Field Isolation As a Basis for Modular Product Architecture

Author(s):  
Valtteri Niutanen ◽  
Kevin Otto

Research into product modularity has created methods to partition a product system in to modules, including DSM algorithms to minimize various within- or between-module connectivity assumptions, as well as heuristic methods for combining functions into common modules, including dominant flow, convert-transmit, repeated elements, and branching flows. We here re-examine these methods in view of flows between field potentials. Fields are spatially defined scalar functions representing temperature, pressure, voltage, etc., each with associated flows such as heat, fluid, current, etc. It is hypothesized that isolation of elements to desired field values can form a physical basis for module definition. Product functional descriptions were examined from the literature. Those found sufficiently detailed with function structures, module definitions, part lists and subassembly definitions were studied here. Within these examples, there were 183 functions grouped into 51 modules. Of these, a statistically significant 67% of the modules had boundaries which isolated a field. For example, all elements within the module were at a high temperature and all elements outside the module were at a low temperature. Such agreement between actual modularity and field isolation provides evidence that an effective module definition strategy is to use field boundaries to separate into modules the necessarily high and low field values in the product structure. A second analysis considered how desired flows are designed to cross field boundaries. In 84% of the cases of flows crossing field boundaries, specific field separation functions were defined. Care was taken through specific functionality provision to ensure field boundary isolation. In summary, we find containing fields within a product can form a physics based guideline for defining product modularity.

Author(s):  
Xiaoxia Lai ◽  
John K. Gershenson

Researchers have expanded the definition of product modularity from function-based modularity to life-cycle process-based modularity. In parallel, measures of product modularity have been developed as well as corresponding modular product design methods. However, a correct modularity measure and modular design method are not enough to realize modular product design. To apply the measure and design method correctly, product representation becomes an important aspect of modular design and imperative for realizing the promised cost savings of modularity. In this paper, a representation for retirement process-based modular design has been developed. Built upon previous representations for assembly and manufacturing-based product design, the representation includes a process similarity matrix and a process dependency matrix. The retirement process-based similarity is based on the similarity in components’ post-life intents (recycling, reuse, disposal), and either the degree of their material compatibility if the components will be recycled, or their disassembly direction or disassembly tools if they need to be disassembled from each other for retirement. Process similarity within a module leads to increased process efficiency (the elimination of non-value added tasks) from the sharing of tooling/equipment. Retirement process-based dependency is developed based on disassembly difficulty, one aspect of the physical interactions between components. Retiring components together as a module to eliminate disassembly and differential processing and reducing the disassembly difficulty between the modules can increase the efficiency of the retirement process. We have first presented which process elements we should consider for defining retirement process similarity and dependency, and then constructed the respective similarity and dependency factors tables. These tables include similarity and dependency factors, which, along with their quantifications, are used to determine a product’s modular architecture to facilitate the retirement process. Finally, a fishing reel is used to illustrate how to apply these factors tables to generate the similarity and dependency matrices that represent a product for retirement-process based modular design. Using these representations as input to the DSM-based modular design methods, we can achieve a design with a modular architecture that improves the retirement process efficiency and reduces retirement costs.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hai-jun Wang ◽  
Chao-hui Shu

In an open innovation environment, it is meaningful for manufacturing enterprises targeting global markets to integrate qualified innovation resources. In this paper, the linkage between product modularity and open innovation is first discussed, revealing a role that modular product architecture plays in linking enterprises’ innovation requirements and innovation resources as external innovation inputs. Next, indices for evaluating external innovation resources are developed. An evaluation method based on fuzzy distance is then proposed, which is intended to select optimal resources for the core modules of modular product architecture. A modular product of Haier Group is used as a typical case to verify the proposed method. Consistent evaluation results of innovation resources are achieved for different decision-making attitudes. Another finding regarding the case enterprise is that the resource management mechanisms it employs lead to a win-win cooperative relationship with its partners.


2010 ◽  
Vol 101 (2) ◽  
pp. 135-144 ◽  
Author(s):  
M.D. Eyre ◽  
C. Leifert

AbstractActivity of 12 beneficial invertebrate groups was assessed in 2005 and 2006 on a farm in northern England split into conventional and organic management halves, using pitfall and pan traps set in both crops and field boundaries. Management, crop and boundary structure influences on invertebrate activity were assessed, as was the relationship between crop and boundary type. Classification of crop and boundary assemblages produced three and two groups, respectively, in both years. Organic arable crops had well-defined assemblages in both years; and, while grass and grass/clover fields were separated from conventional arable fields in 2005, there was mixing in 2006. One boundary group, in both years, was dominated by conventional arable fields with tall herbaceous boundary vegetation. The other group had more organic arable and grassy fields with shorter boundary vegetation. Redundancy analyses showed that a number of groups (Cantharidae, Coccinellidae, Syrphidae, Ichneumonidae, Braconidae, Proctotrupoidea, Lycosidae) were more active in organic arable fields with more Staphylinidae in conventional arable crops and no obvious trend with Carabidae, Hemiptera, Neuroptera and Linyphiidae. Activity of some groups, especially Coccinellidae, Syrphidae and parasitic wasps, was strongly related to weed cover. Staphylinidae were most active in tall herbaceous boundaries by conventional arable crops with more of a number of groups (Cantharidae, Coccinellidae, parasitic wasps) in short herbaceous boundaries by organic arable crops. Organic management produced most differences in aerially-dispersed invertebrates, and management had a profound effect on activity in field boundaries. Possible management prescriptions to increase invertebrate activity include changing sowing times, weed cover manipulation and field boundary and margin management.


2021 ◽  
Vol 13 (4) ◽  
pp. 722
Author(s):  
Alireza Taravat ◽  
Matthias P. Wagner ◽  
Rogerio Bonifacio ◽  
David Petit

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.


2021 ◽  
Vol 13 (11) ◽  
pp. 2197
Author(s):  
François Waldner ◽  
Foivos I. Diakogiannis ◽  
Kathryn Batchelor ◽  
Michael Ciccotosto-Camp ◽  
Elizabeth Cooper-Williams ◽  
...  

Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics.


2019 ◽  
Vol 142 (4) ◽  
Author(s):  
Kevin Otto ◽  
Katja Hölttä-Otto ◽  
Roozbeh Sanaei ◽  
Kristin L. Wood

Abstract System architecture and modularity decisions are inherent to preliminary concept design. Prior modularity research has considered minimizing interactions between modules and increasing the commonality among modular product variants. Effective approaches include function structure partitioning guidelines, affinity analysis, or matrix clustering algorithms. We consider here designs with field constraints, such as situations when elements cannot be placed in certain regions such as a high-temperature field, a high-pressure field, a high magnetic field, etc. which place constraints on modularity choices. Practical design guidelines are developed here for modularity considering field constraints. Two types of guidelines are proposed, field separation and concept generation. The field separation guidelines propose zonal boundaries within which system modules need be confined. The concept generation guidelines propose how to violate the field constraints through new concepts. Moving functionality from one side of a field boundary to the other is nontrivial and involves new concept generation for the modules to function at the higher or lower field values. The guidelines are defined and illustrated via multiple common examples as well as two extended case studies. We demonstrate the approach using field boundaries on an electric motor controller and on a medical contrast injector, and also use of fields to generated novel concepts. The guidelines support for modularity concept and embodiment decisions.


2020 ◽  
Vol 1 ◽  
pp. 2435-2444
Author(s):  
C. Wyrwich ◽  
G. Jacobs ◽  
J. Siebrecht ◽  
C. Konrad

AbstractFacing a rising competitive pressure, manufactures create advantages when they are able to offer customer-specific products to the conditions of a mass production article. Traditional configurators support the creation of personalized products from the elements of a modular product system, but are based on a pre-defined set of rules. The model based approach changes the environment of configuration from static configuration rules to the dependencies defined within the product's system model. So, by regarding target quantities of the user, the configurator identifies the optimal variant.


2015 ◽  
Vol 48 (3) ◽  
pp. 1387-1392 ◽  
Author(s):  
Kenneth E. Hernández ◽  
Elías Olivares-Benítez ◽  
Catya A. Zuñiga

2018 ◽  
Vol 5 (10) ◽  
pp. 2252-2256 ◽  
Author(s):  
Changwoo Kim ◽  
Seung Soo Lee ◽  
Benjamin J. Reinhart ◽  
Minjung Cho ◽  
Brandon J. Lafferty ◽  
...  

In this work, we systematically design and synthesize manganese ferrite coated superparamagnetic magnetite nanocrystals, with oleylphosphate bilayer surface coatings (Fe3O4@MnxFeyO4@OP), for ultra high capacity uranium sorption and low-field magnetic-based separation in water.


Sign in / Sign up

Export Citation Format

Share Document