 84.58%, respectively. A comparative analysis between the performance of the intelligent system and the conventional assessment is performed to examine the benefits of applying the machine learning approach in the RBI screening assessment. The result shows that the application of the machine learning approach potentially improves the quality of the conventional RBI screening assessment output by reducing output variability and increasing accuracy and precision.

Clients expect a short lead-time from aircraft maintenance services. In order to minimize the lead time, it is vital to analyse service processes related to maintenance tasks and identify existing problems such as bottleneck tasks and wastes related to different tasks. The main purpose of the manuscript is to demonstrate the use of the VSM-based (value stream mapping) methodology together with other tools, which were used in aircraft maintenance processes in minimizing the lead-time of maintenance services and, subsequently, minimizing the costs of maintenance services. The manuscript illustrates the use of VSM in enhancing the process cycle efficiency (PCE) of maintenance services provided in an aircraft maintenance services providing firm (AMSPF).

Spare parts management (SPM) is a typical challenge in the offshore and onshore petroleum industry. SPM consumes a significant amount of operational expenses that are attributed to maintaining the integrity of production and process facilities (PP in particular, ageing P&PFs and legacy assets exacerbate the challenge of maintaining integrity at the anticipated level. This manuscript firstly provides an overview of how additive layer manufacturing/three-dimensional (3D) printing has been effectively utilized by different manufacturers to produce equipment, which resembles the equipment used in the petroleum industry. Then, it discusses the potential use of a digital spare parts library and the optimal strategy to mitigate current challenges pertaining to SPM. Finally, it proposes an architecture for a digital spare-parts library.

This concept study focuses on technological process planning (TPP), taking into account the application potential of open access digital tools (OADT) in small- and medium-scale manufacturing enterprises (SMMEs). It presents the authors’ classification of digital tools (DT) used in the SMMEs and available groups of OADT. It also proposes possible scenarios’ potential for future TPP by taking into account the developments in artificial intelligence (AI) and immersive technologies, i.e. virtual and augmented realities (VR/AR). It also focuses on challenges and procedures regarding the implementation of DT in specific SMMEs’ environments, focusing on how open access tools play a crucial role at the first stages of SMME development, as these tools enable minimization of resource wastage. Although the capabilities of these tools are limited, it is vital to develop implementation strategies within a SMME, based on specific need(s).

Recent research underlines the crucial role of supply chain optimization, in terms of maximize profit and minimize cost. Today the stakeholders are also empowered and the organizations are becoming stakeholder-centered, relates to the main objectives of a supply chain are availability and inventory control so the particular aim for availability must relate to stakeholder satisfaction. The implementation of supply chain optimization in tire industry nowadays not only focuses on profit, but also on the environmental and societal effect that is considered as ways to achieve the sustainable supply chain and stakeholder satisfaction. Currently a wealth of literature on supply chain optimization with maximize profit and minimize cost, to the best of our knowledge there is limited state-of-the art review on supply chain optimization considering with economy, environment and stakeholder satisfaction. This manuscript analyze research stream on supply chain optimization with economy objectives such maximize profit and minimize cost, environmental effect and stakeholder satisfaction with the aim to relate the existing optimization methods to empirical research and reveal the conceptual framework. The paper classifies existing research streams and application in tire industry areas with different optimization subject. The results of this study gives outlook which optimization methods are available for supply chain managers and give a conceptual framework in tire industry considering sustainable supply chain factors from economic, environmental and societal effect.

An important element of ensuring the continuity of production is maintaining machines on a proper, high level of their performance and availability. The basic process which guarantees such requirements is the process of machine supervision realized properly and which involves not only activities connected with continuous monitoring of their technical state, but also with taking preventive and corrective measures related to the failures elimination. One of the key factors ensuring a high level of the process realization of the machine supervision is the right procedure of machine spare parts availability management. This work aims at developing a model of machine spare parts categorization. For the evaluation of the significance of spare parts in the correction process, the criteria connected to the machine corrective measures performance and logistics have been adopted. The proposed parts categorization, and determining the importance of a spare part in the machine corrective processes will have a substantial impact on reducing the time of a failure removal. Additionally, the adopted evaluation criteria in the area of logistics will have a considerable effect on the purchase lead time of spare parts as well as on reducing the costs related with their purchase and maintenance. The model was verified in practice.

Significant investments in productivity improvement approaches over the past two decades have been embedded in ‘lean’ principles and philosophies within the manufacturing sector. Although the manufacturing sector has focused on offering integrated bundles of physical goods and services via product-service systems with lean principles and philosophies, there has been considerably less adoption of lean methodologies in the service sector. Hence, it is vital to investigate the application potential of lean principles in service processes’ productivity improvement. This manus cript presents the results of a case study performed to improve the service performance of a firm providing plain old telephone services (POTS). The firm provides, among other things, telecommunication related ‘voice-grade telephone services’ employing analog signal transmission over copper loops. The analysis methodology contains tailor-made value stream mapping (VSM) and value stream analysis (VSA), which has been carried out to investigate the current state of the value adding [also including non-value adding (not essential) and non-value adding (essential)] activities involved in the POTS related tasks. A current state value stream map (CSVS-Map) has been developed and VSA has been performed. Based on the CSVS-Map and the results of VSA, a future state value stream map (FSVS-Map) has been developed. Finally, the resulting improved performance of the POTS related tasks has been presented. The goal of this paper was to identify the advantages of implementing lean tools in service processes in the example of the described case study. The main obtained advantages are a 50% decrease in service lead time, which gives the capability to deliver almost double the amount of POTS installation service.

Damage mechanisms reduce the ability of equipment to deliver its intended function and, thus, increase the equipment’s probability of failure. Damage mechanism assessment is performed to identify the credible damage mechanisms of the equipment; thereby, appropriate measures can be applied to prevent failures. However, due to its high dependency on human cognition, damage mechanism assessment is error-prone and time-consuming. Additionally, due to its multi-disciplinary nature, the damage mechanism assessment process requires unambiguous communication and synchronization of perspectives among collaborating parties from different knowledge domains. Thus, the Damage Mechanism Identification Ontology (DMIO), supported by Web Ontology Language axioms and Semantic Web Rule Language rules, is proposed to conceptualize damage mechanism knowledge in both a human- and machine-interpretable manner and to enable automation of the damage mechanism identification task. The implementation of DMIO is expected to create a leaner damage mechanism assessment process by reducing the lead-time to perform the assessment, improving the quality of assessment results, and enabling more effective and efficient communication and collaboration among parties during the assessment process.

This article demonstrates the implementation of lean principles for performance improvement in a manufacturing firm. Value stream mapping and waste identification diagrams (VSM+WID) are integrated to assess the level of currently existing waste and the overall current status of the manufacturing flow. The VSM+WID enables an increase in the awareness of relative waste distribution among different processes in the selected case study manufacturing unit. This manuscript demonstrates how to use VSM+WID to understand the current status of the manufacturing flow related challenges such as: overproduction, work-in-process, inefficient use of man-hours (e.g. unbalanced work distribution), etc. It also demonstrates the effectiveness of visualization of the performance gap between the current and future state. The aforementioned type of performance assessment enables effective identification of waste present in a manufacturing flow in order for future improvement initiatives to be taken., This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

An increased focus on risk based maintenance (RBM) optimization has been prompted in the offshore oil and gas (O&G) production and process industry due to the currently existing regulations and guidelines on preventing the functional failure risk (FFR) of rotating equipment and instrumentation. The RBM optimization approach prioritizes functions based on the potential risk of a given functional failure. Then, the equipment and instrumentation connected to the different functions are assigned with appropriate maintenance routines according to the potential risk of corresponding functional failure. This manuscript focuses on mitigating the suboptimal prioritizations made in implementing petroleum safety authority (PSA) specified regulations and NORSOK standard Z008 specified guidelines. It is mandatory to follow these to prevent functional failures on offshore O&G production and process facilities (P&PFs) operational on the Norwegian Continental Shelf (NCS). This manuscript suggests a fuzzy inference system (FIS) to minimize the suboptimal prioritizations of functions in the FFR analysis using an illustrative tailor-made risk matrix. This risk matrix has been developed to be similar to currently existing tailor-made risk matrices that have derived from Z008 guidelines and which have been used for FFR in different P&PF owner organizations operational on the NCS. Membership functions and the rule base were developed utilizing Z008 guidelines, data, experience, intuition and intentions of maintenance engineers. A risk rank calculation has been performed using the suggested FIS. The results indicate how the proposed approach helps in simplifying and making a more reliable and uniform FFR estimation.

Asset-intensive organisations are under rising pressure from their stakeholders to realise the optimum level of exploiting assets to achieve a balanced and sustainable performance (SP) over their life cycle. However, industrial organisational structures delineated along traditional disciplines fail to provide an asset-centric focus. Accomplishing SP in an asset-intensive organisation depends on the ability of the human authority in an organisational hierarchy to maintain different asset operations align with sustainable considerations. The level of physical assets' integrity in a particular industrial setting depends on the personnel ability for acquisition, exploitation (includes design and operation), maintenance, modification and disposal of critical assets and properties within the limits of universally accepted norms. Thus, human factor (HF) is a central measure to evaluate the ‘integrity’ of physical assets in asset management as the ‘integrity’ is a characteristic that human beings can have. The pu...

This paper proposes a novel method for the structuring of the knowledge of a service process in order to be processed by lightweight declarative computing infrastructures. Through the identification of self-similarities in the process, the flow of the structured information and the sequence of activities performed in the process are easily implemented by means of cyber-physical systems technologies, in order to timely meet the customer/stakeholder’s requirements. The study was performed in a telecommunication service providing organization. Service teams create a collaborative network. With the use of the CPS proposed in this work they can communicate problems and disseminate solutions. This methodology uses the information of a set of performance indicators of the service organization to achieve a better control of the effectiveness and the bottlenecks in the supply network. The methodology is borrowed from the mechatronics field and it is prone to a natural extension and reuse for the similar information structures in manufacturing processes.

The management of the integrity of engineering assets such as production plants, facilities and equipment can be a challenging task due to their interrelationships with financial, information, human and intangible assets. In order to ensure effective utilisation and to improve the management of such assets, one has to make balanced and effective decisions based on performance assessments. The main challenge for asset integrity management (AIM) is related to various aspects of its human dimension as apparent on organisational settings and associated cognitive dispensations. In this article, AIM is cascaded down to design, operational and technical integrity management. Furthermore, the performance of physical assets (PA) is discussed in terms of financial, societal and environmental dimensions that deliver sustainability value to the assets' owner. The management of such a sustainable value is vital in managing the overall asset integrity in order to minimise rising stakeholder pressure and achieve balance...

Super steel materials play a vital role in the construction and fabrication of structural, piping and pipeline components. In assuring the integrity of onshore and offshore operating systems, they enable life cycle costs to be minimized. In this context, Duplex stainless steel (DSS) material related welding on constructions and fabrications plays a significant role in maintaining and assuring integrity at an optimal expenditure over the life cycle of production and process systems as well as associated structures. In DSS welding, factors such as gap geometry, shielding gas supply rate, welding current, and type of the welding process are vital to the final joint performance. Hence, an experimental investigation has been performed using an engineering robust design approach (ERDA) to investigate the optimal settings that generate optimal super DSS (i.e. UNS S32750) joint performance. 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The identification and prioritization of locations that have potential for failure (also referred to as thickness measurement locations (TMLs)) in the in-service inspection planning of offshore topside piping equipment requires a significant amount of data analysis together with relevant information. In this context, planning personnel analyze data and information retrieved from piping inspection databases through enterprise resource planning (ERP) software to investigate possible degradation trends in order to recognize the TMLs that have reached a critical level. It is observed that suboptimal prioritization occurs due to time restriction vs. amount of data and/or information that has to be evaluated. The suboptimal prioritization omits some of the critical TMLs, increasing the risk of failures whilst also increasing cost due to taking non-critical TMLs into inspection. Therefore, this manuscript illustrates an approach to integrate the decision support modules (DSMs) via an artificial neural network model for the optimum prioritization.

The in-service inspection planning process for topside piping equipment of aging oil and gas (O&G) production and process facilities (P&PFs) involves personnel with different kinds of expertise, experience, and knowledge as well as a vast amount of data and information. To simplify the inspection planning process and increase the quality of an inspection program, various industrial organizations as well as researchers have been developing numerous techniques in an isolated fashion to address the challenges pertaining to different activities involved in the inspection planning process. In order to mechanize the overall inspection process, suitable techniques need to be identified for the different activities carried out in a generic inspection planning process. This manuscript discusses the potential use of multi-criteria decision analysis (MCDM) and artificial intelligence (AI) techniques. It also provides evidence about the suitability of AI techniques in relation to fuzzy logic and artificial neural networks for the mechanization of the inspection planning process in a dynamic manner.

Natural latex foam mattresses (NLFMs) are manufactured using the latex extracted in its purest form of milky white liquid, which is tapped from the trunks of mature rubber trees. The natural latex foam mattress manufacturing (NLFMM) process undergoes several processing stages before the natural latex is converted into an ultra cushioning material with a wide range of firmness and comfort. Among the other processing steps, the gelling process has been found to play a significant role in controlling the quality of the final product, provided that quality raw materials have been supplied to the manufacturing plant. This manuscript suggests a methodology for estimating optimal levels of parameters that affect the gelling process, focusing on minimizing the influence on mattresses’ surface quality deterioration. A case study has been performed in collaboration with one of the natural latex foam manufacturing plants to recognize main quality deterioration mechanisms and corresponding process parameters using the engineering robust design approach to establish the optimal combination of influencing factor levels.

Risk based inspection analysis (RBIA) on offshore oil and gas (O&G) production systems optimize level of in-service inspection. The potential failure risk of a system, a sub-system or a thickness measurement location (TML) of O&G production systems comprise the consequence of failure (CoF) and probability of failure (PoF). A tailor-made risk matrix supports the estimation of maximum inspection intervals. When the inspection intervals are calculated using a risk matrix, suboptimal classification tends to occur as there are no means to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. This manuscript suggests a fuzzy inference system (FIS) to overcome the aforementioned. Membership functions and the rule base development have been carried out in alignment with a tailor-made risk matrix which has been utilized by a production plant owner operator organization. A rule view and a calculation result have been demonstrated to illustrate the methodology.

Considerable amount of Oil and Gas (O&G) production and process installationsoperatingin the North Sea are getting aged and requiring continuous modifications. Nature of such contractual jobs is relatively short term with demanding deliverables. Hence, the engineering contractors require excellent coordination and seamless communication during all phases of a project. For any contractor/supplier, it is of utmost importance to ensure that equipment related technical information is made available at the right time to avoid any unexpected project delays. Experience shows that poor coordination among project participants can result in failing to comply with regulatory and governing requirements. Also, there are other challenges to managing tags, databases and tag history as well as keeping technical information integrity. This manuscript illustrates role of a tag management system developed by an engineering contractor company which provides services to operators in NCS. The particular system is developed and customized to meet a drilling rig client’s specification.

Copyright © 2012 Inderscience Enterprises Ltd. The ‘strategic’ management of technical integrity has become a high profile subject matter over recent years within inspection and maintenance of assets in the process industry. The assets such as production and process facilities require being optimised, prioritised and cost-effective inspection and maintenance, depending on whether they are at the beginning or end of their design life. In both cases, it is vital to ensure that sufficient condition monitoring data from all relevant sources are collated and analysed as a part in a planned scheme of inspection and maintenance. This is largely driven by certifying authority requirements, sound mechanical and corrosion engineering principles as well as inspection and maintenance approaches, indicated in a plant strategy. This manuscript suggests a model to execute plant strategy using analytic hierarchy process method. The model indicates the incorporation of requirements specified in a plant strategy for reaching optimised, prioritised and cost-effective outcome.

PurposeThe purpose of this paper is to provide a comprehensive overview of technical integrity management (TIM) and propose a methodology for assessing to which extent the technical integrity (TI) performance is balanced in terms of goal awareness among the personnel responsible for TIM, and the degree to which high level goals are implemented in maintenance strategies.Design/methodology/approachThe study involved a comprehensive literature survey as well as information and data collected in the Norwegian oil and gas (O&G) industry. Several discussion rounds were carried out with the industrial maintenance management experts to recognize the existing practices and to confirm the suggested model.FindingsThe literature review indicated that there is a need for a methodology for assessing to which extent the technical integrity (TI) performance is balanced in terms of goal awareness among the personnel responsible for TIM, and the degree to which high‐level goals are implemented in maintenance strategies. The suggested model can incorporate industrial data as well as intentions, intuitions and experiences of industrial experts who are making decisions sensitive to TI of a production installation.Research limitations/implicationsThe study has been conducted in relation to TIM of production installations on the Norwegian Continental Shelf (NCS). The study is limited to measuring TI performance in relations to experts' awareness.Practical implicationsThe study stresses the need for improving synergy between the TI and maintenance management function. It proposes a methodology to measure to which extent organizational priorities are balanced, while addressing financial as well as health, safety and environmental (HSE) interests when selecting a maintenance strategy.Originality/valueThis paper addresses a problem that is not given enough attention in the currently available literature. Available approaches suggest models and frameworks for measuring integrity performance. However, the operationalization of most of the existing models and frameworks, remains vague. Instead a formal mechanism is needed to analyze the gap between the present performance and performance targets.

Control system of a computer numerical control (CNC) machine has provisions to estimate machining parameters of a tailor made job. The machining parameter settings supposed to set aiming on the target machining effectiveness and efficiency, quality and martial to be machined. The CNC machine’s control system generated machining parameter settings for a tailor made machining job do not guarantee the optimum machining performance. This is mostly evident in electro discharge machining (EDM) which involves several control factors. Aforementioned necessitates a methodology for computing optimum machining parameter settings based on the target machining performance. This manuscript provides a mathematical framework for computing machine parameter settings. It has been developed employing the engineering robust design approach (ERDA). The manuscript also illustrates a case study explaining the use of the mathematical framework for computing machining parameter settings using an EDM application.

The NOSOK standard Z-008 suggests a matrix to perform criticality assessment for categorising the rotating equipment and instrumentation connected to them. The categorisation of aforementioned has been utilised for maintenance planning, inspection planning and for prioritising work orders. When the criticality assessments has been carried out using a criticality matrix, suboptimal classification tends to occur as no means to incorporate actual circumstances at the boundary of the input (such as mean time between failure, down time cost, etc.) ranges or at the levels of linguistic data and risk categories. This manuscript suggests a fuzzy inference system (FIS) to overcome the aforementioned challenge whilst minimising the variation that may occur during the criticality assessment. Membership functions and a rule base were developed aligning with the Z-008 standard recommended guidelines as well as utilising data, experience, intuition and intensions of maintenance engineers. An illustration of rule view and a calculation result has been demonstrated to illustrate the methodology.

Failures related to major structures in the long-term have highlighted the devastating consequences when poor welding practices and procedures compromise the integrity of the welds that hold them together. The inspection and testing alone do not prevent defects in welding, as it is acknowledged that defects occur in welds because of inappropriate welding procedures and welder performance. An important part of any quality control programme is assuring that the personnel have the appropriate skill to perform the work. This is mainly due to the fact that the execution of critical welds requires skilled welders who will follow the project welding requirements imposed by the regulations. In this manuscript, a methodology is proposed to prioritise the quality deterioration factors that are attributed to welding personnel performance. A case study is performed to verify the applicability of the methodology.