Formal Process Model Research Articles

Investigation of Micropattern Forming Process on Zr‐4 Alloy Surface Considering Plowing Effects

Micropattern forming process is an efficient and low‐cost process for fabricating multifunctional surfaces in electronics, energy, and biomedical products, but this process of creating microdimples on the surface of zirconium alloys has not been investigated. Currently, there are few models describing the micropattern forming process, and these existing models have not considered the effect of roughness on forming. The influence of roughness on micropattern forming process is not to be neglected due to the size effect, especially the plowing effects caused by asperities in surface roughness during sliding process. In this article, a micropattern forming process model is developed by combining a rough plane indentation model and a plowing effects model, and the experimental study of the forming process is conducted, which can quantitatively evaluate the effects of different process parameters and different roughness of forming die on forming process. The applicability range of the developed model is obtained, and the model is capable of predicting the forming process with various process parameters and surface roughness of forming die by comparing the experiment results with the model results, which is of great significance to the design of the forming die and the improvement of the forming process.

Features of transport flow modeling in projects of sustainable development in urban areas

The paper highlights the problems of modern Russian cities that significantly affect their socio-economic development. Most of the problems are related to the transport and logistics system due to the historical design of urban neighborhoods, chaotic and point development, and the increasing importance of road transport. All this supports the need to develop comprehensive projects for the development of urban areas. It is impossible without the use of specialized software tools, which are based on all aspects of the territory being modernized. The use of a set of methods, based on structural analysis and simulation modeling, allowed us to obtain formal models of transport processes associated with Yeniseyskaya Street in the city of Moscow. Based on the obtained models, examples of created scenarios for forecasting changes in the processes of the object of study over time during the modernization of its individual elements are presented.

Formal theories clarify the complex: Generalizing a neural process account of the interaction of visual exploration and word learning in infancy.

The interaction of visual exploration and auditory processing is central to early cognitive development, supporting object discrimination, categorization, and word learning. Research has shown visual-auditory interactions to be complex, created from multiple processes and changing over multiple timescales. To better understand these interactions, we generalize a formal neural process model of early word learning to two studies examining how words impact 9- to 22-month-olds' attention to novelty. These simulations clarify the origin and nature of previously demonstrated effects of labels on visual exploration and the basis of mutual exclusivity effects in word learning. We use our findings to discuss key questions for this special section: what makes a good theory and how should formal theories interface with empirical paradigms and findings?

Patterns for modeling process variability in a healthcare context

PurposeMedical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.Design/methodology/approachThe authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.FindingsA set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.Practical implicationsWhen communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.Originality/valueThe patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

Supporting product lifecycle collaboration and knowledge-related evaluation: an active-passive collaboration mechanism and fuzzy evaluation method

Collaboration is essential to improve the efficiency of product research and development (R&D), shorten the R&D cycle, and reduce the R&D costs in complex product lifecycle model management (CPLMM). However, disorganized processes and the unreliability of the result evaluation remain enormous challenges for efficient collaboration. This article proposes an active-passive collaboration mechanism to enable a regulated collaboration system, which can direct the self-organized collaboration of stakeholders. C-D-Petri Net is presented for the formal collaboration process modeling. The result evaluation in active-passive collaboration involves multi-source knowledge across disciplines and phases. To address the unreliable collaboration evaluation (Co-evaluation) caused by insufficient evaluation knowledge and weak correlation between expertise and evaluation task, the collaborative fuzzy comprehensive evaluation (CFCE) model is established to support Co-evaluation actions, and its core improvement lies in the definition and introduction of collaboration volume. Finally, a simulated aircraft horizontal tail control system is regarded as an engineering application case to demonstrate and verify the effectiveness of the proposed method.

A flow forming process model to predict workpiece properties in AISI 304L

The control of workpiece properties enables an application-oriented and time-efficient production of components. In reverse flow forming, e.g., the control of the microstructure profile, in contrast to the adjustment of the geometry, is not yet part of the state of the art. This is particularly challenging when forming seamless tubes made of metastable austenitic stainless AISI 304L steel. In this steel, a phase transformation from austenite to martensite can occur due to mechanically and/or thermally induced energy. The α’-martensite has different mechanical and micromagnetic properties, which can be advantageous depending on the application. For the purpose of local property control, the resulting α’-martensite content should be measured and controlled online during the forming process. In this paper, results from an empirical correlation model of process parameter combinations and resulting α’-martensite content as well as geometry will be presented. Based on this, the focus of the paper will be on process modeling by means of FEM in order to create the transition to a numerically supported process model. Furthermore, it will be specified how the numerical process model can be used in a predictive manner for an online closed-loop process control.

Understanding how systemic change happens -marketisation and de-marketisation

This paper discusses possible conceptual foundations of formal models of endogenous change processes, understood here as movements between market and non-market transactions at the level of the national economy. It links but does not merge movements of resources with shifts in the pattern of transaction types. In focussing on transaction types, it deploys insights from Commons, Coase, and Godelier, to discuss how framing transaction types as the fundamental 'thing to be explained' points to the value of choices about how activity may best be organised, which requires a general concept, which can be found in Commons' 'going concern', applicable to transactions focussing on markets or not. It entails the possibility of institutional change and shifts in the location of economic resources without formal policy change. It suggests that the main requirement for such change processes are dualistic incentive patterns that operate upon institutional choice and/or development, which derive at root from experienced contrasts between the realities of existing and normatively privileged systems, and others, normatively initially deemed inferior, that offer key actors greater economic efficiency. Moves of institutional activity from one to the other are thus conceptually processes of endogenous systemic change. System in this sense is thus viewed as a coexistence of alternatives. The motivation comes directly from consideration of two very different historical moments: endogenously driven shifts 'from plan to market' in countries attempting central planning, and contemporary pressures in market economies from areas of the economy, such as services, where joint production and/or own consumption imply irremediable market failure and so non-market based economic institutions offer greater economic efficiency and may therefore attract both resources (factors of production) and investment in development of suitable transactions and their organisation.

Proposal of BPMN extension with a view to effective modeling of clinical pathways

PurposeThe aim of the article is to propose BPMN extensions that facilitate the modeling of Clinical Pathways in a way that enables for various groups of users, the transfer of a much wider range of information in the form of process models without compromising their readability and usefulness.Design/methodology/approachThe paper uses the design science research methodology (DSRM) and covers phases of a design-oriented research project extending BPMN notation for clinical pathway modeling.FindingsThe article proposes extensions of BPMN in 5 areas, enabling standardization of the description of business processes of different natures and complexity and in turn meeting the needs and requirements of modeling clinical pathways and, more broadly speaking, knowledge-intensive business processes (kiBPs) in general. As shown by the evaluation carried out among medical personnel, the proposed extensions allow for the readable transfer of a considerably larger body of information relevant to the planned, conducted and assessed therapy (kiBPs) than the current BPMN 2.0 standard.Originality/valueThe BPMN extensions proposed in the article fill the gaps in this notation and do not require users to know many notations, which in practice is unrealistic. Defined extensions to the BPMN specification makes it possible to standardise the description of processes of different natures and levels of complexity. In this way, both simplified models (and views of models) dedicated to users unfamiliar with BPMN and models (or views) using advanced possibilities provided by BPMN can be based on one standard, even if they use only a small part of its possibilities.

Prediction of shear deformation and thickness gradients in deep-draw forming of ultra-high molecular weight polyethylene

Ultra-high molecular weight polyethylene (UHMWPE) composite armor offers a high level of lightweight protection. In the manufacturing of complex curvature UHMWPE based protective materials, UHMWPE composites are transformed from flat sheet stock to a near net shape complex curvature preform through deep-draw forming. The development of a deep-draw forming process model is critical for predicting UHMWPE sheet deformation during processing and knowing the fiber orientations at each location in the entire final part. This work introduces a robust methodology to model the deep-draw forming process. The model is validated by comparing critical output with experimental results for multiple UHMWPE materials and over a range of part thicknesses. A hemispherical geometry is used to demonstrate the methodology. A homogenization strategy for grouping sheets is presented and validated, which is critical for keeping computational times manageable. The influence of the material in-plane shear response, a critical model input, is investigated. The model developed in this work represents a valuable tool for selecting deep-draw forming parameters and accelerates the evaluation of new and emerging UHMWPE materials for application in manufacturing complex curvature protective armor.

Shape, Resonant Frequency and Thermoelastic Dissipation Analysis of Free-Formed Microhemispherical Shells Based on Forming Process Modeling.

Free-form microhemispherical shell resonators have the advantages of high quality factor and mass production. The shape of microhemispherical shells created via this process is based on a single mold and is difficult to adjust, which affects the resonant frequency and quality factor. In this paper, a process analysis model is established through in-depth analysis of the process mechanism and flow of the free-forming method. Based on this model, the influence of the designed preforming parameters on the shape, resonant frequency and thermoelastic dissipation of the microhemispherical shell are analyzed in detail, providing theoretical guidance for parameter design. The results show that the depth and the ratio of internal to external pressure of the substrate’s annular groove affect the height and thickness of the microhemispherical shell, and the structural thickness affects the thickness of the microhemispherical shell; these in turn affect the resonant frequency and thermoelastic dissipation of the microhemispherical shell resonator. In addition, the inner diameter of the substrate’s annular groove mainly affects the radius of the support column of the microhemispherical shell, and the influence on the resonant frequency and thermoelastic dissipation of the resonator is relatively low.

Stress- and process model for dispersing of nanoparticulate suspensions in laminar shear flow

This work presents a stress model and a process model for dispersing processes in laminar shear flow, which are performed in devices such as kneaders, extruders and three roller mills. Based on deep investigations of two kneaders (lab- and pilot-scale) a methodology is presented, which allows modelling the dispersing process by applying a stress model along with a population balance model to the given task. Stress models describe how often particles are stressed during the process and which energy is transferred to the particles. For dispersing in laminar shear flow, it is crucial to distinguish between dispersing-effective and ineffective stress events based on whether the stress intensity is high enough to overcome the particle strength. Results suggest, that the effective specific energy input determines the dispersing progress independent of the process scale. A process model in form of a population balance was required to obtain otherwise unknown parameters for the stress model. The applied population balance model accounts for the acting working principles during dispersing. For such a mechanistic depiction of the dispersing progress, the trilateral dependency between particle size, resulting viscosity, particle stressing and fragmentation must be modelled. It was found that based on the models, the process behaviour on both scales can be depicted, which offers the perspective of a knowledge-based scale-up of machines and processes, process control and process design based on these results.

Forming process model of rail corrugation based on friction induced torsional vibration determined by vertical dynamics

Recently, field measurements show that corrugations are generated on curved tracks and that its wavelength varies with fasteners. The phenomenon that corrugations are generated on curved tracks can be explained with the assumption that corrugation is generated by the torsional vibration of wheelset. This assumption, however, has not been investigated in details yet. In this research, a finite element model including the nonlinear friction-creepage characteristics is developed to analyse the unstable vibration of a wheelset negotiating a curved track, and the results show that the friction induced torsional vibration between inner wheel and rail is most likely to be generated, which can verify the assumption. Furthermore, a mathematical model is developed to illustrate the forming process of rail corrugation, and the results indicate that the corrugation generated on curved track can be attributed to the periodical fluctuation of frictional power due to torsional vibration. Based on the results above, a forming process model of rail corrugation is formulated, which can be applied to explain why wavelength of corrugation varies with fasteners: the vertical dynamics affected by fasteners determine the frequency of torsional vibration and further affect the wavelength of corrugation, indicating the reliability and effectiveness of this forming process model.

Predicting remaining execution time of business process instances via auto-encoded transition system

As an important task in business process management, remaining time prediction for business process instances has attracted extensive attentions. However, most of the traditional remaining time prediction approaches only take into account formal process models and cannot handle large-scale event logs in an effective manner. Although machine learning and deep learning have been recently applied to the remaining time prediction task, these approaches cannot incorporate domain knowledge naturally. To overcome these weaknesses of existing studies, we propose a remaining execution time prediction approach based on a novel auto-encoded transition system, which can enhance the complementarity of process modeling and deep learning techniques. Through auto-encoding the event-level and state-level features, the proposed approach can represent process instances in a comprehensive and compact form. Furthermore, a transfer learning strategy is proposed to train the remaining time prediction model so as to avoid overfitting and improve the accuracy of prediction. We conduct extensive experiments on four real-world datasets to verify the effectiveness of the proposed approach. The results show its superiority over several state-of-the-art approaches.

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM.

Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece

Safety in touristic destinations is of utmost importance since tourists’ preferences change frequently in response to emerging threats. Natural hazards are a significant risk and, as such, they need to be considered in the effort for safe tourism. Services and systems monitoring and predicting extreme natural phenomena and disasters in sites of special tourist and cultural interest can lead to more effective risk management and incident response. This paper presents Xenios, a system under development in Greece that provides early warning and risk communication services via web-based and mobile phone applications. We present the user requirements analysis contacted, which led to the design of a modular system architecture through a formal Business Process Model procedure. Currently, early warning systems for wildfire, floods, and extreme weather events are offered, based on a fusion of information from satellite imagery, meteorological forecasts, and risk estimation models. Moreover, visitors’ dispersion monitoring via unmanned aerial vehicles (UAVs) and Wi-Fi connection signals is also offered, along with emergency response planning and ticketing system’s interfacing. The system is built around a modular architecture that permits the easy integration of new subsystems or other danger forecasting modules, depending on the site’s actual needs and limitations. Xenios also provides a mobile app for site visitors, which establishes a communication link for sending alarms, but also serves them with useful tourist information, so that they are encouraged to download and use the app. Finally, the opportunities for supporting a viable business model are also discussed. The results of this study could prove useful in designing other natural risk management systems for sites of cultural and natural interest.

Methods for online measurement and control of section deviations during hot rolling of wire rod and bars

In the joint project PIREF, the metal forming group of the University of Duisburg-Essen has collaborated with the University of Applied Sciences Ruhr-West Mülheim (Ruhr), the University of Siegen, EMG Automation GmbH and SMS group GmbH to develop sensors, for an online measurement of material velocity and cross section as well as control models for the rolling process of wire rod and bars. The University of Duisburg-Essen provided a metal forming process model for the rolling process to assess the influencing parameters on the rolled section precision. A technique was found to segregate height- from width- influencing parameters from a measured cross-sectional area and actual roll gap. With this measuring technology and with help of the process model, rules for control of the rolling process to achieve close tolerances were obtained. The modelling was accompanied by rolling trials on a laboratory rolling mill at the University of Duisburg-Essen, where a typical Round-OvalRound pass sequence was used for validation of the rolling model concerning lateral spread, inlet and outlet velocity as well as rolling force and torque calculation. The present paper shows how the material flow and the distribution of the velocity in the roll gap can be described. In subsequent rolling of bar and rod in a continuous rolling mill the dimensions can be influenced by application of longitudinal stresses and screwdown. The application of stress can be achieved by an inter-stand velocity mismatch. With the developed models the necessary velocity mismatch can be calculated.

Tool-based automatic generation of digital assembly instructions

Formal modelling methods can be applied to tackle the complexity of production processes and varying product portfolios particularly affecting SMEs. As part of the project SynDiQuAss a software tool has been developed to automatically generate assembly instructions from a formal process model. Based on a template for a whole product family, the assembly process is decomposed into elementary tasks. In the second step available information such as CAD diagrams, required tools and parts is linked to provide cognitive assistance. The tool-based generation of assembly instructions can thus help increase both productivity and product quality for small lot sizes in manual assembly.

Developing a policy framework for adoption and management of drones for agriculture in Africa

ABSTRACT This paper proposes a drone policy framework for developing drone regulations for Africa, expediting the individual countries’ developing drone regulations. This research was conducted following concerns raised by individuals and businesses whose imported drones were seized by customs or had to go through a cumbersome process for approval to use drones for lack of regulations. To derive the policy framework, we matched the provisions of 15 existing drone regulations from Africa to the International Civil Aviation Organization (ICAO) Manual on Remotely Piloted Aircraft Systems (RPAS) using a decomposition modelling technique. The proposed framework is presented as a formal business process model and is evaluated in a multi-case study approach in two different case studies. The results indicate that two key aspects/elements are crucial in the development of a drone framework and thus are included in our framework: (i) stakeholders’ inclusion in the policy development and (ii) regulators liaising with security agencies like the police and immigration officials to implement and enforce developed regulations. Applying the proposed framework in describing existing regulations reveals that 40-85% of provisions in existing regulations conform to the provisions in ICAO’s RPAS manual.

Translating Workflow Nets to Process Trees: An Algorithmic Approach

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.

IT-Assisted Process Management in Healthcare.

Clinical processes need to be well understood before a new health IT tool can be introduced. Observations, interviews, surveys, or documentation analysis are carried out to systematically collect information to better understand a clinical process. To aggregate and visualize the collected information about a clinical process, use case diagrams can build a basis. Formal process models such as process chain diagrams or BPMN diagrams are well suited to model the process in detail. The objective of this chapter is to discuss these methods for analyzing and modeling clinical processes, as this is an important precondition for systematic process management in health care.