Organisations comprise human and technical systems that typically perform a variety of business, engineering and production roles. Human systems comprise individuals, people groups and teams that work systematically to conceive, implement, develop and manage the purposes of any enterprise in response to customer requirements. Recently attention has been paid to modelling aspects of people working within production systems, with a view to improving: production performance, effective resource allocation and optimum resource management. In the research reported, graphical and computer executable models of people have been conceived and used in support of human systems engineering. The approach taken has been to systematically decompose and represent processes so that elemental production and management activities can be modelled as explicit descriptions of roles that human systems can occupy as role holders. First of all, a preliminary modelling method (MM1) was proposed for modelling human systems in support of engineering enterprise; then MM1 was implemented and tested in a case study company 1. Based on findings of this exploratory research study an improved modelling method (MM2) was conceived and instrumented. Here characterising customer related product dynamic impacts extended MM1 modelling concepts and methods and related work system changes. MM2 was then tested in case study company 2 to observe dynamic behaviours of selected system models derived from actual company knowledge and data. Case study 2 findings enabled MM2 to be further improved leading to MM3. MM3 improvements stem from the incorporation of so-called DPU (Dynamic Producer Unit) concepts, related to the modelling of human and technical resource system components . Case study 4 models a human system for targeted users i.e. production managers etc to facilitate analysis of human configuration and also cost modelling. Modelling approaches MM2, MM3 and also Case Study 4 add to knowledge about ways of facilitating quantitative analysis and comparison between different human system configurations. These new modelling methods allow resource system behaviours to be matched to specific, explicitly defined, process-oriented requirements drawn from manufacturing workplaces currently operating in general engineering, commercial furniture and white goods industry sectors.

Business environments, in which any modern Manufacturing Enterprise (ME) operates, have grown significantly in complexity and are changing faster than ever before. It follows that designing a flexible manufacturing system to achieve a set of strategic objectives involves making a series of complex decisions over time. Therefore manufacturing industry needs improved knowledge about likely impacts of making different types of change in MEs and improved modelling approaches that are capable of providing a systematic way of modelling change impacts in complex business processes; prior to risky and costly change implementation projects. An ability to simulate the execution of process instances is also needed to control, animate and monitor simulated flows of multiple products through business processes; and thereby to assess impacts of dynamic distributions and assignments of multiple resource types during any given time period. Further more this kind of modelling capability needs to be integrated into a single modelling framework so as to improve its flexibility and change coordination. Such a modelling capability and framework should help MEs to achieve successfully business process re-engineering, continuous performance development and enterprise re-design. This thesis reports on the development of new modelling constructs and their innovative application when used together with multiple existing modelling approaches. This enables human and technical resource systems to be described, specified and modelled coherently and explicitly. In turn this has been shown to improve the design of flexible, configurable and re-usable manufacturing resource systems, capable of supporting decision making in agile manufacturing systems. A newly conceived and developed Role-Based Modelling Methodology (R-BMM) was proposed during this research study. Also the R-BMM was implemented and tested by using it together with three existing modelling approaches namely (1) extended Enterprise Modelling, (2) dynamic Causal Loop Diagramming and (3) Discrete Event Simulation Modelling (via software PlantSimulation ®). Thereby these three distinct modelling techniques were deployed in a new and coherent way. The new R-BMM approach to modelling manufacturing systems was designed to facilitate: (1) Graphical Representation (2) Explicit Specification and (3) Implementation Description of Resource systems. Essentially the approach enables a match between suitable human and technical resource systems and well defined models of processes and workflows. Enterprise Modelling is used to explicitly define functional and flexibility competencies that need to be possessed by suitable role holders. Causal Loop Diagramming is used to reason about dependencies between different role attributes. The approach was targeted at the design and application of simulation models that enable relative performance comparisons (such as work throughput, lead-time and process costs) to be made and to show how performance is affected by different role decompositions and resourcing policies. The different modelling techniques are deployed via a stepwise application of the R-BMM approach. Two main case studies were carried out to facilitate methodology testing and methodology development. The chosen case company possessed manufacturing characteristics required to facilitate testing and development; in terms of significant complexity and change with respect to its products and their needed processing structures and resource systems. The first case study was mainly designed to illustrate an application, and benefits arising from application, of the new modelling approach. This provided both qualitative and quantitative results analysis and evaluation. Then with a view to reflecting on modelling methodology testing and to address a wider scope manufacturing problem, the second case study was designed and applied at a different level of abstraction, to further test and verify the suitability and re-usability of the methodology. Through conceiving the new R-BMM approach, to create, analyse and assess the utility of sets of models, this research has proposed and tested enhancements to current means of realising reconfigurable and flexible production systems.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

Business Processes (BP) and Information Technology (IT) are two areas that work very closely in helping organisations to keep or retain competitive advantage. Therefore, design in these areas should consider the advantages provided by, and the limitations that each of these domains imposes on each other. BP design tries to ensure that IT specifications are considered during the design of BP. Similarly, Information Systems (IS) design attempts to capture organisational needs, known as IS functional and Non-Functional Requirements (NFR), in order to meet the organisational goals. Despite this, BP and IT modelling techniques barely depict the way IT may affect BP performance or vice versa. For example, Business Process Simulation (BPS) is one of the modelling techniques that has been increasingly used to support process design. The performance measurements obtained from BPS models, though, are obtained considering only organisational issues, and thus cannot be used to assess the impact that IT may have on process performance. Similarly, IT modelling techniques do not provide IS performance measurements, and hence cannot depict the way IS may improve BP performance. The relationship between BP and IT can be alternatively described in terms of the relationships between BP, IS and Computer Networks (CN). By looking at the parameters that govern these relationships a simulation framework was developed, namely ASSESS-IT, that develops simulation models that provide performance measurements of BP, IS and CN, and thus can reflect the impact that IT (IS and CN) may have on BP performance. This research uses a case study to test the proposed framework (theory testing), to understand the way BP, IS, and CN domains interact (discovery), and to propose alternative theories to solve the problems found (theory building). The experimentation with the ASSESS-IT framework suggests that in order to portray the impact that IT may have on BP, analysts in these domains should first identify those performance specifications that describe how well the IS delivers its functionality (also known as non-functional requirements). It was found that when the IS does not depend on determined response time, the relationships between BP, IS and CN can be assessed using only the relationship between BP and IS. An alternative simulation framework, namely BPISS, is proposed to produce BPS models that provide performance measurements of BP and IS. Thus, BP and IT analysts can investigate the impact that a given IS design may have on BP performance, and identify a better BP and IS solution.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies.

Buildings are responsible for a significant portion of energy consumption worldwide. Intelligent buildings have been devised as a potential solution, where energy consumption and building use are harmonised. At the heart of the intelligent building is the building energy management system (BEMS), the central platform which manages and coordinates all the building monitoring and control subsystems, such as heating and lighting loads. There is often a disconnect between the BEMS and the building it is installed in, leading to inefficient operation, due to incongruous commissioning of sensors and control systems. In these cases, the BEMS has a lack of knowledge of the building form and function, requiring further complex optimisation, to facilitate efficient all year round operation. Flawed BEMS configurations can then lead to ‘sick buildings’. Recently, building energy performance simulation (BEPS) has been viewed as a conceptual solution to assist in efficient building control. Building energy simulation models offer a virtual environment to test many scenarios of BEMS operation strategies and the ability to quickly evaluate their effects on energy consumption and occupant comfort. Challenges include having an accurate building model, but recent advances in building information modelling (BIM) offer the chance to leverage existing building data, which can be translated into a form understood by the building simulator. This study will address these challenges, by developing and integrating a BEMS, with a BIM for BEPS assisted predictive control, and assessing the outcome and potential of the integration.

Context. Due to increase in demand for RORO shipping services,the RORO terminal gate system need to handle more number of vehicles for every RORO vessel departure. Therefore, various congestion problems can occur; so, to address all possible congestion related problemsat RORO terminal, terminal gate systems are implemented with advanced technologies and updated to full or partial functioning automated gate systems. Objectives. In this research study considering the future increase in demand for wheeled cargo shipping, we attempt to propose a solution for reducing congestion and investigating optimal positions for each automated gate system service at RORO port terminal. Methods. In this Master thesis, as part of qualitative study we conduct a literature review and case study to know about the existing related work on this research problem and know about the real world system operation and behaviour of a RORO terminal gate system.Later, applying the adequate knowledge acquired from above mentioned qualitative studies, we perform a discrete event simulation experiment using Anylogic® professional 7.02 simulation software to address defined research objectives. Results. Considering the peak and low periods of present and future estimated demand volumes as different scenarios,various simulation experiment results are generated for different key performance indicators. The result values of these key performance indicators address various research objectives. Conclusions. This research study finally concludes that, the average queue length values at each automated gate system service implicates optimal position for each service and directly address the congestion problem. We also conclude that in every estimated increase in vehicles attending the RORO terminal, assigning optimal arrival time windows for respective vehicle types minimizes the congestion problem at automated gate system.

The replacement of the conventional field effect transistor (FET) by single electron transistors (SET) would lead to high energy savings and to devices with significantly longer battery life. There are many production approaches, but mostly for specimens in the laboratory. Most of them suffer from the fact that they either only work at cryogenic temperatures, have a low production yield or are not reproducible and each unit works in a unique way. A room temperature (RT) operating SET can be configured by inserting a small (few nm diameters) Si-Nanocrystal (NC) into a thin (<10 nm) SiO2 interlayer in Si. Industrial production has so far been excluded due to a lack of manufacturing processes. Classical technologies such as lithography fail to produce structures in this small scale. Even electron beam lithography or extreme ultraviolet lithography are far from being able to realize these structures in mass production. However, self-organization processes enable structures to be produced in any order of magnitude down to atomic sizes. Earlier studies realized similar systems using a layer of Si-NCs to fabricate a non-volatile memory by using the charge of the NCs for data storage. Based on this, it is very promising to use it for the realization of the SET. The self-organization depends only on the start configuration of the system and the boundary conditions during the process. These macroscopic conditions control the self-formed structures. In this work, ion beam irradiation is used to form the initial configuration, and thermal annealing is used to drive self-organization. A Si/SiO2/Si stack is irradiated and transforms the stack into Si/SiOx/Si by ion beam mixing (IBM) of the two Si/SiO2 interfaces. The oxide becomes metastable and the subsequent thermal treatment induces selforganization, which might leave a single Si-NC in the SiO2 layer for a sufficiently small mixing volume. The transformation of the planar SiOx layer (restriction only in one dimension) into a small SiOx volume (restriction in all three dimensions) is done by etching nanopillars with a diameter of less than 10nm. This forms a small SiOx plate embedded between two Si layers. The challenge is to control the self-organization process. In this work, simulation was used to investigate dependencies and parameter optimization. The ion mixing simulations were performed using binary collision approximation (BCA), followed by kinetic Monte Carlo (KMC) simulations of the decomposition process, which gave good qualitative agreement with the structures observed in related experiments. Quantitatively, however, the BCA simulation seemed to overestimate the mixing effect. This is due to the neglect of the positive entropy of the Si-SiO2 system mixing, i.e. the immiscibility counteracts the collisional mixing. The influence of this mechanism increases with increasing ion fluence. Compared to the combined BCA and KMC simulations, a larger ion mixing fluence has to be applied experimentally to obtain the predicted nanocluster morphology. To model the ion beam mixing of the Si/SiO2 interface, phase field methods have been applied to describe the influence of chemical effects during the irradiation of buried SiO2 layers by 60 keV Si+ ions at RT and thermal annealing at 1050°C. The ballistic collisional mixing was modeled by an approach using Fick’s diffusion equation, and the chemical effects and the annealing were described by the Cahn Hilliard equation. By that, it is now possible to predict composition profiles of Si/SiO2 interfaces during irradiation. The results are in good agreement with the experiment and are used for the predictions of the NCs formation in the nanopillar. For the thermal treatment model extensions were also necessary. The KMC simulations of Si-SiO2 systems in the past were based on normed time and temperature, so that the diffusion velocity of the components was not considered. However, the diffusion of Si in SiO2 and SiO2 in Si differs by several orders of magnitude. This cannot be neglected in the thermal treatment of the Si/SiO2 interface, because the processes that differ in speed in this order of magnitude are only a few nanometers apart. The KMC method was extended to include the different diffusion coefficients of the Si-SiO2 system. This allows to extensively investigate the influence of the diffusion. The phase diagram over temperature and composition was examined regarding decomposition (nucleation as well as spinodal decomposition) and growing of NCs. Using the methods and the knowledge gained about the system, basic simulations for the individual NC formation in the nanopillar were carried out. The influence of temperature, diameter, and radiation fluence was discussed in detail on the basis of simulation results.

Powder metallurgy is a branch of metal forming technology where metal powders are used to manufacture parts and components. It is a flexible and economical technique for manufacturing complicated shapes. This present work focuses on press and sinter technology and forms a part of Höganäs’s efforts of modelling hardenability through quenching. It aims to reduce the number of experimental trials for optimising heat treatment. Hardenability is a measure of how much martensite can be formed during heat treatment, thereby making steels hard, tough and impart strength. The presence of alloying elements like carbon, manganese, chromium, molybdenum, and nickel affects the hardenability of the steel and improves performance like fatigue strength and corrosion resistance. These elements influence the critical cooling rate necessary to form martensite during heat treatment. Component geometry also influences hardenability. Depending on the surface area available to cool, and volume of component, cooling rates may locally be different thereby resulting in an inhomogeneous structure. The work focuses particularly on two grades of powders manufactured by Höganäs AB - Astaloy® CrA and Astaloy® CrS which are evaluated for their hardenability. The aim of this work is to take cooling conditions observed in the actual furnace, use them to predict the amount of martensite present and the martensite start temperature and then compare it with experimental results thereby linking experiment to simulations. For the experimental part, dilatometry was used. Quenching data is obtained from the furnace along with heat capacity of the component and are used as input in Abaqus, which gives us the cooling rates for the component in the furnace. This data is then utilised as an input to dilatometry, where the samples are representative of sections of component. After dilatometry, vital information like martensite start temperature is recorded and metallography is performed, where phase fraction is obtained. Hardness measurements are also performed to verify the phases present. Simulation tools like JMatPro and Thermo-Calc are employed to obtain data for correlation. An extensive study on the difference between them are also studied and presented. The data from simulation and actual experiment is compared and, Ms evaluated from JMatPro and Thermo-Calc for CrA shows a deviation of 12°C. For CrS samples, a higher deviation is observed, with JMatPro showing deviation of 44°C and Thermo-Calc, 52°C in respect to the measured values. For CrA, we observe a fully martensitic structure for the higher carbon samples, including ones alloyed with Ni. For samples with lower carbon, metallographic investigation results in an unclear picture as to if the structure observed is bainite or martensite. CrS samples are mostly martensitic with some bainite present. CrS samples alloyed with Ni and Cu show the least amount of bainite present. The phase fractions predicted by JMatPro show good agreement with results from metallography. Data from microhardness confirms the presence of phases present. Samples with low carbon are softest but show a great improvement in hardness when alloyed. Overall, simulations and actual experimental values are seen to be in good agreement, thereby establishing a strong foundation for future work, where actual components can be evaluated. Quenching conditions observed in the furnace are validated through this work. / Pulvermetallurgi är en gren av metallformningsteknik där metallpulver används för att tillverka delar och komponenter. Det är en flexibel och ekonomisk teknik för att tillverka komplicerade former. Detta nuvarande arbete fokuserar på press- och sinterteknik och är en del av Höganäs arbete med att modellera härdbarhet genom härdning. Det syftar till att minska antalet experimentella försök för att optimera värmebehandlingen. Härdbarhet är ett mått på hur mycket martensit som kan bildas vid värmebehandling, vilket gör stålen hårda, sega och ger styrka. Närvaron av legeringselement som kol, mangan, krom, molybden och nickel påverkar stålets härdbarhet och förbättrar prestanda som utmattningshållfasthet och korrosionsbeständighet. Dessa element påverkar den kritiska kylningshastighet som krävs för att bilda martensit under värmebehandling. Komponentgeometrin påverkar också härdbarheten. Beroende på den yta som är tillgänglig för kylning och volymen av komponenten, kan kylningshastigheterna lokalt vara olika, vilket resulterar i en inhomogen struktur. Arbetet fokuserar särskilt på två kvaliteter av pulver tillverkade av Höganäs AB - Astaloy® CrA och Astaloy® CrS som utvärderas för sin härdbarhet. Syftet med detta arbete är att ta kylförhållanden som observerats i den faktiska ugnen, använda dem för att förutsäga mängden närvarande martensit och martensitens starttemperatur och sedan jämföra den med experimentella resultat och därigenom koppla experiment till simuleringar. För den experimentella delen användes dilatometry. Släckningsdata erhålls från ugnen tillsammans med värmekapaciteten hos komponenten och används som indata i Abaqus, vilket ger oss kylhastigheten för komponenten i ugnen. Dessa data används sedan som indata till dilatometry, där proverna är representativa för sektioner av komponenten. Efter dilatometri registreras viktig information som martensitstarttemperatur och metallografi utförs, där fasfraktion erhålls. Hårdhetsmätningar utförs också för att verifiera de närvarande faserna. Simuleringsverktyg som JMatPro och Thermo-Calc används för att få data för korrelation. En omfattande studie om skillnaden mellan dem studeras och presenteras också. Data från simulering och faktiska experiment jämförs och Ms utvärderade från JMatPro och Thermo-Calc för CrA visar en avvikelse på 12°C. För CrS-prover observeras en högre avvikelse, där JMatPro visar en avvikelse på 44°C och Thermo-Calc, 52°C i förhållande till de uppmätta värdena. För CrA observerar vi en helt martensitisk struktur för de högre kolproverna, inklusive de som legerats med Ni. För prover med lägre kolhalt resulterar metallografisk undersökning i en oklar bild av om den observerade strukturen är bainit eller martensit. CrS-prover är mestadels martensitiska med viss bainit närvarande. CrS-prover legerade med Ni och Cu visar den minsta mängden bainit som finns närvarande. Fasfraktionerna som förutspåtts av JMatPro visar god överensstämmelse med resultaten från metallografi. Data från mikrohårdhet bekräftar närvaron av faser. Prover med låg kolhalt är mjukast men visar en stor förbättring i hårdhet när de är legerade. Sammantaget bedöms simuleringar och faktiska experimentella värden stämma överens, vilket skapar en stark grund för framtida arbete, där faktiska komponenter kan utvärderas. Släckningsförhållanden som observerats i ugnen valideras genom detta arbete.