scholarly journals Quantifying Discretization Errors in Electrophoretically-Guided Micro Additive Manufacturing

Micromachines ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 447 ◽  
Author(s):  
David Pritchet ◽  
Newell Moser ◽  
Kornel Ehmann ◽  
Jian Cao ◽  
Jiaxing Huang

This paper presents process models for a new micro additive manufacturing process termed Electrophoretically-guided Micro Additive Manufacturing (EPμAM). In EPμAM, a planar microelectrode array generates the electric potential distributions which cause colloidal particles to agglomerate and deposit in desired regions. The discrete microelectrode array nature and the used pulse width modulation (PWM) technique for microelectrode actuation create unavoidable process errors—space and time discretization errors—that distort particle trajectories. To combat this, we developed finite element method (FEM) models to study trajectory deviations due to these errors. Mean square displacement (MSD) analysis of the computed particle trajectories is used to compare these deviations for several electrode geometries. The two top-performing electrode geometries evaluated by MSD were additionally investigated through separate case studies via geometry variation and MSD recomputation. Furthermore, separate time-discretization error simulations are also studied where electrode actuating waveforms were simulated. The mechanical impulse of the electromechanical force, generated from these waveforms is used as the basis for comparison. The obtained results show a moderate MSDs variability and significant differences in the computed mechanical impulses for the actuating waveforms. The observed limitations of the developed process model and of the error comparison technique are briefly discussed and future steps are recommended.

Author(s):  
Paul Witherell ◽  
Shaw Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process to provide illustrative examples of the proposed classification and metamodel approach. We describe how a coordinated approach can be used to extend modeling capabilities by promoting model composability. As part of future work, a framework is envisioned to realize a more coherent strategy for model development and deployment.


Author(s):  
Sungshik Yim ◽  
David W. Rosen

This research discusses a framework for automating process model realization for additive manufacturing. The models map relationships from design requirements to process variables and can be utilized for future process planning. A repository is employed to collect data and contains previous process plans and corresponding design requirements. The framework organizes data through a statistical clustering method and builds regression models using a multi-layer neural network. Hierarchical and k-means clustering methods are employed in series to manage the data. A two layer neural network and augmented training algorithm are employed to build process models. The framework has been tested with Stereolithography and Selective Laser Sintering process planning problems to demonstrate its usefulness.


2019 ◽  
Vol 25 (2) ◽  
pp. 255-265 ◽  
Author(s):  
Matthijs Langelaar

PurposeThe purpose of this paper is to communicate a method to perform simultaneous topology optimization of component and support structures considering typical metal additive manufacturing (AM) restrictions and post-print machining requirements.Design/methodology/approachAn integrated topology optimization is proposed using two density fields: one describing the design and another defining the support layout. Using a simplified AM process model, critical overhang angle restrictions are imposed on the design. Through additional load cases and constraints, sufficient stiffness against subtractive machining loads is enforced. In addition, a way to handle non-design regions in an AM setting is introduced.FindingsThe proposed approach is found to be effective in producing printable optimized geometries with adequate stiffness against machining loads. It is shown that post-machining requirements can affect optimal support structure layout.Research limitations/implicationsThis study uses a simplified AM process model based on geometrical characteristics. A challenge remains to integrate more detailed physical AM process models to have direct control of stress, distortion and overheating.Practical implicationsThe presented method can accelerate and enhance the design of high performance parts for AM. The consideration of post-print aspects is expected to reduce the need for design adjustments after optimization.Originality/valueThe developed method is the first to combine AM printability and machining loads in a single topology optimization process. The formulation is general and can be applied to a wide range of performance and manufacturability requirements.


SPIEL ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 121-145
Author(s):  
Larissa Leonhard ◽  
Anne Bartsch ◽  
Frank M. Schneider

This article presents an extended dual-process model of entertainment effects on political information processing and engagement. We suggest that entertainment consumption can either be driven by hedonic, escapist motivations that are associated with a superficial mode of information processing, or by eudaimonic, truth-seeking motivations that prompt more elaborate forms of information processing. This framework offers substantial extensions to existing dual-process models of entertainment by conceptualizing the effects of entertainment on active and reflective forms of information seeking, knowledge acquisition and political participation.


2013 ◽  
Vol 770 ◽  
pp. 361-365
Author(s):  
Yu Peng Xin ◽  
Xi Tian Tian ◽  
Li Jiang Huang ◽  
Jun Hao Geng

In order to improve the efficiency of NC machining programming, and realize the rapid establishment of blank model or middle blank model, a geometrical modeling method of process driven by typical process model was put forward. This method is based on the typical process for the establishment of typical process model, to establish a mapping between modeling operation and machining process ontology, and format model mapping rules. In the process geometrical modeling of the high similarity parts, by calling the typical process model mapping rules, can generate process models automatically. A enterprise disc type parts typical process as an example is used to verify the proposed method.


Author(s):  
A. V. Vinnichenko ◽  

The paper presents methods and approaches for mathematical modeling and rationalization of flexible additive manufacturing, as well as other processes by which it is possible to create additive models for their integration into the system of experimental or pilot production. The work has also formed and synthesized a process model, which includes flexible production indicators, service indicators, and a developed criterion base for their assessment. The work takes into account the optimization criteria, as well as maximizing and minimizing risks for additive manufacturing, taking into account the possible risk component when deploying new processes for experimental and small-scale production. The models and methods described in the article will make it possible to carry out mathematical modeling and subsequent improvements for the flexible production process using additive technologies, used as a means of achieving the rational use of existing production resources within the framework of existing scientific and production complexes.


2019 ◽  
Vol 25 (5) ◽  
pp. 908-922 ◽  
Author(s):  
Remco Dijkman ◽  
Oktay Turetken ◽  
Geoffrey Robert van IJzendoorn ◽  
Meint de Vries

Purpose Business process models describe the way of working in an organization. Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow. However, they often present an idealized view. This means that unexpected exceptions – exceptions that are not modeled in the business process model – can also occur in practice. This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions. The purpose of this paper is to study the relation between the occurrence of exceptions and operational performance. Design/methodology/approach The paper does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional. Subsequently, it analyzes the differences between normal and exceptional paths. Findings The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions. Practical implications These findings lead to practical implications on policies that can be followed with respect to exceptions. Most importantly, unexpected exceptions should be avoided by incorporating them into the process – and thus transforming them into expected exceptions – as much as possible. Also, as not all exceptions lead to longer throughput times, continuous improvement should be employed to continuously monitor the occurrence of exceptions and make decisions on their desirability in the process. Originality/value While work exists on analyzing the occurrence of exceptions in business processes, especially in the context of process conformance analysis, to the best of the authors’ knowledge this is the first work that analyzes the possible consequences of such exceptions.


2021 ◽  
Vol 6 (3) ◽  
pp. 170
Author(s):  
Hilman Nuril Hadi

Business process model was created to make it easier for business process stakeholders to communicate and discuss the structure of the process more effectively and efficiently. Business process models can also be business artifacts and media that can be analyzed further to improve and maintain organizational competitiveness. To analyze business processes in a structured manner, the effect/results of the execution of business processes will be one of the important information. The effect/result of the execution of certain activities or a business process as a whole are useful for managing business processes, including for improvements related to future business processes. This effect annotation approach needs to be supported by business process modeling tools to assist business analysts in managing business processes properly. In previous research, the author has developed a plugin that supports business analysts to describe the effects semantically attached to activities in the Business Process Model and Notation (BPMN) business process model. In this paper, the author describes the unit testing process and its results on the plugin of semantic effect annotation that have been developed. Unit testing was carried out using the basic path testing technique and has obtained three test paths. The results of unit test for plugin are also described in this paper.


2020 ◽  
Vol 17 (3) ◽  
pp. 927-958
Author(s):  
Mohammadreza Sani ◽  
Sebastiaan van Zelst ◽  
Aalst van der

Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.


2018 ◽  
Vol 2 (4-2) ◽  
pp. 349
Author(s):  
Ivaylo Kamenarov ◽  
Katalina Grigorova

This paper describes the internal data model for a business process generator. Business process models are stored in an Event-driven process chain notation that provides a natural way to link the individual elements of a process. There is a software architecture that makes it easy to communicate with users as well as external systems.


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