Layered Manufacturing: Current Status and Future Trends

2000 ◽  
Vol 1 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Debasish Dutta ◽  
Fritz B. Prinz ◽  
David Rosen ◽  
Lee Weiss
Keyword(s):  
Process Planning ◽  
Layered Manufacturing ◽  
Current Status ◽  
Manufacturing Processes ◽  
Future Trends ◽  
Planning Techniques ◽  
Current Technology ◽  
Heterogeneous Objects ◽  
The Future ◽  
Design Systems

This paper reviews the emerging field of layered manufacturing. This field is little over 10 years old but a significant amount of research has been conducted and results to date are quite promising. We consider three broad topics namely, design systems for heterogeneous objects, layered manufacturing processes, and process planning techniques. Several applications/examples are included in the course of the survey and limitations of current technology identified. We conclude with some possibilities for the future.

Layered Manufacturing With Embedded Components: Process Planning Considerations

1999 ◽  
Author(s):  
Jorge G. Cham ◽  
Beth L. Pruitt ◽  
Mark R. Cutkosky ◽  
Mike Binnard ◽  
Lee E. Weiss ◽  
...  
Keyword(s):  
Process Planning ◽  
Layered Manufacturing ◽  
Manufacturing Processes ◽  
Smart Products ◽  
Design And Manufacturing ◽  
Manufacturing Methods ◽  
The Creation ◽  
Shape Deposition Manufacturing ◽  
Planning Algorithms ◽  
Mechanical Components

Abstract This paper addresses the design and manufacturing of products with embedded components through layered manufacturing processes such as Shape Deposition Manufacturing (SDM). Embedding components allows the creation of novel designs such as “smart” products and integrated assemblies of sensors, actuators and other mechanical components. We present prototypes to illustrate the possibilities for such devices and we address the issues that constrain their process planning. Next, we present a combination of process planning algorithms and manufacturing methods that we have developed to support the design of layered products with embedded components.

Adaptive Slicing of Heterogeneous Solid Models for Layered Manufacturing

1998 ◽  
Author(s):  
Vinod Kumar ◽  
Prashant Kulkarni ◽  
Debasish Dutta
Keyword(s):  
Process Planning ◽  
Layered Manufacturing ◽  
Functionally Graded ◽  
Adaptive Slicing ◽  
Heterogeneous Objects ◽  
Solid Models ◽  
Fundamental Process ◽  
Planning Task ◽  
Material Information ◽  
Heterogeneous Solid

Abstract A novel feature of Layered Manufacturing, an emerging manufacturing technology, is that it enables fabrication of heterogeneous objects (multi-material and functionally graded interiors). In our earlier work, we developed new modeling schemes (called heterogeneous solid models) for representing these heterogeneous objects by capturing both geometry and material information. One of the crucial steps for fabricating these heterogeneous objects in LM is adaptive slicing, a fundamental process planning task. In this paper, we describe how the heterogeneous solid models can be adaptively sliced to aid in the LM fabrication of heterogeneous objects.

On the Application of Tensor Product Solids in Heterogeneous Solid Modeling

1998 ◽  
Author(s):  
Anne Marsan ◽  
Debasish Dutta
Keyword(s):  
Tensor Product ◽  
Process Planning ◽  
Material Properties ◽  
Model Material ◽  
Layered Manufacturing ◽  
Solid Model ◽  
Heterogeneous Objects ◽  
Solid Models ◽  
Multiple Materials ◽  
Heterogeneous Solid

Abstract With the development of layered manufacturing (LM) technologies, engineers are now able to build objects which are composed of multiple materials and/or have varying material properties throughout. These so called heterogeneous objects can be described by heterogeneous solid models, which contain information about the boundaries of the object, as well as material properties. In this paper we show how tensor product solids, which are the 3D extension of tensor product surfaces, can be used to model material properties within the framework of a heterogeneous solid model. We then show how a heterogeneous solid model which makes use of tensor product solids can be used in reverse engineering and process planning for LM.

scholarly journals Reviewing the Current Threat Landscape of Botnets

2020 ◽  
Author(s):  
Emmanuel C. OGU
Keyword(s):  
Rapid Evolution ◽  
Current Status ◽  
Exploratory Research ◽  
Future Trends ◽  
Future Prospects ◽  
Network Infrastructure ◽  
Research Areas ◽  
The Future ◽  
The Status ◽  
Short Time

Botnets have been around for about three decades, and their sophistication and capabilities have evolved rapidly over the period. Originally simple codes that were used for the administration of IRC channels, botnets today pose very formidable threats to systems and network infrastructure. They have become one of the more-preferred options in the toolkit of hackers and cybercriminals; particularly due to their ability to subvert and overrun secure infrastructures within a relatively short time. Research has greatly advanced in trying to keep up with the rapid evolution of the botnet threat. At this time, it is important to review the status of the threat, vis-á-vis the extent of research that has emerged in relation to the threat. This is crucial for understanding the future prospects of the threat, in terms of where it is headed next; as well as what research areas require more work. This exploratory research serves this purpose. It introduces the botnet threat from its early origins; then it traverses the current status of botnets, and summarizes research efforts so far (highlighting some limitations of modern countermeasures). It further goes on to discuss the future trends of botnets and botnet research, before bringing it together to present the current threat landscape.

Process-Planning for Layered Manufacturing of Heterogeneous Objects Using Direct Metal Deposition

2002 ◽  
Vol 2 (4) ◽  
pp. 330-344 ◽  
Author(s):  
Ki-Hoon Shin ◽  
Debasish Dutta
Keyword(s):  
Process Planning ◽  
Tool Path ◽  
Three Dimensional ◽  
Layered Manufacturing ◽  
Pattern Generation ◽  
Adaptive Slicing ◽  
Heterogeneous Objects ◽  
Build Time ◽  
Material Information ◽  
Discretization Process

Layered manufacturing (LM) is emerging as a new technology that enables the fabrication of three-dimensional heterogeneous objects such as multimaterials and functionally gradient materials (FGMs). The necessary steps for LM fabrication of heterogeneous objects include representation and process planning of material information inside an object. This paper introduces a new processing planning method that takes into account the processing of material information. The detailed tasks are pre-processing (discretization), orientation (build direction selection), and adaptive slicing of heterogeneous objects. In particular, this paper focuses on the discretization process that converts all of the material information inside a heterogeneous object into material features like geometric features. It is thus possible to choose an optimal build direction among various preselected ones by approximately estimating build time. This is because total build time depends on the complexity of features. This discretization process also allows adaptive slicing of heterogeneous objects to minimize surface finish and material composition error. In addition, tool path planning can be simplified into fill pattern generation. Examples are shown to illustrate the overall procedure.

A review of process planning techniques in layered manufacturing

2000 ◽  
Vol 6 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Prashant Kulkarni ◽  
Anne Marsan ◽  
Debasish Dutta
Keyword(s):  
Process Planning ◽  
Layered Manufacturing ◽  
Planning Techniques

scholarly journals Worldwide Prediction on COVID-19 Spreading Future Trends using Gaussian Model

2021 ◽  
pp. 291-298
Author(s):  
Dr. Bechoo Lal ◽  
Fareeha ◽  
Ashna Farah
Keyword(s):  
Predictive Model ◽  
Sudden Change ◽  
The Body ◽  
Current Status ◽  
Future Trends ◽  
Related Services ◽  
The World ◽  
The Future ◽  
Health Related ◽  
Entire World

Background: COVID-19 is a pandemic, which covered to all over the world and started the end of 2019. COVID-19 spreading rapidly from person to person and from one environment to another environment. In the current situation the entire world is passing through a very critical situation and medical services almost collapse due to the span of COVID-19. The virus is directly threatening to human being's life and attack to their nervous system, collapse lungs, breathing problems and damage other parts of the body system. The researcher build a predictive model using a Gaussian approach to find out the current status of COVID-19 and its future prediction. This predictive model is very helpful for countries and before timely they can manage their health related services, make a change in their decision making policy to stop COVID-19 spreading. Method: in this research paper the researcher builds a predicting model using real time analytics to measure the intensity of spreading COVID-19 in major concern countries. The main objective of this research article to predict the rate of spreading COVID-19 cases, visualize, and represent the future trends of COVID-19 cases. For the predictive analysis the researcher used the Gaussian Prediction model, time series analysis, exploratory data analysis, and K-means clustering. The researcher used the parameters such as rate of spreading, slow down speed, a sudden change in rate, prediction of the number of cases and differences in mortality rate. The results: The researcher discussed the weekly, monthly rate of spreading COVID-19 cases and predicted how it covered the world entire populations. The predictive model is very helpful to the countries where a number of cases are rapidly spreading and showing the future trends whether it is decreasing or increasing ratio. The countries can manage their health related services and other possible resources to stop the COVID-19 cases in their countries. If the prediction is unknown that situation is horrible for the entire world. Conclusion: Finally the researcher concluded that the predictive model of COVID-19 cases has significant impact to all over countries to show the future spreading trends, the accuracy level of this predictive model is 92% which is verified by using Gaussian approach. In some cases prediction might be unfavorable to handle the health care industries that are only %8 chances. The researcher giving the assurance the developed predictive model is more reliable and efficient to predict the COVID-19 case and its future trends, so the countries and their health related unit can manage the health related services in rapid manner.

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