Optimal Placement of Actuators Via Sparse Learning for Composite Fuselage Shape Control

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Juan Du ◽  
Xiaowei Yue ◽  
Jeffrey H. Hunt ◽  
Jianjun Shi
Keyword(s):  
Shape Control ◽  
Estimation Algorithm ◽  
Fixed Number ◽  
Optimal Placement ◽  
Sparse Learning ◽  
Parameter Estimation Algorithm ◽  
Dimensional Shape ◽  
Shape Adjustment ◽  
Actuator Placement

Shape control is a critical task in the composite fuselage assembly process due to the dimensional variabilities of incoming fuselages. To realize fuselage shape adjustment, actuators are used to pull or push several points on a fuselage. Given a fixed number of actuators, the locations of actuators on a fuselage will impact on the effectiveness of shape control. Thus, it is important to determine the optimal placement of actuators in the fuselage shape control problem. In current practice, the actuators are placed with equal distance along the edge of a fuselage without considering its incoming dimensional shape. Such practice has two limitations: (1) it is non-optimal and (2) larger actuator forces may be applied for some locations than needed. This paper proposes an optimal actuator placement methodology for efficient composite fuselage shape control by developing a sparse learning model and corresponding parameter estimation algorithm. The case study shows that our proposed method achieves the optimal actuator placement for shape adjustments of the composite fuselage.

scholarly journals Optimal placement design for picking and packing in distribution centers

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110106
Author(s):  
John Rios ◽  
Rodrigo Linfati ◽  
Daniel Morillo-Torres ◽  
Iván Derpich ◽  
Gustavo Gatica
Keyword(s):  
Optimal Allocation ◽  
Programming Model ◽  
Operating Cost ◽  
Nonlinear Integer Programming ◽  
Operating Costs ◽  
Optimal Placement ◽  
Distribution Centers ◽  
Suitable Space ◽  
Efficient Distribution

An efficient distribution center (DC) is one that receives, stores, picks and packs products into new logistics units and then dispatches them to points of sale at the minimal operating cost. The picking and packing processes represent the highest operating cost of a DC, and both require a suitable space for their operation. An effective coordination between these zones prevents bottlenecks and has a direct impact on the DC’s operational results. In the existing literature, there are no studies that optimize the distribution of the picking and packing areas simultaneously while also reducing operating costs. This article proposes an integer nonlinear integer programming model that minimizes order preparation costs. It does so by predicting customer demand based on historical data and defining the ideal area for picking and packing activities. The model is validated through a real case study of seven clients and fifteen products. It achieves a [Formula: see text] reduction in operating costs when the optimal allocation of the picking and packing areas is made.

A Study on DTW-based RUL Estimation Algorithm of Propulsion Motor: Case Study

2021 ◽  
Vol 26 (4) ◽  
pp. 386-397
Author(s):  
Junseok Kim ◽  
Gangbok Lee ◽  
Hoesun Hwang ◽  
Jisoo Ahn ◽  
Jeongrim Oh ◽  
...  
Keyword(s):  
Estimation Algorithm

“Sintering” Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1593-1601
Author(s):  
W. Steven Rosenthal ◽  
Francesca C. Grogan ◽  
Yulan Li ◽  
Erin I. Barker ◽  
Josef F. Christ ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Selective Laser Sintering ◽  
Phase Field Model ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Multiple Data ◽  
In Situ Experiments ◽  
Parameter Estimation Algorithm ◽  
Microstructure Prediction

ABSTRACTSelective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

scholarly journals Implications of Spatially Variable Costs and Habitat Conversion Risk in Landscape-Scale Conservation Planning

2018 ◽  
Vol 9 (2) ◽  
pp. 402-414 ◽  
Author(s):  
Max Post van der Burg ◽  
Neil Chartier ◽  
Ryan Drum
Keyword(s):  
Spatial Variation ◽  
Habitat Loss ◽  
Tallgrass Prairie ◽  
Habitat Conservation ◽  
Optimal Placement ◽  
Management Actions ◽  
The North ◽  
Conservation Costs ◽  
Conservation Actions

Abstract “Strategic habitat conservation” refers to a process used by the U.S. Fish and Wildlife Service to develop cost-efficient strategies for conserving wildlife populations and their habitats. Strategic habitat conservation focuses on resolving uncertainties surrounding habitat conservation to meet specific wildlife population objectives (i.e., targets) and developing tools to guide where conservation actions should be focused on the landscape. Although there are examples of using optimization models to highlight where conservation should be delivered, such methods often do not explicitly account for spatial variation in the costs of conservation actions. Furthermore, many planning approaches assume that habitat protection is a preferred option, but they do not assess its value relative to other actions, such as restoration. We developed a case study to assess the implications of accounting for and ignoring spatial variation in conservation costs in optimizing conservation targets. We included assumptions about habitat loss to determine the extent to which protection or restoration would be necessary to meet an established population target. Our case study focused on optimal placement of grassland protection or restoration actions to influence bobolink Dolichonyx oryzivorus populations in the tallgrass prairie ecoregion of the north central United States. Our results show that not accounting for spatially variable costs doubled or tripled the cost of meeting the population target. Furthermore, our results suggest that one should not assume that protecting existing habitat is always a preferred option. Rather, our results show that the balance between protection and restoration can be influenced by a combination of desired targets, assumptions about habitat loss, and the relative cost of the two actions. Our analysis also points out how difficult it may be to reach targets, given the expense to meet them. We suggest that a full accounting of expected costs and benefits will help to guide development of viable management actions and meaningful conservation plans.

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