PROTOTYPE SURFACE MICRO- PRECISION IN FUSED DEPOSITION MODELING PROCESS

To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Research on Petri Net Based Modeling and Analyzing Methods for Workflow Process

Workflow management is an important aspect in CSCW at present. The elementary knowledge of workflow process is introduced, the Petri nets based process modeling methodology and basic definitions are provided, and the analysis and verification of structural and behavioral correctness of workflow process are discussed. Finally, the algorithm of verification of process definitions is proposed.

Dynamic business process modeling and verification for inter-organizational collaboration

To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Process Modeling and Dynamic Simulation for EAST Helium Refrigerator

In this paper,the process modeling and dynamic simulation for the EAST helium refrigerator has been completed.The cryogenic process model is described and the main components are customized in detail.The process model is controlled by the PLC simulator,and the realtime communication between the process model and the controllers is achieved by a customized interface.Validation of the process model has been confirmed based on EAST experimental data during the cool down process of 300-80 K.Simulation results indicate that this process simulator is able to reproduce dynamic behaviors of the EAST helium refrigerator very well for the operation of long pulsed plasma discharge.The cryogenic process simulator based on control architecture is available for operation optimization and control design of EAST cryogenic systems to cope with the long pulsed heat loads in the future.

PRODUCT MODELING AND PROCESS PLANNING WITHIN CONCURRENT ENGINEERING ENVIRONMENT

A prodouct modeling and a process planning that are two essential basses of realizing concurrent engineering are investigated , a logical modeling technique , grammar representation scheme of technology knowledge and architecture of expert system for process planning within con- current engineering environment are proposed. They have been utilized in a real reaserch project.

On Treatment Patterns for Modeling Medical Treatment Processes in Clinical Practice Guidelines

Clinical practice guidelines(CPGs)contain evidence-based and economically reasonable medical treatment processes.Executable medical treatment processes in healthcare information systems can assist the treatment processes.To this end,business process modeling technologies have been exploited to model medical treatment processes.However,medical treatment processes are usually flexible and knowledge-intensive.To reduce the effort in modeling,we summarize several treatment patterns(i.e.,frequent behaviors in medical treatment processes in CPGs),and represent them by three process modeling languages(i.e.,BPMN,DMN,and CMMN).Based on the summarized treatment patterns,we propose a pattern-based integrated framework for modeling medical treatment processes.A modeling platform is implemented to support the use of treatment patterns,by which the feasibility of our approach is validated.An empirical analysis is discussed based on the coverage rates of treatment patterns.Feedback from interviewed physicians in a Chinese hospital shows that executable medical treatment processes of CPGs provide a convenient way to obtain guidance,thus assisting daily work for medical workers.

Formalization and Verification of Business Process Modeling Based on UML and Petri Nets

In order to provide a quantitative analysis and verification method for activity diagrams based business process modeling, a formal definition of activity diagrams is introduced. And the basic requirements for activity diagrams based business process models are proposed. Furthermore, the standardized transformation technique between business process models and basic Petri nets is presented and the analysis method for the soundness and well-structured properties of business processes is introduced.

Mathematical modeling and analysis of thermodynamic processes in a twin-rotor piston engine

In order to study the major performance indicators of the twin-rotor piston engine(TRPE), Matlab/simulink was used to simulate the mathematical models of its thermodynamic processes. With consideration of the characteristics of the working processes in the TRPE, corresponding differential equations were established and then simplified by period features of the TRPE. Finally, the major boundary conditions were figured out. The changing trends of mass, pressure and temperature of working fuel in the working chamber during a complete engine cycle were presented. The simulation results are consistent with the trends of an actual working cycle in the TRPE, which indicates that the method of simulation is feasible. As the pressure in the working chamber is calculated, all the performance parameters of the TRPE can be obtained. The major performance indicators, such as the indicated mean effective pressure, power to weight ratio and the volume power, are also acquired. Compared with three different types of conventional engines, the TRPE has a bigger utilization ratio of cylinder volume, a higher power to weight ratio and a more compact structure. This indicates that TRPE is superior to conventional engines.

Modeling study on the flow patterns of gas–liquid flow for fast decarburization during the RH process

A water model and a high-speed video camera were utilized in the 300-t RH equipment to study the effect of steel flow patterns in a vacuum chamber on fast decarburization and a superior flow-pattern map was obtained during the practical RH process. There are three flow patterns with different bubbling characteristics and steel surface states in the vacuum chamber： boiling pattern（BP）, transition pattern（TP）, and wave pattern（WP）. The effect of the liquid-steel level and the residence time of the steel in the chamber on flow patterns and decarburization reaction were investigated, respectively. The liquid-steel level significantly affected the flow-pattern transition from BP to WP, and the residence time and reaction area were crucial to evaluate the whole decarburization process rather than the circulation flow rate and mixing time. A superior flow-pattern map during the practical RH process showed that the steel flow pattern changed from BP to TP quickly, and then remained as TP until the end of decarburization.

Hierarchical Colored Timed Petri Nets for Maintenance Process Modeling of Civil Aircraft

Civil aircraft maintenance process simulation model is an effective method for analyzing the maintainability of a civil aircraft. First, we present the Hierarchical Colored Timed Petri Nets for maintenance process modeling of civil aircraft. Then, we expound a general method of civil aircraft maintenance activities, determine the maintenance level for decomposition, and propose the methods of describing logic of relations between the maintenance activities based on Petri Net. Finally, a time Colored Petri multi-level network modeling and simulation procedures and steps are given with the maintenance example of the landing gear burst tire of a certain type of aircraft. The feasibility of the method is proved by the example.

Enterprise Process Modeling Methodology and Environment Supporting Project Management

Development of the global economy makes modem enterprises confront challenges of efficient managements for large projects and complex processes. Projects are typically managed in rather special manners. On the contrary, there exist many methodologies for product process management to achieve consistency and continuance. However, processes often lack flexibility offered by projects. This paper dis~ the relationship of conceptual characteristics between process and project, gives low-level details to tackle the difference between them, and proposes an enterprise process modeling method for project management. An integrated environment is designed to support the method from which both project management and process management can receive benefits and conform to the limitations.

Medical Treatment Process Modeling Based on Process Mining and Treatment Patterns

activities.Ex-periments on a synthetic log of the non-secondary hy-pertension MTP and empirical findings demonstrate the effectiveness of our approach.The results show that the process mining in our approach framework can automatically generate more accurate MTP mod-els,and the subprocess models based on treatment pat-terns make the models easy to understand.

Mobile businessprocess modelingusinghigh level Petri nets

This paper aims at providing an effective modeling methods for more and more mobile business applications. First a high level Petri nets method-Xml nets is introduced, which combines the advantage of Petri nets and XML. Petri nets have strict mathematics and intuitionistic diagram ex- pression way, rich system describing means and system behavior analytical technology, and the re- lated powerful supporting tools. XML, as an extensible markup language, is already a information description de facto standard. XML nets could be utilized to model both the controlling flow and the data flow of mobile business process effectively, which consequently enables the analysis and optimi- zation of the mobile business processes and could be utilized to simulate the execution of the mobile business process with the Petri nets tools to improve the system execution efficiency. In this paper, to demonstrate the modeling of the controlling flow and data flow of mobile business process, an in- strument repair business case is modeled with XML nets.

Neural network modeling for dynamic pulsed GTAW process with wire filler based on MATLAB

Double-sided weld pool shapes were determined by multiple welding parameters and wire feed parameters during pulsed GTAW with wire filler. Aiming at such a system with multiple inputs and outputs, an effective modeling method, consisting of the impulse signal design, model structure and parameter identification and verification, was developed based on MATLAB software. Then, dynamic neural network models, TDNNM (Topside dynamic neural network model) and BHDNNM (Backside width and topside height dynamic neural network model), were established to predict double-sided shape parameters of the weld pool. The characteristic relationship of the welding process was simulated and analyzed with the models.

Modeling and Optimization of a Fractionation,Absorption,and Stabilization System in an Industrial Fluid Catalytic Cracking Process

Fluid catalytic cracking(FCC)is a vitally important refinery process.The fractionation,absorption,and stabilization system in the FCC process is a significant way to obtain key products,and its parameters will directly affect the quality of the products.In this work,using industrial data from an actual FCC process,a model of the FCC fractionation,absorption,and stabilization system was developed using process simulation software.The sequence quadratic program algorithm was then used to identify the parameters of each tower,increasing the accuracy of the simulation results.Next,using this improved model,a sensitivity analysis was performed to examine the effects of different operating conditions.The pattern-search method was then used to optimize the operating parameters of the system.The results showed that the optimized model has good prediction accuracy,and using the model,it was found that changing the operation parameters could result in a 1.84%improvement in economic benefits.As such,the developed model was demonstrated to be usefully applicable to the optimization of the process operation of an FCC fractionation,absorption,and stabilization system.

Artificial neural networks applied to spot welding process modeling

Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding qualitv parameters in terms of the welding process parameter. The performance of the neural networks for modeling is presented and evaluated using actual welding data. It is concluded that neural network modeling is a good means of estimating spot welding quality on-line.

Process Modeling of Ferrofluids Flow for Magnetic Targeting Drug Delivery

Among the proposed techniques for delivering drugs to specific sites within the human body, magnetic targeting drug delivery surpasses due to its non-invasive character and its high targeting efficiency. Although there have been some analyses theoretically for magnetic drug targeting, very few researchers have addressed the hydrodynamic models of magnetic fluids in the blood vessel of human body. This paper presents a mathematical model to describe the hydrodynamics of ferrofluids as drug carriers flowing in a blood vessel under the applied magnetic field. A 3D flow field of magnetic particles in a blood vessel model is numerically simulated in order to further understand clinical application of magnetic targeting drug delivery. Simulation results show that magnetic nanoparticles can be enriched in a target region depending on the applied magnetic field intensity. Magnetic resonance imaging confirms the enrichment of ferrofluids in a desired body tissue of Sprague-Dawley rats. The simulation results coincide with those animal experiments. Results of the analysis provide the important information and can suggest strategies for improving delivery in favor of the clinical application.

A method of manufacturing process modeling based on activity network for large-sized and complex products

Simulation technique is an efficient approach to realize the planning and scheduling of manufacturing process of products. An appropriate and efficient manufacturing process model is the basis and key of manufacturing process simulation. By analyzing the features of large-sized and complex products, a method of manufacturing process modeling based on activity network is presented and a mapping algorithm of translating BOM/BOP into the manufacturing process model is designed in detail.