Showing papers on "Process modeling published in 2018"

A literature review of powder-based electron beam melting focusing on numerical simulations

Abstract: The Electron Beam Melting (EBM) process is an additive manufacturing process in which an electron beam melts metallic powders to obtain the geometry of a specific part. The use of an electron beam in the AM field is relatively recent. Numerous applications have already been made in the aerospace and medical fields, in which the EBM process is used to produce complex parts, made of an excellent quality material, for which other technologies would be expensive or difficult to apply. Because of the growing interest of industry in this technology, the research community has been dedicating a great deal of effort to making the EBM process more reliable. The modelling of the EBM process is considered of utmost importance as it could help to reduce the process optimisation time, compared with the trial and error approach, which is currently the most widely used method. From this point of view, the aim of this paper has been to provide a literature review of numerical simulation models of the EBM process. The various studies on numerical modelling are presented in detail. These studies are mainly classified according to the level of approximation introduced into the modelling methodology. The simulations have first been categorised according to the powder modelling approach that has been adopted (i.e. mesoscopic or FE approach). The studies have then been categorised, as far as FE-based simulations are concerned, as either uncoupled or coupled modelling approaches. All the current approaches have been compared, and how the researchers have modelled the EBM process has been highlighted, considering the assumptions that have been made, the modelling of the material properties, the material state change, and the heat source. Moreover, the adopted validation approaches and the results have been described in order to point out any important achievements. Deviations between numerical and experimental results have been discussed as well as the current level of development of the simulation of the EBM process.

Predictive Monitoring of Business Processes: A Survey

Abstract: Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.

Linearity Evaluation and Variable Subset Partition Based Hierarchical Process Modeling and Monitoring

Abstract: Complex industrial processes may be formulated with hybrid correlations, indicating that linear and nonlinear relationships simultaneously exist among process variables, which brings great challenges for process monitoring. However, previous work did not consider the hybrid correlations and treated all the variables as a single subject in which single linear or nonlinear analysis method was employed based on prior process knowledge or some evaluation results, which may degrade the model accuracy and monitoring performance. Therefore, for complex processes with hybrid correlations, this paper proposes a linearity evaluation and variable subset partition based hierarchical modeling and monitoring method. First, linear variable subsets are separated from nonlinear subsets through an iterative variable correlation evaluation procedure. Second, hierarchical models are developed to capture linear patterns and nonlinear patterns in different levels. Third, a hierarchical monitoring strategy is proposed to monitor linear feature and nonlinear feature separately. By separating and modeling different types of variable correlations, the proposed method can explore more accurate process characteristics and thus improve the fault detection ability. Numerical examples and industrial applications are presented to illustrate its efficiency.

Static and Dynamic Process Change

Abstract: Approaches for modifying processes both at build time and at run time are commonly referred to as process change , which play an increasingly important role in the enterprise today, where more than ever before, changing requirements must be rapidly accommodated. Over the years, approaches for supporting process change have received much attention from the research community. In spite of that, no comprehensive survey of this important subject exits. To draw a clear picture that analyzes the status of research in this area, in this paper, we conduct a systematic literature review on process change. The resulting survey sheds light on how to classify approaches for process change, determines what the principal research questions and challenges are, and identifies several research directions for further study.

Improving process discovery results by filtering outliers using conditional behavioural probabilities

Abstract: Process discovery, one of the key challenges in process mining, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log conform to correct execution of the process, and hence, incorporate all behaviour in their resulting process model. However, in real event logs, noise and irrelevant infrequent behaviour are often present. Incorporating such behaviour results in complex, incomprehensible process models concealing the correct and/or relevant behaviour of the underlying process. In this paper, we propose a novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities. The method has been implemented in both the ProM toolkit and the RapidProM framework. We evaluate our approach using real and synthetic event data. The results show that the proposed method accurately removes irrelevant behaviour and, indeed, improves process discovery results.

Modeling process-structure-property relationships for additive manufacturing

Abstract: This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process-structure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high-efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Discovering workflow nets using integer linear programming

Abstract: Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging process mining task. State-of-the-art process discovery algorithms only discover local control flow patterns and are unable to discover complex, non-local patterns. Region theory based techniques, i.e. an established class of process discovery techniques, do allow for discovering such patterns. However, applying region theory directly results in complex, overfitting models, which is less desirable. Moreover, region theory does not cope with guarantees provided by state-of-the-art process discovery algorithms, both w.r.t. structural and behavioural properties of the discovered process models. In this paper we present an ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets. Moreover, we devise a filtering algorithm, based on the internal working of the ILP-formulation, that is able to cope with the presence of infrequent, exceptional behaviour. We have extensively evaluated the technique using different event logs with different levels of exceptional behaviour. Our experiments show that the presented approach allows us to leverage the inherent shortcomings of existing region-based approaches. The techniques presented are implemented and readily available in the HybridILPMiner package in the open-source process mining tool-kits ProM ( http://promtools.org ) and RapidProM ( http://rapidprom.org ).

Business process management – at the crossroads

Abstract: Business process management (BPM) has attracted much focus throughout the years, yet there have been calls questioning the future of BPM. The purpose of this paper is to explore the current state of the field through a dynamic literature review and identify the main challenges for its future development.,A dynamic co-citation network analysis identifies the “evolution” of knowledge of BPM and the most influential works. The results present the developed BPM subthemes in the form of clusters.,The focus within the field has shifted from facilitating wide-ranging business performance improvements to creating introverted optimizations within a particular BPM subgroup. The BPM field has thus experienced strong fragmentation throughout the years and has accrued into self-fueling subareas of BPM research such as business process modeling and workflow management. Those subareas often neglect related disciplines in other management, process modeling and organizational improvement fields.,The study is limited by the initial keyword choice of the authors. The subsequent co-citation analysis ameliorates the subjectivity since it produces a data set and contributions based on references.,A new combination of historical development and the state-of-the-art of the BPM field, by employing a co-citation and cluster analysis. This dynamic literature review presents the current state of the theoretical core and attempts to identify the crossroads that BPM has reached. The study can be replicated in the future to track the changes in the field.

Complete and Interpretable Conformance Checking of Business Processes

Abstract: This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline.

A dynamic dual process model of risky decision making.

Abstract: Many phenomena in judgment and decision making are often attributed to the interaction of 2 systems of reasoning. Although these so-called dual process theories can explain many types of behavior, they are rarely formalized as mathematical or computational models. Rather, dual process models are typically verbal theories, which are difficult to conclusively evaluate or test. In the cases in which formal (i.e., mathematical) dual process models have been proposed, they have not been quantitatively fit to experimental data and are often silent when it comes to the timing of the 2 systems. In the current article, we present a dynamic dual process model framework of risky decision making that provides an account of the timing and interaction of the 2 systems and can explain both choice and response-time data. We outline several predictions of the model, including how changes in the timing of the 2 systems as well as time pressure can influence behavior. The framework also allows us to explore different assumptions about how preferences are constructed by the 2 systems as well as the dynamic interaction of the 2 systems. In particular, we examine 3 different possible functional forms of the 2 systems and 2 possible ways the systems can interact (simultaneously or serially). We compare these dual process models with 2 single process models using risky decision making data from Guo, Trueblood, and Diederich (2017). Using this data, we find that 1 of the dual process models significantly outperforms the other models in accounting for both choices and response times. (PsycINFO Database Record

Augmenting processes with decision intelligence: Principles for integrated modelling

Abstract: Until recently decisions were mostly modelled within the process. Such an approach was shown to impair the maintainability, scalability, and flexibility of both processes and decisions. Lately, literature is moving towards a separation of concerns between the process and decision model. Most notably, the introduction of the Decision Model and Notation (DMN) standard provides a suitable solution for filling the void of decision representation. This raises the question whether decisions and processes can easily be separated and consistently integrated. We introduce an integrated way of modelling the process, while providing a decision model which encompasses the process in its entirety, rather than focusing on local decision points only. Specifically, this paper contributes formal definitions for decision models and for the integration of processes and decisions. Additionally, inconsistencies between process and decision models are identified and we remedy those inconsistencies by establishing FivePrinciples for integrated Process and Decision Modelling (5PDM). The principles are subsequently illustrated and validated on a case of a Belgian accounting company. We address the question of consistent integration of process and decision models.We provide a formalisation on which the integration is based.We list possible inconsistencies between process and decision models.We provide modelling guidelines for consistent integration.Our guidelines are applied and illustrated on a real life case.

From Crystal to Adsorption Column: Challenges in Multiscale Computational Screening of Materials for Adsorption Separation Processes

Abstract: Multiscale material screening strategies combine molecular simulations and process modeling to identify the best performing adsorbents for a particular application, such as carbon capture. The idea to go from the properties of a single crystal to the prediction of material performance in a real process is both powerful and appealing; however, it is yet to be established how to implement it consistently. In this article, we focus on the challenges associated with the interface between molecular and process levels of description. We explore how predictions of the material performance in the actual process depend on the accuracy of molecular simulations, on the procedures to feed the equilibrium adsorption data into the process simulator, and on the structural characteristics of the pellets, which are not available from molecular simulations and should be treated as optimization parameters. The presented analysis paves the way for more consistent and robust multiscale material screening strategies.

Event stream-based process discovery using abstract representations

Abstract: The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

Automated discovery of structured process models from event logs: The discover-and-structure approach

Abstract: This article tackles the problem of discovering a process model from an event log recording the execution of tasks in a business process. Previous approaches to this reverse-engineering problem strike different tradeoffs between the accuracy of the discovered models and their structural complexity. With respect to the latter property, empirical studies have demonstrated that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several methods for automated process model discovery generate block-structured models only. These methods however intertwine the objective of producing accurate models with that of ensuring their structuredness, and often sacrifice the former in favour of the latter. In this paper we propose an alternative approach that separates these concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers accurate but oftentimes unstructured (and even unsound) process models, and then we transform the resulting process model into a structured (and sound) one. An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.

Process discovery from event data: Relating models and logs through abstractions

Abstract: Event data are collected in logistics, manufacturing, finance, healthcare, customer relationship management, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, etc. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control-flow model from the event data. This is very challenging, but in recent years many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad-hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block-structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also used to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps to broaden the view and facilitates the creation of new discovery approaches. ∗Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany

Toward a Distinct and Quantitative Validation Method for Predictive Process Modelling—On the Example of Solid-Liquid Extraction Processes of Complex Plant Extracts

Abstract: Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data to any desired scale, if characteristic numbers like e.g., Reynolds, Peclet, Sherwood, Schmidt remain constant and fluid-dynamics of both scales are known and can be described by the model. A useful model has to be accurate and therefore match the experimental data at different scales and combinations of process and operating parameters. Besides accuracy as one quality attribute for the modelling depth, model precision also has to be evaluated. Model precision is considered as the combination of modelling depth and the influence of experimental errors in model parameter determination on the simulation results. A model is considered appropriate if the deviation of the simulation results is in the same order of magnitude as the reproducibility of the experimental data to be substituted by the simulation. Especially in natural product extraction, the accuracy of the modelling approach can be shown through various studies including different feedstocks and scales, as well as process and operating parameters. Therefore, a statistics-based quantitative method for the assessment of model precision is derived and discussed in detail in this paper to complete the process engineering toolbox. Therefore a systematic workflow including decision criteria is provided.

Multisensor Data Fusion for Additive Manufacturing Process Control

Abstract: Achieving cutting-edge mechanical properties of metal parts realized by additive manufacturing (AM) demands articulated process control strategies, due to the multitude of physical phenomena involved in this kind of manufacturing processes. Complexity is even higher for what concerns the direct energy deposition (DED) technique, which offers much more potential flexibility and efficiency with respect to other metal AM technologies, at the cost of more difficult process control. The present work presents a multisensor approach able to combine online signals, collected while monitoring the deposition process, and data coming from offline inspection devices, during the built part quality check phase. This data fusion approach constitutes the foundation for the process modeling phase and, consequently, for the implementation of an intelligent control strategy that would act online by adjusting the machine process parameters chasing part dimensional, mechanical, and quality targets. The benefits of the proposed solution are assessed through a dedicated experimental campaign on a DED machine.

What we know and what we do not know about DMN

Abstract: The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research.

Process mining and simulation: a match made in heaven!

Abstract: Event data are collected everywhere: in logistics, manufacturing, finance, healthcare, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for particular cases. Process mining provides the means to discover the real processes, to detect deviations from normative processes, and to analyze bottlenecks and waste from such events. However, process mining tends to be backward-looking. Fortunately, simulation can be used to explore different design alternatives and to anticipate future performance problems. This keynote paper discusses the link between both types of analysis and elaborates on the challenges process discovery techniques are facing. Quality notions such as recall, precision, and generalization are discussed. Rather than introducing a specific process discovery or conformance checking algorithm, the paper provides a comprehensive set of conformance propositions. These conformance propositions serve two purposes: (1) introducing the essence of process mining by discussing the relation between event logs and process models, and (2) discussing possible requirements for the quantification of quality notions related to recall, precision, and generalization.

Process intensification in biologics manufacturing

Abstract: Over the last decade, the term process intensification (PI) has been applied almost exclusively to large scale chemical processes, but hardly to aspects of biopharmaceutical manufacturing, and not at all to relatively small scale, but high value generating pharmaceutical productions. Personalized and stratified medicine approaches for innovative therapies are changing that as well as economic limitations for health care systems in ageing society. Recent developments have resulted in new concepts for biologics manufacturing. The chemical engineering tool box for the adoption of process intensification methods to biologics is ready for industrial transfer; e.g. process modelling and comprehension, miniaturization/ scaled-down equipment for rapid process development, advanced process control, innovative analytics, mass transfer enhancement and process integration. Nevertheless, methods and equipment of process intensification, developed for chemical engineering applications, which are not as strictly regulated as in the case of biologics manufacturing, cannot simply be transferred to biotherapeutical applications. They require specific adjustment to the technological and regulatory requirements, for example any process intensification step within process development has to be finalized before producing first supplies for clinical trials. This generates quite a challenge with regard to technical readiness level and organization of companies in process development and implementation.

Optimal Process Mining for Large and Complex Event Logs

Abstract: This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners —Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort.