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Ni–Mn–Ga Heusler alloys are multifunctional materials that demonstrate macroscopic strain under an externally applied magnetic field through the motion of martensite twin boundaries within the microstructure. This study sought to comprehensively characterize the microstructural, mechanical, thermal, and magnetic properties near the solidus in binder-jet 3D printed 14M Ni50Mn30Ga20. Neutron diffraction data were analyzed to identify the martensite modulation and observe the grain size evolution in samples sintered at temperatures of 1080 °C and 1090 °C. Large clusters of high neutron-count pixels in samples sintered at 1090 °C were identified, suggesting Bragg diffraction of large grains (near doubling in size) compared to 1080 °C sintered samples. The grain size was confirmed through quantitative stereology of polished surfaces for differently sintered and heat-treated samples. Nanoindentation testing revealed a greater resistance to plasticity and a larger elastic modulus in 1090 °C sintered samples (relative density ~95%) compared to the samples sintered at 1080 °C (relative density ~80%). Martensitic transformation temperatures were lower for samples sintered at 1090 °C than 1080 °C, though a further heat treatment step could be added to tailor the transformation temperature. Microstructurally, twin variants ≤10 μm in width were observed and the presence of magnetic anisotropy was confirmed through magnetic force microscopy. This study indicates that a 10 °C sintering temperature difference can largely affect the microstructure and mechanical properties (including elastic modulus and hardness) while still allowing for the presence of magnetic twin variants in the resulting modulated martensite.
In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles.
Keywords: Classification, Data mining, Financial market, Predictive performance, Regression, Stock market prediction
This study investigates cause and effect relationships in cognitive maps and the coexistence of pairs of such relationships in cognitive maps of a chosen group of decision-makers. We call the existence of a pair of causal relationships shared by the group of decision-makers in their cognitive maps inter-causal relationship. We investigate the coexistence of the chosen pairs of causal relationships in the maps in terms of one of the causal relationships being a necessary and/or sufficient condition for the existence of the other using the tools of fuzzy-set qualitative comparative analysis. We develop and propose a framework to extract and examine the inter-causal relationships from the cognitive maps. The proposed method is based on set-theoretic consistency and coverage measures. We used empirical data (of 71 cognitive maps) collected from a cognitive mapping approach performed by individuals in management teams within a strategic decision-making simulation process to test the proposed approach. Empirical results show that our method can identify inter-causal relationships and provide analytic results for a more complex interpretation if the information arises from the structure of cognitive maps.
Laser powder bed fusion (L-PBF) additive manufacturing process was employed to manufacture polycrystalline Ni-Mn-Ga samples. The samples were heat-treated for chemical homogenization and grain growth. It is demonstrated that the chemical composition, resulting martensitic crystal structures, and phase transformation temperatures of the L-PBF-built Ni-Mn-Ga can be precisely changed in-situ by controlling the selective evaporation of Mn through adjusting the process parameters. Subsequently, repeatable and fully reversible magnetic-field-induced strain of 5.8% was measured in a single crystalline grain of an additive manufactured polycrystalline Ni-Mn-Ga sample exhibiting a 10M martensitic structure at ambient temperature. The results indicate that L-PBF can be used to manufacture Ni-Mn-Ga devices containing active parts that can be strained by an external magnetic field.
Keywords: Additive manufacturing, Laser powder bed fusion, Ferromagnetic shape memory, Twinning, Ni-Mn-Ga
We revisit the fuzzy similarity and entropy (FSAE) filter method for supervised feature selection and propose the C-FSAE filter method that only uses similarities of observations to their own class’s ideal vector (intra-class) and deploys a normalized scaling factor that accounts for the distance between ideal vectors normalized by the standard deviations of each feature in the classes. The same adjustments are implemented for the fuzzy entropy and similarity (FES) and the proposed version is termed as the C-FES. Three simple artificial example cases showcase that the C-FSAE and C-FES result in intuitive feature rankings and consistently rank the features according to their relevance to the classification problem. On most of the seven medical real-world data sets in this study the C-FSAE demonstrates competitive validation accuracies to ReliefF, Fisher score, Laplacian score and Symmetrical uncertainty and performs at least as good and often better than the FSAE. The test set accuracies indicate and confirm the competitive results of the C-FSAE, where it led for two data sets to the highest test set accuracy overall and for more than half of the data sets provided the highest test accuracy for at least one of the classifiers.
In this paper we propose three novel feature ranking methods for supervised feature selection in the context of classification which are based on possibility theory. All three methods – nonspecificity, strife and total uncertainty – are tested on eight artificial data sets and ten medical real-world data sets and benchmarked against ReliefF, the Fisher score, the fuzzy entropy and similarity (FES), the Fuzzy similarity and entropy (FSAE) filter, symmetrical uncertainty as well as using no feature selection. The feature ranking methods were applied following two approaches: (1) using a fixed threshold for the number of highest-ranking features selected and (2) using a hybrid feature selection approach with a classifier (k-nearest neighbor classifier, decision tree, similarity classifier, SVM) to select the optimal number of features to select. The results indicate that strife and the Fisher score are the two feature ranking methods that for both approaches are on average ranked the highest in terms of the test set accuracy on the real-world data sets. Besides that, for the hybrid approach, strife uses most of the time a considerably smaller number of features than nonspecificity and total uncertainty. In terms of stability, which was measured with the adjusted stability measure (ASM), the Fisher score and strife were among the most stable feature ranking methods in this study. Additionally, strife’s feature subsets were diverse compared to those of the remaining feature selection methods, making it a good candidate to be included in a feature selection ensemble.
Keywords: Classification, Feature ranking, Filter method, Dimensionality reduction, Fuzzy logic, Machine learning
The lack of information on the current water demand of individual thermal power plants is a problem for the planning of future energy systems, especially in regions with high water scarcity and an elevated power demand. This lack is linked to the limited availability of data on the type of cooling technology for these power plants. In this study, we propose a hybrid decision-tree based classification model to impute the missing values of the cooling technology for individual power plants globally. The proposed model is cross-validated on the GlobalData database and benchmarked against several approaches for missing value imputation of the cooling technology of individual power plants found in the scientific literature. The decision tree model (with the average test set accuracy of 75.02%) outperforms all alternative approaches in terms of accuracy, often by a considerable margin. In addition, for 103 out of the 137 minor regions in this study, the hybrid model yields the highest test set accuracy of all approaches. It is apparent that, in terms of accuracy, the proposed hybrid model seems to outperform more general models which are based on shares or the portfolio mix in a region/country. The proposed model can be replicated and used in future studies, which have different data sources at their disposal.
This book presents a selection of current research results in the field of intelligent systems and draws attention to their practical applications and issues connected with the areas of decision-making, economics, business and finance. The nature of the contributions is interdisciplinary – combining psychological and behavioural aspects with the theory and practice of decision-support, design of intelligent systems and development of machine learning tools. The authors, among other topics, discuss the multi-expert evaluation with intangible criteria, suggest a redefinition of the standard multiple-criteria decision-making framework, propose novel methods for causal map analysis and new feature selection methods. The topics are selected to stress the potential of the up-to-date intelligent methods to deal with practical problems relevant in these areas and to provide inspiration for advanced students, researchers and practitioners in the respective fields.
Keywords: Intelligent Systems Multi-expert Evaluation Cognitive Maps Multiple-criteria Decision Making Decision Support Feature Selection Machine Learning Classification Prediction Finance
Traffic speed and traffic jam prediction are necessary for a successful regulation of traffic flow and also for the prevention of accidents. This chapter contributes to the body of knowledge on traffic characteristics prediction by focusing on the possibilities of traffic speed prediction in an urban area of a medium-sized European city—the Finnish capital of Helsinki. The predictive ability of simple models such as ARIMA-family models, Linear Regression, K-Nearest Neighbor and Extreme Gradient Boosted Tree (XGBoost) is investigated with the prediction horizons of 5, 10 and 15 min. The main goal is to find out if the results provided by these models can be sufficient for traffic control in medium-sized city areas. Open data is obtained from the Finnish Transport Agency and the city of Helsinki is chosen for the purpose of the analysis. Particular attention is paid to the possibilities of predicting sudden speed drops and traffic jams in the highly regulated metropolitan area of Helsinki. Traffic and weather data are considered as inputs and traffic jams are identified from the predicted speed, i.e. using a timeseries approach, and using a classification approach. The results indicate that XGBoost outperforms all the other considered models for all prediction horizons, but the speed drops are clearly underestimated by the timeseries models. On the other hand classification-oriented models such as decision trees seem to be better suited for the prediction of traffic jams (speed drops below 40 km/h) from the same data and provide promising results.
To accomplish the climate-neutrality objective by 2050, it is necessary to increase the share of renewable energy sources globally. A significant portion of this burden should be carried by palm oil biomass. However, the palm oil biomass supply may not be large enough to satisfy the rising demand worldwide. International trade and maritime transportation networks may play a significant role in satisfying the objectives defined for biomass renewable energy worldwide. In this context, seaports play a major part in developing palm oil global biomass supply chains (GBSC). This study aims to fill the research gap by investigating the effects of ship technology developments (capacity and size), shipment distance, and mass flow of the palm oil GBSC on environmental emissions. To achieve this goal, a novel dynamic simulation model is developed and tested on two leading palm oil suppliers: Malaysia and Indonesia. The results show to what extent container ship technologies such as size and capacity would affect the environmental emissions in the next 30 years.
Keywords: Global biomass supply chain, biofuel maritime transportation, environmental emissions, dynamic model
In this study, hedging effectiveness of 10 currencies ETFs over the crude oil price fluctuations have been tested using symmetric and asymmetric dependence structure. We use ARMA-EGARCH model to obtain margins that are utilized in both static, and time-varying copulas to examine the static and time-varying dependence. Particularly, four static copulas (Student–t, Clayton, Frank, and Gumbel) and two time-varying copulas (Normal, and SJC) are used to explore both average and extreme dependence between 10 currency exchange-traded funds and the WTI crude oil. Our findings suggest that Invesco DB US Dollar Index Bullish fund (UUP) provides the strongest hedging effectiveness against crude oil price volatility both in average and extreme dependence scenarios. Whereas WisdomTree Bloomberg US Dollar Bullish Fund (USDU) and Invesco Currency Shares Japanese Yen Trust (FXY) appeared to behave as safe havens against crude oil prices as shown by significant extreme dependence parameters during bearish periods. Moreover, Invesco DB US Dollar Index Bearish Fund (UDN) is shown to be a good safe haven in the time of stress. Further, the remaining six ETFs are found to be positively linked with crude oil prices showing significant upper and lower tail dependence.
Exchange traded funds
WTI crude oil
Most of the studies investigating machine learning methods such as neural networks and their ability to forecast stock markets deploy a binary target (buy, sell). However, when trading strategies are implemented based on the prediction of a binary target, they can be vulnerable to misclassification and their overall return may also be more susceptible to transaction cost. This research work used the well-known S&P 500 index and neural networks to compare three different targets (binary and two different ternary cases: a ±0.5% threshold and a ±1% threshold) that represent different cut-offs for the buy and sell classes. In addition, different thresholds for the class probabilities are used, that reflect the confidence that the neural network has in the prediction in order to decide when a buy and sell decision is made in the trading strategy. The experiments including transaction costs indicated that increasing the confidence threshold on a binary model increases the returns gained. Moreover, stricter cut-offs for the target classes tended to decrease the confidence thresholds required to obtain the best performances for the strategy.
S&P 500 index
This chapter proposes a new multi-criteria decision-making (MCDM) problem formulation. We generalize the standard MCDM problem by assigning a specific role to one alternative from the pool of alternatives—to the one the decision maker is currently in possession of. We call this current solution a baseline. We propose that the baseline can be treated differently from the other alternatives and in fact can represent the reference solution for the whole decision-making problem. It can even serve as a basis for the determination or modification of criteria weights. The introduction of the baseline in MCDM problems allows for more customizability in the modeling of real-life decision-making. Two rules for criteria weight formation based on the baseline are introduced and discussed: Appreciating Possessed—weights of criteria are proportional to the satisfaction of the criteria by the baseline; Craving Unavailable—higher weights are assigned to those criteria that are less satisfied by the baseline while lower weights are assigned to criteria that the baseline satisfies better. Combining these two rules gives birth to several common behavioral effects, such as reluctance to switch to an alternative identical with the baseline; unwillingness to switch to a better alternative if satisfied with the baseline; endless switching between two alternatives in case of poor satisfaction; and seemingly exaggerated impact of a new feature introduction. The overall satisfaction with the baseline is quantified, however the specific role of the baseline alternative gives the name to the proposed approach. Overall, we provide a formal model for many real-life decision-making problems that are difficult to model under the standard MCDM problem formulation.
In this paper we introduce a transformation of the center of gravity, variance and higher moments of fuzzy numbers into their possibilistic counterparts. We show that this transformation applied to the standard formulae for the computation of the center of gravity, variance, and higher moments of fuzzy numbers give the same formulae for the computation of possibilistic moments of fuzzy numbers that were introduced by Carlsson and Fullér (2001) for the possibilistic mean and variance, and also the formulae for the calculation of higher possibilistic moments as presented by Saeidifar and Pasha (2009). We also present an inverse transformation to derive the formulae for standard measures of central tendency, dispersion, and higher moments of fuzzy numbers, from their possibilistic counterparts. This way a two-way transition between the standard and the possibilistic moments of fuzzy numbers is enabled. The transformation theorems are proven for a wide family of fuzzy numbers with continuous, piecewise monotonic membership functions. Fast computation formulae for the first four possibilistic moments of fuzzy numbers are also presented for linear fuzzy numbers, their concentrations and dilations.
Keywords: Center of gravity; Fuzzy number; Possibilistic mean; Possibilistic variance; Possibilistic moment; Transformation
The transportation sector is responsible for the largest share (29% in 2019) of greenhouse gas emissions (GHG). One of the ways to reduce this share consists in introducing lightweight vehicles in order to diminish fuel consumption, and, in consequence, GHG emissions. Magnesium (Mg) carries considerable potential as a lightweight material to be used in automotive industry, e.g., in gearboxes, front end and IP beams, steering column and driver’s airbag housings, steering wheels, seat frames and fuel tank covers. Weight savings resulting from the use of Mg could reach up to 70% of the conventional parts. However, energy consumption and emissions from magnesium primary production are higher than for steel and aluminum. Therefore, enhancing magnesium recycling would be vital for ensuring its sustainable use. This study assesses the life cycle of magnesium extracted from end-of-life products. Using system dynamics modelling, we quantify the potential environmental benefits and examine aspects related to circularity of magnesium to be used in the automotive industry. Energy consumption, water use and related emissions are assessed in processes of functional (recovered Mg reused in the vehicle manufacturing processes) and non-functional (recovered Mg as an element used in aluminum alloys) recycling as well as casting and molding. Obtained results show that the implementation of circular economy strategies may help in increasing the supply of magnesium from the automotive industry up to 0.70 million tonnes (mt) in 2050. As shown by the analysis, 43% of total energy consumption and water use at the global Mg recycling stage in 2050 corresponds to non-functional casting and molding in automotive industry, followed by non-functional recycling (11%), functional casting and molding (7%) and functional recycling (2%). Also, it is estimated that the existing technologies in functional and non-functional Mg recycling in the automotive industry contribute to the global warming with an emission of 5.62 and 29.66 mt of CO2 eq. in 2050, respectively.
Keywords: Critical materials; Circular economy; Environmental sustainability; Dynamic modelling
Decarbonization of economy is intended to reduce the consumption of non-renewable energy sources and emissions from them. One of the major components of decarbonization are “green energy” technologies, e.g. wind turbines and electric vehicles. However, they themselves create new sustainability challenges, e.g. use of green energy contributes to the reduction of consumption of fossil fuels, on one hand, but at the same time it increases demand for permanent magnets containing considerable amounts of rare earth elements (REEs). This article provides the first global analysis of environmental impact of using rare earth elements in green energy technologies. The analysis was performed applying system dynamics modelling methodology integrated with life cycle assessment and geometallurgical approach. We provide evidence that an increase by 1% of green energy production causes a depletion of REEs reserves by 0.18% and increases GHG emissions in the exploitation phase by 0.90%. Our results demonstrate that between 2010 and 2020, the use of permanent magnets has resulted cumulatively in 32 billion tonnes CO2-equivalent of GHG emissions globally. It shows that new approaches to decarbonization are still needed, in order to ensure the sustainability of the process. The finding highlights a need to design and implement various measures intended to increase REEs reuse, recycling (currently below 1%), limit their dematerialization, increase substitution and develop new elimination technologies. Such measures would support the development of appropriate strategies for decarbonization and environmentally sustainable development of green energy technologies.
Keywords: Green energy; Energy supply; Critical materials; Decarbonization measures; Environmental sustainability
Evaluation is an important part of any design, construction or engineering process. The selection of the most appropriate form can prove crucial for the success of the product or solution that is being designed or developed. The selection of criteria, their measurability and the ability to estimate future values of some criteria can influence the result of the evaluation, as well as the definition of the overall goal and the selection of evaluators. Apart from measurable criteria, also emotions and attitudes can play an important role in the success of the final solution. This paper suggests an adaptation of the interval-valued semantic differential method proposed by Stoklasa et al. in 2019 for the purpose of design evaluation. We discuss the process of design evaluation using this tool and suggest its use in multi-expert evaluation setting. We propose a definition of consensus in this context, discuss the issue of the ease of achieving consensus, and suggest an approach that can provide information on the level of agreement of the evaluators in terms of the evaluation expressed as a object in the semantic space. We also suggest a methodology for the selection of the best design based on the aggregated multi-expert evaluations.
Keywords:Design, Evaluation,TRIZ, Semantic differential, Interval-valued, Semantic design,Consensus
For most of the time, equity index option implied volatilities exceed the corresponding realized volatilities. The resulting volatility risk premium seems to be directly linked with the equity risk premium, which motivates to study whether this investor risk aversion-related premium has explanatory power on the future stock index returns. Based on several linear regression models, this study shows that volatility risk premiums can explain a non-trivial fraction of the aggregate stock returns in Europe. Furthermore, both local and global risks are found to be systematically priced. Our findings confirm the consistency and deterministic power of volatility risk premium in the European equity markets. Additionally, the evidence supports the hypothesis that the global volatility risk and equity market premium are inter-linked.
This paper proposes a novel framework based on a recently introduced classifier called multi-local power mean fuzzy k-nearest neighbor (MLPM-FKNN) and the Minkowski distance to classify biomass feedstocks into property-based classes. The proposed approach uses k nearest neighbors from each class to compute class-representative multi-local power mean vectors and
the Minkowski distance instead of the Euclidean distance to fit the most suitable distance metric based on the properties of the data in finding the nearest neighbors to the new data point. We evaluate the performance of the proposed approach using three biomass datasets collected from several articles published in reputable journals and the Phyllis 2 biomass database. Input
features of the biomass samples include their characteristics from the proximate analysis and ultimate analysis. In the developed framework, we interpret the biomass feedstocks classification as a five-class problem, and the classification performance of the proposed approach is benchmarked with the results obtained from classical k-nearest neighbor-, fuzzy k-nearest neighbor- and support vector machine classifiers. Experimental results show that the proposed approach outperforms the benchmarks and verify its effectiveness as a suitable tool for biomass classification problems. It is also evident from the results that the features from both ultimate and proximate analyses can offer a better classification of biomass feedstocks than the features considered
from each of those analyses separately.
Keywords: Biomass feedstocks, Fuzzy k-nearest neighbor, Machine learning, Minkowski distance, Proximate properties,
Magnetic shape memory (MSM) alloys have a high potential as an emerging class of actuator materials for a new generation of fast and simple digital components. In this study, the MSM alloy Ni50.5Mn27.5Ga22.0 was built via laser powder bed fusion (L-PBF) using gas atomized powder doped with excess Mn to compensate for the expected evaporation of Mn during L-PBF. The built samples were subjected to stepwise chemical homogenization and atomic ordering heat treatments. The experiments followed a systematic experimental design, using temperature and the duration of the homogenization treatment as the varied parameters. Overall, the produced samples showed only a minor variation in relative density (average density ~98.4%) and chemical composition from sample to sample. The as-built material showed broad austenite-martensite transformation and low saturation magnetization. The crystal structure of the as-built material at ambient temperature was a mixture of seven-layered modulated orthorhombic (14 M) and five-layered modulated tetragonal (10 M) martensites. Notably, ordering heat treatment at 800 °C for 4 h without homogenization at a higher temperature was enough to obtain narrow austenite-14 M martensite transformation, Curie temperature, and saturation magnetization typical for bulk samples of the same composition. Additionally, homogenization at 1080 °C stabilized the single-phase 14 M martensite structure at ambient temperature and resulted in considerable grain growth for homogenization times above 12 h. The results show that post-process heat treatment can considerably improve the magneto-structural properties of Ni-Mn-Ga built via L-PBF.
Keywords: Additive manufacturing, Powder bed fusion, 4D printing, Magnetic shape memory materials, Magnetic properties
This paper focuses on recognizing different postal shipment types from images taken by the sorting machine. Greyscale images obtained from sorting machines are used to build a classifier using transfer learning to recognize seven different classes of shipments. Three convolutional neural networks (VGG16, GoogLeNet and ResNet50), that were pre-trained using the ImageNet dataset, were used as feature extractors and the extracted features were subsequently supplied to a neural network classifier. VGG16 demonstrated the best performance for six out of the seven classes and achieved an overall mean accuracy of 95.69% on the independent test set. The model accomplished F1 scores exceeding 90% for five out of seven classes, only having a lower recall for the aggregated class “Other” and shipments from abroad. The results of this study highlight the potential of transfer learning for computer vision in the context of shipment classification.
In this paper we present similarity based TOPSIS with OWA operators. The motivation behind called TOPSIS by aggregating alternatives’ similarities towards positive ideal solution and negative ideal solution and aggregating
these similarities using OWA. The use of linguistic quantifiers represented by OWA weights generated by a selected RIM quantifier allows for the reflection of decision-maker’s attitude to risk in the calculation of the similarities of the alternative with positive and negative ideal solutions.
This chapter focuses on the technological, the managerial, and the societal transformation from the old manufacturing system into the new, discussing the drivers, the challenges, and the opportunities connected to the transformation. The suggestion is that we are moving from a Taylorist-Fordian factory model towards “Manufacturing-as-a-Network,” which indicates new types of busines possibilities, risks, and transformative implications to the society at large. Traditionally, manufacturing happens within factory walls, where a factory is understood as a place for mass production of goods. It is an assembly of machines and workers who are organized and managed to maximize efficiency and productivity. “Manufacturing-as-a-Network” is unlike the factory as we know it and answers to the needs of the postindustrial society. It is a network structured to perform specific and tailored products in collaboration with customers, for customers, and sometimes by customers.
Industry 4.0 Manufacturing Factory Business model Network
This paper suggests a multiple-criteria decision-support tool for voters, that compares the attitudes of the voters with the declared attitudes of the political parties in several sets of relevant issues. The model intends to identify parties that seem to provide the best fit with the voter attitude-wise. The data input methodology uses discrete 5-point Likert-type
scales. We investigate the effect of the inclusion of weights of
different sets of issues, of the numerical anchors of the values of the Likert-type scales and also of the potential presence of extremity/leniency effect on the suggestion of the “most compatible” political party suggestion. We also propose a simple fuzzyrule based evaluation tool to identify serious incompatibilities or desired compatibilities in the attitudes of the voter and the party to the relevant issues. This tool introduces (un)acceptability thresholds for the differences in attitudes between the parties and the respondents and provides lists of parties to vote for or to avoid voting for accompanied by the strengths of these suggestions. The tool is shown to have several desirable features including lower sensitivity to small differences in the attitudes, respondents’ ability to express their preferences and also preventing the compensation of unacceptable differences in some categories of important issues by high compatibility in the
Keywords: Critical materials; phosphorus; social sustainability; circular economy; dynamic modelling
This paper discusses the value proposition of a system-level Digital Twin (DT) in the context of complex manufacturing processes from the managerial perspective. The central promise of DT-technology is to use and iterate the available real-time process information in a simulation setting, transforming it instantly to operational or managerial-level decision-making implications. Despite the clear potential of this emerging technology, a gap of knowledge exists on how such DT could be implemented and what would be its defining features. The key contribution of this research is to lay out the central discrepancies between the promise of digital twin technology vision versus what is possible within the limits of current industrial infrastructure in the short- and mid-term. This research builds on the currently existing scientific literature which we use to point out ten foundational issues of system-level digital twins that are analyzed and discussed in detail. As conclusion, we propose that large system-level DT projects have a managerial rationale only when several preliminary conditions are met and fulfilled.
Keywords: Digital twin simulation, investments cyber-physical system
Abstract Optimization of operations and maintenance (O&M) in the industry is a topic that has been largely studied in the literature. Many authors focused on reliability-based approaches to optimize O&M, but little attention has been given to study the influence of macroeconomic variables on the long- term maintenance policy. This work aims to optimize time-based maintenance (TBM) policy in the mining industry. The mine environment is reproduced employing a virtual model that resembles a digital twin (DT) of the system. The effect of maintenance decisions is replicated by a discrete event simula- tion (DES), whereas a model of the financial operability of the mine is realized through System Dynamics (SD). The simultaneous use of DES and the SD allows us to reproduce the environment with high-fidelity and to minimize the cost of O&M. The selected illustrative case example demonstrates that the proposed approach is feasible. The issues of using high dimensional simulation data from DT-models in managerial decision making is identified and discussed.
Transport systems significantly impact our environment, accounting for 29% and 25% of the global energy consumption and greenhouse gas (GHG) emissions, respectively. Therefore, international efforts promote the use of electric vehicles (EVs) to reduce overall GHG emissions with the aim of achieving the energy transition from fossil fuels to renewable energy sources. As a result, the world’s demand for EVs is expected to increase exponentially, leading to increase in using lithium-ion batteries (LIBs). Therefore, the battery is considered the most important component of an EV, and a vital industry with increasing importance for the economy and environment. Based on secondary data, this study aims to underline the most relevant factors for developing a circular design system of LIBs across the value chain of EVs. Due to the dynamic nature of LIBs and EVs market over time and complex interrelationship among processes and stages of their value chain, this study adopts a system dynamics approach. The results of the study reveal the impacts of different factors and their interrelations in the value chain of EVs. The findings of this study would contribute to the decision making and management at different stages in the EVs’ value chain to accelerate a sustainable mobility and energy transition in line with eight relevant sustainable development goals (SDGs).
Keywords: Lithium-ion batteries, electric vehicles energy transition, sustainable mobility
This paper presents the first fully possibilistic method for real option valuation of investment projects, a new possibilistic variant of the fuzzy pay-off method for real option valuation. The new variant is derived by using the Luukka-Stoklasa-Collan transformation and is proven to be consistent with financial theory. The new variant is comparatively analyzed with the original method and the previously presented probabilistic variant. Fast computation formulae for the new variant in all use-cases in the triangular context are presented and complete fast computation formulae also for the previously presented probabilistic variant of the method are presented for the first time. The use of the new variant is illustrated with a set of numerical examples including examples of Research and Development investment analysis.
The paper discusses the possibilities of adapting the recently introduced interval-valued semantic differential method to the multiple-criteria decision-making and evaluation context. It focuses on the differences and common ground of the intended use of the original semantic differentiation method and general multiple-criteria evaluation problems. The paper identifies the
aspect that can be beneficial in the multi-expert evaluation setting and also possible limitations stemming from the transition to the multiple-criteria (or multi-expert) evaluation context. Finally the paper suggests potential application areas for the (interval- valued) semantic differential based methods.
Decision-makers face several challenges on transportation network as the main component of logistics management in the biomass supply chain (BSC). Transportation of biomass focuses on how and when to get raw materials, intermediate products, and finished goods from their respective origins to their destinations. Each supplier can implement strategic and tactical decisions to reduce costs and improve local and export customer service levels in a responsive, economic, and sustainable transportation network. In the process of quantifying economic and environmental sustainability, it is necessary to take into account the greenhouse gas emissions and cost resulting from the transportation of the biomass. This study focuses on the techno-economic and environmental assessment of BSC by considering three transportation modes that include truck, train, and barge. For this purpose, a simulation model is developed using AnyLogic software and tested on a case study in Malaysia. Results show that each type of transportation plays a vital role in designing a BSC transportation network to decrease the cost and GHG emissions. The results of this work would help the biomass industry stockholders and suppliers to define long, and short-term horizons to achieve a timely, cost-effective, and sustainable BSC transportation network.
Keywords: Biomass supply chain; transportation modes; environmental; transportation cost; computer simulation
Direct material budgeting is an essential part of financial planning processes. It often implies the need to predict quantities and prices of hundreds of thousands of materials to be purchased by an enterprise in the upcoming fiscal period. Distortion effects in demand projections and overall uncertainty cause the enterprises to rely on internal data to build their forecasts.
In this paper we are dealing with material demand forecasting and evaluate the feasibility of fuzzy time series forecasting models as compared to classical forecasting models. Relevant methods are shortlisted based on existing practice described in academic research. Three datasets from industry are used to evaluate the predictive performance of the shortlisted methods. Our findings show an improvement in prediction accuracy of up to 47% compared to naïve approach. Fuzzy time series models are reported to be the most reliable forecasting method for the analyzed intermittent time series in all three datasets.
The paper applies the tools of fsQCA and their recent modifications by Stoklasa, Luukka and Talášek to analyze the possible drivers of high performance of European ESG funds. 429 mutual equity growth ESG funds from the European area are being analyzed. We focus mainly on the connection
of Morningstar sustainability rating with the performance of the funds during 2018-2021 measured by Jensen’s alpha and the Sharpe ratio. Other possible drivers of the success of these funds are also being explored. We identify the prevailing assumed relationships between funds’ sustainability and other characteristics with their performance and formulate rules to be investigated using the fsQCA methodology. More specifically the possibility of high performance being associated with a high sustainability rating of the funds is explored in detail. Our results indicate that although the high performance cannot be clearly associated with the high sustainability rating of a fund, high sustainability rating seems to be preventing the low performance of the fund.
Additive manufacturing (AM) is a manufacturing method that creates components in a layer-wise manner. Laser-based powder bed fusion (L-PBF) is one of the most used AM subcategories to manufacture metal components, referred to in this thesis as metal AM/LPBF. The effective use of AM offers a trifactor of part complexity, simplified manufacturing and improved performance with digital tools to the achievement of resource-efficient, cost-effective, durable components as well as waste and emissions reductions. Currently, this manufacturing method can be used to manufacture optimised, lightweight and multi-material components. AM has inherent limitations that need conscious designing and planning to be able to offer the expected benefits. The design system (designing and manufacturing) can either positively or negatively influence the integrity of the final component. Critical consideration of these is often required to avoid unwanted defects that may influence the performance of the final components. This often increases labour intensiveness, digital tools, time and consequent increase in costs. The practice of sustainable manufacturing focuses on product design that has the least negative environmental impact through economically-sound processes that support waste reduction and long-term cycle usage goals, termed circular economy, (CE). The question then is how can metal AM/L-PBF enhance sustainability and the CE to meet the goals of sustainable manufacturing? How can the benefits offered by metal AM/L-PBF be evaluated from a life cycle (LC) perspective?
The principal motivation of this thesis was to offer a critical fact-based contribution that is free from subjective or commercial considerations to support the sustainability arguments of metal AM/L-PBF. The main aim of the thesis was to identify the hotspots of metal AM/L-PBF that could be optimised to improve sustainable practice. The objective of this thesis was to theoretically and experimentally study how metal L-PBF enhances the achievement of sustainability and the CE through energy-efficient, materialefficient processing and the minimisation of waste and emissions.
Firstly, this thesis includes investigatory studies on the environmental and economic aspects of sustainability of metal AM/L-PBF through life cycle inventory (LCI) and supply chain analyses. A preliminary review of the social aspect of sustainability is generally presented. Secondly, the thesis incorporates a practical investigation of the effect of process parameters in metal L-PBF on melt pool formation and spatial resolution of finely-featured metal components. Thirdly, the thesis uses reviews and case studies to assess the influence of simulation-driven design for additive manufacturing (simulationdriven DfAM) on the life cycle cost (LCC). Fourthly, the thesis investigates the flexibility and suitability of manufacturing intricate and multi-material electrochemical separation units using reviewed data. The review focused on how metal L-PBF manufactured electrodes improved performance and cost-efficiency. The final part of the thesis was carried out as discussions with industrial representatives on the benefits/limitations of metal L-PBF to identify practical strategic approaches to harness the identified benefits/limitations of metal AM/L-PBF. The discussion aimed to modify an initially created LCC-driven model in publication 4 and to highlight its suitability as a useful tool to support decision-making in industries to the adoption of metal AM/L-PBF. Business process modelling, (value chain analysis (VCA); strength, weakness, opportunities and threat (SWOT) models) were used to identify the best adoption plan to maximise value creation from idea generation to end-of-life (EOL).
The results of this thesis showed that metal L-PBF lessens the need and distance of transportation thereby reduces transport-related emissions. Metal L-PBF reduces the need for spare parts and inventory with on-demand manufacturing which reduces cost and waste. Again, this thesis showed that L-PBF allows optimised designs with intricate internal and outer geometries to be manufactured in resource-efficient and cost-efficient manner. The results of the experimental study on the process parameters showed that optimising process parameter values directly enhances part qualify and reduces defects. The potential to control the process efficiency is one way by which raw material and high energy utilisations can be improved in metal L-PBF. The results of the LCC studies identified key drivers to cost and how they could be optimised in metal L-PBF using digital simulations and DfAM rules, referred to in this thesis as LCC-driven DfAM. The simulation-driven DfAM study showed how digital tools allow for the acceleration of sustainable products via product optimisation while maintaining cost-effectiveness and waste reductions. The results of the review on metal L-PBF manufactured separation units for electrochemical application showed that the method made it possible to create intricate structures such as lattices and conformal flow channels. This benefit offered the possibility of improved functional multi-metal separation units.
The main outcome of this thesis is the first-ever integrated LCC-driven DfAM model that can be used as a decision-making tool to the adoption of metal AM/L-PBF towards high performing, resource efficiency, cost-efficient components. The model can be used in industries to identify best practices that can help create optimised metal components without adding to costs. The model highlights the phases in which the greatest cost reductions are achievable from the design, manufacturing, use and EOL phases. The thesis shows that metal AM/L-PBF is constantly developing. These include innovations and new solutions to improve productivity, resource efficiency as well as the reduction of waste and emissions. Metal AM/L-PBF can enhance resource consumption, reduce costs, drive innovations in sustainable business practice and offer means of competitiveness. The main conclusion of this thesis is that metal L-PBF offers means to optimised product design, possibilities of reducing raw material usage, operational costs, waste and emissions.
Plans to experimentally compare the performance of L-PBF and CNC-machining manufactured components and the effect of build platform utilisation on specific energy consumption (SEC) in L-PBF did not materialise due to a lack of funds. The thesis identified that ongoing sustainability studies of metal AM/L-PBF do not include the entire aspects of sustainability and value chain. For example, the social aspects, experimental energy and raw material consumptions during the powder production phases. Further studies could include the limitations of this thesis and provide comprehensive continuity of the subject to overcome some of the identified gaps in literature and process limitations.
Keywords: Additive manufacturing, design for additive manufacturing, (DfAM), circular economy, (CE), laser-based powder bed fusion, (L-PBF), life cycle cost, LCC-driven, metal AM, metal L-PBF, simulation-driven DfAM, sustainability.
The transition towards cloud computing is transforming the way software solutions are designed and developed, priced and packaged, as well as delivered and maintained. Software companies are moving away from the traditional model of selling software solutions as off-the-shelf software products to providing Software-as-a-Service (SaaS) solutions. This transition unlocks unique opportunities for reconsidering product and marketing strategies, including pricing. The fundamental changes that affect pricing are the adoption of value-based and subscription-based approaches. Both of these create challenges for product and pricing managers, and only a handful of software companies succeed in taking advantage of all the opportunities the SaaS model offers.
This dissertation explores how software companies establish and implement the pricing of their SaaS solutions. This research aims to reveal the nature of pricing for SaaS solutions and empower SaaS companies with the knowledge required to advance their pricing processes and practices. The dissertation consists of four studies that employed a portfolio of research methods, including a simulation modeling, a multivocal literature review, a multiple case study research, and an industry survey.
The contribution of this dissertation is threefold. First, the dissertation bridges the gap between scholars and practitioners and proposes a typology of SaaS pricing aspects, affecting factors, frameworks, and structures. It updates the knowledge and expertise in the SaaS pricing area of research and practice. Second, the dissertation reveals how SaaS companies price their solutions by evaluating industrial practices and exploring the reasons behind them. This allows proposing a typology of SaaS pricing practices. Thirdly, an integrated simulation model of SaaS pricing is put forward to analyze dynamic pricing mechanisms. This model serves as an example of how different pricing mechanisms and factors can be explored to improve decision-making in SaaS pricing.
Ultimately, this research should contribute to a reduction in the market failure risk for technologically advanced SaaS solutions. The result of the research indicates a lack of silver-bullet solutions for pricing, meaning that it should not be left to intuition and performed in an ad hoc manner. On the contrary, pricing requires efficient collaboration between different business units and a comprehensive analysis that incorporates experimentation, data analytics, and modeling.
Keywords: Software-as-a-Service, SaaS, pricing, multivocal literature study, case study, SaaS product management
The ability of the magnetic shape memory (MSM) alloy Ni-Mn-Ga to exhibit large magnetic-field-induced strain (MFIS) of 6-12% makes it a promising actuation material for small devices in which traditional mechanisms and piezoelectric materials are impractical. As the grain boundaries in fine-grained polycrystalline material significantly hinder twin boundary motion, large MFIS is almost exclusively obtained in oriented single crystals. However, a moderate MFIS of ~1-4% can be obtained in bulk polycrystalline Ni-Mn-Ga after a sufficient reduction of the grain boundary constraints and the introduction of a strong crystallographic texture. The drawbacks of conventionally manufactured single crystals and polycrystalline Ni-Mn-Ga, e.g. low geometric freedom and high production costs, currently limit the development of novel functional MSM devices. Therefore, additive manufacturing (AM) is attracting increasing attention as a promising method for manufacturing polycrystalline Ni-Mn-Ga, especially as it allows realization of complex geometries or device structures.
Here, a laser powder bed fusion (L-PBF) AM process and a subsequent heat-treatment process were developed for the manufacture of coarse-grained polycrystalline Ni-Mn-Ga samples. It is shown that the chemical composition and resulting MSM-related properties of the L-PBF-built Ni-Mn-Ga can be precisely changed in-situ by adjusting the applied L-PBF process parameters to control the selective evaporation of Mn. A repeatable and fully reversible MFIS of 5.8% is demonstrated for a single crystalline grain of an L-PBFbuilt Ni-Mn-Ga exhibiting a five-layered modulated martensitic structure at ambient temperature. The obtained MFIS is two orders of magnitude larger than the 0.01% MFIS previously reported for additively manufactured Ni-Mn-Ga and is similar to that of conventional single crystals exhibiting the same crystal structure.
The results indicate that L-PBF can be used to manufacture functional polycrystalline Ni-Mn-Ga, facilitating a new generation of fast and simple digital components with integrated MSM alloy sections that can be actuated by an external magnetic field. Practically, the reported results will permit the exploration of polycrystalline-MSM-based devices with a geometric freedom that has thus far been impossible with conventional manufacturing methods.
Keywords: additive manufacturing, 4D printing, laser powder bed fusion, Ni-Mn-Ga,
magnetic shape memory, magnetic-field-induced strain, twinning
This open access book is among the first cross-disciplinary works about Manufacturing 4.0. It includes chapters about the technical, the economic, and the social aspects of this important phenomenon. Together the material presented allows the reader to develop a holistic picture of where the manufacturing industry and the parts of the society that depend on it may be going in the future. Manufacturing 4.0 is not only a technical change, nor is it a purely technically driven change, but it is a societal change that has the potential to disrupt the way societies are constructed both in the positive and in the negative.
This book will be of interest to scholars researching manufacturing, technological innovation, innovation management and industry 4.0.
We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delaying rejection. The ergodicity of the approach is proved, and the efficiency of the combination is demonstrated with various test examples. We present situations where the combination outperforms the original methods: the adaptation clearly enhances the efficiency of the delayed rejection algorithm in cases where good candidates for the proposal distributions are not available. Similarly, the delayed rejection provides a systematic remedy for cases where the adaptation has difficulties to get started.
Design of support-mechanisms is an important component of renewable energy policy. In order to be able to choose desirable designs one must have a good understanding of the most likely outcomes from different policy designs – this calls for proper before-implementation policy analysis and especially for analysis the results of which are intuitively understandable for the decisionmakers. We propose a simple process, based on the fuzzy pay-off method, for the purpose of analyzing renewable energy support designs in the context of auction-based support mechanism implementation. A numerical case from Finland is used to illustrate the proposed process. The results show that the process is relatively simple to use and able to produce intuitively understandable relevant information for design selection.
A review of economic geography studies on renewable energy showed a lack of the investors’ perspective in such an analysis that is crucial for both a single investment planning and policy development. This paper introduces a framework for a cross-regional analysis of renewable energy investment attractiveness and illustrates its use on the case of Russia. The attractiveness of each Russian region is analyzed based on four main variables that are used in the construction of an attractiveness indicator. In addition, the indicator takes into consideration the effect of the different renewable energy investment support mechanisms presented in the country. The results allow the comparative analysis of different regions in terms of renewable energy investment attractiveness. The graphical representation of the results enables intuitive understanding and facilitates decision making.
This paper is the first documented research effort on how simple meta-models can be used in simulation-based investment analysis. Modern computers allow the construction and simulation of near real-world emulating models, often referred to as “digital twins”, that offer requisite variety to real world phenomena, such as an industrial investment. These models can be extremely complex and computationally demanding which reduces the scope of their practical applications. This is where meta-models can help. Meta-models are simple black-box models that are fitted with the input-output -combinations from more complex models to be able to approximate complex model behavior. As the simple meta-models are very fast to solve they may be used to explore much larger solution spaces with considerably higher speed and less computing power needed than the original models. We demonstrate how the meta-modeling approach can be used in the context of metal mining investment analysis that is originally conducted with a dynamic system model constructed based on a real-world metal mining investment. We show how two simple meta-models, a linear regression model and a regression-tree model, can be used in gaining insight about a suitable financing-mix for the said metal mining investment.
This paper introduces laser additive manufacturing as a new method for the fabrication of micro fuel cells: The method opens up the capability of ultrafast prototyping, as the whole device can be produced at once, starting from a digital 3D model. In fact, many different devices can be produced at once, which is useful for the comparison of competing designs. The micro fuel cells are made of stainless steel, so they are very robust, thermally and chemically inert and long-lasting. This enables the researcher to perform a large number of experiments on the same cell without physical or chemical degradation. To demonstrate the validity of our method, we have produced three versions of a micro fuel cell with square pillar flowfield. All three have produced high current and power density, with maximum values of 1.2 A cm−2 for the current and 238 mW cm−2 for power.