Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories

Technical management of vlcc/vlbc hull structures based on safety case principles

Recent high profile accidents involving environmental damage caused by structural failures in ageing oil tankers and bulk carriers has highlighted the importance of structural integrity issues involving these types of ships. Between 1980 and 1996, there were 186 total losses of bulk and combination carriers and 1,278 lives lost. These events have led to concerns from the public, media and within the international maritime community, about deteriorating ship structural safety standards and the environmental impact. Evidence suggests that structural failure may account for more ship losses than hitherto believed. Industry critics have complained that the quality of designs for new tonnage and effectiveness of the present control mechanisms governing structural condition for vessels in service, are inadequate. Due to the relatively low safety margins inherent in modern commercial ship structural designs, a buyer beware policy prevails in ship procurement. A weakness in current ship design practice appears to be the difficulty of incorporating an owner's individual preferences. Recognising that to be effective, improvements in ship structural design must be implemented at the design stage, this study addresses the challenge of further improving the structural safety and performance of large bulk ships through exercising specific options related to the structural design of the ship within the remit of the buyer. A broad comprehensive literature survey was conducted to cast a wide net around the problem. The complex web of regulatory controls affecting the design and operation of bulk ship hull structures was analysed and problems involving design, construction and maintenance of these vessels were uncovered to build evidence to justify proposing an improved method. An analysis of recent high profile tanker and bulk carrier accidents involving structural failure was performed, to determine root causes. These findings formed the basis for a proposed novel risk-based "design for safety" framework The core of the method is the new evidential reasoning (ER) algorithm developed on the basis of a MCDA evaluation framework and the evidence combination rule of the Dempster-Shafer (D-S) Theory. A number of structural design options focused on the cargo tank mid body area of a typical double hull VLCC were evaluated. A set of quantitative and qualitative criteria were identified and articulated, leading to a structural evaluation framework for eliciting preferences for competing options. The MCDAlER model provides a risk-based, rational, transparent methodology for rapid techno-economic evaluation of alternative structural designs, putting buyers in a stronger position to balance risks and determine the expected structural safety outcomes of different designs. The ER modelling is performed using the Intelligent Decision System (IDS) software program developed by Yang and Xu. The method was tested with an example and validated through a sensitivity study. Finally, the evidence necessary for constructing and demonstrating the MCDAlER structural evaluation framework was used to build the arguments for a safety case approach to hull structures using the Australian Offshore safety case model. The safety case for hull structures is built upon a foundation of existing prescriptive statutory and classification society structural regulatory requirements. The advantages of the safety case applied to oil tankers were explained, including suggestions for a new regulatory approach. The application of new technology and tools was discussed

A knowledge base system approach to inspection scheduling for fixed offshore platforms

In the offshore oil and gas industry in the UK, one of the most common forms of structure is the fixed steel jacket type of offshore platform. These are highly redundant structures subject to many random or uncertain factors. In particular, they are subject to uncertainties in the load distribution through the components, and to time-varying and cyclic loads leading to deterioration through fatigue. Operators are required to ensure the integrity of these structures by carrying out periodic inspections and repairing when necessary. Decisions on inspection, repair and maintenance (IRM) actions on structures involves making use of various tools and can be a complex problem. Traditionally, engineering judgement is employed to schedule inspections and deterministic analyses are used to confirm decisions. The use of structural reliability methods may lead to more rational scheduling of IRM actions. Applying structural reliability analysis to the production of rational inspection strategies, however, requires understanding the inspection procedure and making use of the appropriate information on inspection techniques. There are difficulties in collecting input data and the interpreted results need to be combined to form a rational global solution for the structure which takes into account practical constraints. The development of a knowledge base system (KBS) for reliability based inspection scheduling (RISC) provides a way of making use of complex quantitative objective analyses for scheduling. This thesis describes the development of a demonstrator RISC KBS. The general problems of knowledge representation and scheduling are discussed and schemes from Artificial Intelligence are proposed. Additionally, a system for automated inspection is described and its role in IRM of platforms is considered. A RISC System integrating suitable databases with fatigue fracture mechanics based reliability analysis within a KBS framework will enable operators to develop rational IRM scheduling strategies

Onshore Cross Country Pipelines Risk Assessment and Decision Making Under Uncertainty

Onshore cross-country pipelines are a critical component of refined product transportation in the oil and gas industry. The integrity of those pipelines is key to maintaining supply security, protecting the environment and human life. However, due to incessant pipeline damages and resultant consequences of fires, explosion and environmental pollution because of third-party events in Nigeria, stakeholders are looking at solutions to reduce the human, environmental and the financial losses. The main objective of this research is to develop risk-based models for identifying and assessing the oil and gas pipelines failures, including risk reduction decision-making framework and cost-benefit estimates. One of the major challenges of carrying out a pipeline risk assessment in some regions is the lack of reliable and objective data for data-driven analysis. The models developed in this thesis addressed this shortcoming and allowed the subjective data to be incorporated into the analysis. Hazards identification and ranking of the failure modes have been carried out using a modified FMEA based Fuzzy Rules Base (FRB) and Grey Relations Theory (GRT) to accommodate the uncertainty in terms of inadequate data. The results of modified approach serve as an input to developing the failure likelihood and this involves a Bayesian Network (BN) model of the identified failure mode. The BN model has been developed using Hugin software. The results of the BN feeds into the Evidential Reasoning (ER) model to aid risk management decision-making. Also, cost benefit estimates have been carried out to assess the cost benefit of implementing any risk reduction options. All the objectives set out in the thesis have been achieved. The research has contributed to the stated challenges by identifying the parameters for high failure incidences and develop various models and assess contributing failure factors and the risk control options to reducing the likelihood of the failure including cost benefit estimates

A Multi-Criteria Classification Framework for Spare Parts Management: A case study

The offshore petroleum industry can be described as a capital-intensive industry. Capital intensive refers to a heavy and high-value asset structure with long lifetimes that demands considerable effort to maintain. Large investments are required to produce goods and services, and the consequences of downtime, shortage and production loss are extensive. Efficient and reliable maintenance operations are essential to secure safe, productive and reliable production, creating a great incentive to stock up on all kinds of spare parts to reduce the consequences of the above-mentioned. However, there are great costs and inefficiencies related to spare parts inventories. Holding costs are high, turnover ratios are low, and inconsistent demand patterns make demand difficult to predict. Therefore, the trade-off between availability and efficiency is a fundamental principle in inventory management of spare parts. The industry puts a lot of effort into optimising spare parts inventories and spends resources on developing efficient and reliable spare parts operations. Among these efforts is spare parts classification. This is the process of classifying spare parts into distinct groups and is crucial to control the enormous number of parts with different characteristics. The decisions on which characteristics to use in classification practices is not straightforward and has been subject to research and debate for many decades. In current classification practices, most spare parts of an equipment are assigned the same criticality rank as the equipment itself, which is not necessarily the case. Therefore, Moreld Apply AS are interested in developing a method for spare parts classification that further evaluates criticality and consequence analysis on a spare parts level. This study presents a way to classify spare parts using a multi-criteria framework to establish precise criticality classes for each part. The findings in this thesis have ultimately led to the conclusion that multi-criteria approaches have great potential in the classification practices in the industry. We also see that the framework is already implementable for single case scenarios, such as the one analysed in this thesis, and provide reliable results. The results indicate that, in almost all instances, the criticality level of spares is reduced compared to the main equipment. The main contributions of this thesis is a framework with several steps guiding the user through the process of setting up the evaluation, preparing the analysis, as well as doing the analysis. Important aspects will be the selection of the most appropriate classification criteria, data collection processes and preparation activities. These topics form the main body of research.The offshore petroleum industry can be described as a capital-intensive industry. Capital intensive refers to a heavy and high-value asset structure with long lifetimes that demands considerable effort to maintain. Large investments are required to produce goods and services, and the consequences of downtime, shortage and production loss are extensive. Efficient and reliable maintenance operations are essential to secure safe, productive and reliable production, creating a great incentive to stock up on all kinds of spare parts to reduce the consequences of the above-mentioned. However, there are great costs and inefficiencies related to spare parts inventories. Holding costs are high, turnover ratios are low, and inconsistent demand patterns make demand difficult to predict. Therefore, the trade-off between availability and efficiency is a fundamental principle in inventory management of spare parts. The industry puts a lot of effort into optimising spare parts inventories and spends resources on developing efficient and reliable spare parts operations. Among these efforts is spare parts classification. This is the process of classifying spare parts into distinct groups and is crucial to control the enormous number of parts with different characteristics. The decisions on which characteristics to use in classification practices is not straightforward and has been subject to research and debate for many decades. In current classification practices, most spare parts of an equipment are assigned the same criticality rank as the equipment itself, which is not necessarily the case. Therefore, Moreld Apply AS are interested in developing a method for spare parts classification that further evaluates criticality and consequence analysis on a spare parts level. This study presents a way to classify spare parts using a multi-criteria framework to establish precise criticality classes for each part. The findings in this thesis have ultimately led to the conclusion that multi-criteria approaches have great potential in the classification practices in the industry. We also see that the framework is already implementable for single case scenarios, such as the one analysed in this thesis, and provide reliable results. The results indicate that, in almost all instances, the criticality level of spares is reduced compared to the main equipment. The main contributions of this thesis is a framework with several steps guiding the user through the process of setting up the evaluation, preparing the analysis, as well as doing the analysis. Important aspects will be the selection of the most appropriate classification criteria, data collection processes and preparation activities. These topics form the main body of research

A Multi-Criteria Classification Framework for Spare Parts Management: A case study

The offshore petroleum industry can be described as a capital-intensive industry. Capital intensive refers to a heavy and high-value asset structure with long lifetimes that demands considerable effort to maintain. Large investments are required to produce goods and services, and the consequences of downtime, shortage and production loss are extensive. Efficient and reliable maintenance operations are essential to secure safe, productive and reliable production, creating a great incentive to stock up on all kinds of spare parts to reduce the consequences of the above-mentioned. However, there are great costs and inefficiencies related to spare parts inventories. Holding costs are high, turnover ratios are low, and inconsistent demand patterns make demand difficult to predict. Therefore, the trade-off between availability and efficiency is a fundamental principle in inventory management of spare parts. The industry puts a lot of effort into optimising spare parts inventories and spends resources on developing efficient and reliable spare parts operations. Among these efforts is spare parts classification. This is the process of classifying spare parts into distinct groups and is crucial to control the enormous number of parts with different characteristics. The decisions on which characteristics to use in classification practices is not straightforward and has been subject to research and debate for many decades. In current classification practices, most spare parts of an equipment are assigned the same criticality rank as the equipment itself, which is not necessarily the case. Therefore, Moreld Apply AS are interested in developing a method for spare parts classification that further evaluates criticality and consequence analysis on a spare parts level. This study presents a way to classify spare parts using a multi-criteria framework to establish precise criticality classes for each part. The findings in this thesis have ultimately led to the conclusion that multi-criteria approaches have great potential in the classification practices in the industry. We also see that the framework is already implementable for single case scenarios, such as the one analysed in this thesis, and provide reliable results. The results indicate that, in almost all instances, the criticality level of spares is reduced compared to the main equipment. The main contributions of this thesis is a framework with several steps guiding the user through the process of setting up the evaluation, preparing the analysis, as well as doing the analysis. Important aspects will be the selection of the most appropriate classification criteria, data collection processes and preparation activities. These topics form the main body of research

Uncertainty analysis in competitive bidding for service contracts

Sustainable production and consumption have become more important internationally, which has led to the transformation of market structures and competitive situations into the direction of servitisation. This means that manufacturing companies are forced to compete through the supply of services as opposed to products. Particularly the suppliers of long-life products such as submarines and airplanes no longer simply sell these products but provide their capability or availability. Companies such as Rolls-Royce Engines achieve 60% of their revenue through selling a service rather than the engine itself. For a manufacturing company, the shift towards being a service provider means that they usually have to bid for service contracts, sometimes competitively. In the context of competitive bidding, the decision makers face various uncertainties that influence their decision. Ignoring these uncertainties or their influences can result in problems such as the generation of too little profit or even a loss or the exposure to financial risks. Raising the decision maker’s awareness of the uncertainties in the form of e.g. a decision matrix, expressing the trade-off between the probability of winning the contract and the probability of making a profit, aims at integrating these factors in the decision process. The outcome is to enable the bidding company to make a more informed decision. This was the focus of the research presented in this thesis. The aim of this research was to support the pricing decision by defining a process for modelling the influencing uncertainties and including them in a decision matrix depicting the trade-off between the probability of winning the contract and the probability of making a profit. Three empirical studies are described and the associated decision process and influencing uncertainties are discussed. Based on these studies, a conceptual framework was defined which depicts the influencing factors on a pricing decision at the bidding stage and the uncertainties within these. The framework was validated with a case study in contract bidding where the uncertainties were modelled and included in a decision matrix depicting the probability of winning the contract and the probability of making a profit. The main contributions of this research are the identification of the uncertainties influencing a pricing decision, the depiction of these in a conceptual framework, a method for ascertaining how to model these uncertainties and assessing the use of such an approach via an industrial case study.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved