CLASSIFYING COMPONENT FUNCTION IN PRODUCT ASSEMBLIES WITH GRAPH NEURAL NETWORKS

Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function- based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learn- ing from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems, and Design-for-X consideration in function-based design.

A new response surface method based on the adaptive bivariate cut-HDMR

Purpose The purpose of this study is to improve the efficiency and accuracy of response surface method (RSM), as well as its robustness. Design/methodology/approach By introducing cut-high-dimensional representation model (HDMR), the delineation of cross terms and the constitution analysis of component function, a new adaptive RSM is presented for reliability calculation, where a sampling scheme is also proposed to help constructing response surface close to limit-state. Findings The proposed method has a more feasible process of evaluating undetermined coefficients of each component function than traditional RSM, and performs well in terms of balancing the efficiency and accuracy when compared to the traditional second-order polynomial RSM. Moreover, the proposed method is robust on the parameter in a wide range, indicating that it is able to obtain convergent result in a wide feasible domain of sample points. Originality/value This study constructed an adaptive bivariate cut-HDMR by introducing delineation of cross-terms and constitution of univariate component function; and a new sampling technique is proposed.

New Visualization Models of Designation Pathway and Group Categorization of Device–Drug and Device–Biologic Combination Products Classification in the United States: Analysis of FDA Capsular Decisions

Objective The developer and sponsor of new combination products in US needs to forecast which classification and designation to the regulatory scheme of drug, biological product, or device would be required for the new products by the Food and Drug Administration (FDA). To improve the predictability and acceptability of the designation of new combination products for innovators, developers, and sponsors, and to encourage the development and early access of new combination products, we proposed new visualization models of the designation pathway and group categorization. Method We searched the website of the FDA on 15 November, 2020 to identify the regulatory scheme of the FDA’s 129 capsular decision cases of device–drug and device–biologics combination products and other publicly available cases the FDA designated to the drug/biologic or device regulatory scheme. Results By introducing a new definition for primary intended use (PIU) by developers and sponsors extracted from the classification factors of primary mode of action (PMOA), we developed new visualization models of the designation pathway and two-dimensional group categorization. And applying these models to the cases the FDA designated, we proposed a new group categorization of combination products while focusing on the device component function. Conclusions The new visualization models with PIU and PMOA and the new group categorization focusing on the device component function proposed in this study may increase predictability and acceptability of the classification of newly developed combination products into the regulatory scheme of drug, biological product, and device, for innovators, developers, and sponsors.

A Weighted Confidence Metric to Improve Automated Functional Modeling

Expanding on previous work of automating functional modeling, we have developed a more informed automation approach by assigning a weighted confidence metric to the wide variety of data in a design repository. Our work focuses on automating what we call linear functional chains, which are a component-based section of a full functional model. We mine the Design Repository to find correlations between component and function and flow. The automation algorithm we developed organizes these connections by component-function-flow frequency (CFF frequency), thus allowing the creation of linear functional chains. In previous work, we found that CFF frequency is the best metric in formulating the linear functional chain for an individual component; however, we found that this metric did not account for prevalence and consistency in the Design Repository data. To better understand our data, we developed a new metric, which we refer to as weighted confidence, to provide insight on the fidelity of the data, calculated by taking the harmonic mean of two metrics we extracted from our data, prevalence, and consistency. This method could be applied to any dataset with a wide range of individual occurrences. The contribution of this research is not to replace CFF frequency as a method of finding the most likely component-function-flow correlations but to improve the reliability of the automation results by providing additional information from the weighted confidence metric. Improving these automation results, allows us to further our ultimate objective of this research, which is to enable designers to automatically generate functional models for a product given constituent components.

TRAINING RENOVASI DAN PERAWATAN POMPA HIDRAM DI DESA KARANG PELEM, KECAMATAN KEDAWUNG, SRAGEN

Technological Pump the Hidram technology pump to use the basework style of grafitasi earth and designed in such a way till work the pumpwork automatically without there external energi for example energi oil.Constraint which often bother the [job/activity] pump the hidram is notoptimal is component function pump the effect stuffing up of dirt andcomponent damage pump. The pump Hidram equiped some kinds ofspillway, for example functioning water spillway to arrange awaking ofcurrent and awaking of pressure irrigate the , air-trap to arrange awakingof air composition in water. Especial benefit air good for condition of[isnature of water from nature of water which incompresible become semiincompresible so that punching influence irrigate the/hummer water notdestroy the material pump specially save the tekan.Tabung depress tofunction to catch and maintain the yielded water pressure. Component pumpthe hidram consisted of the Reservoir concentration, fast pipe, spillway oflimbah,balance weight, air spillway, spillway depress and save to depressand also other supporter that is pipe between. In course of evaluation[done/conducted] repair problem of water spillway using rubber of vehiclerut/rubber konveyor, and if/when reckoned from a economic side pumpthis very efficient because energi used to create the pressure irrigate tocome from laboring earth gravitation style air.Benefit from spillway irrigateand also air spillway can be arranged in such a way till work the pump canwork automatically and will labour on during balance constraint workthe pump not annoyed. From repair result got a technical age spillwaymount and stay more 6 month;moon and loss / water leakage causing tolessen the pressure can be avoided. To the condition of in such a way tillwork the pump automatically [done/conducted] a trial and error

Pfaffian differential equations over exponential o-minimal structures

In this paper, we continue investigations into the asymptotic behavior of solutions of differential equations over o-minimal structures.Let ℜ be an expansion of the real field (ℝ, +, ·).A differentiable map F = (F1,…, F1): (a, b) → ℝi is ℜ-Pfaffian if there exists G: ℝ1+l → ℝl definable in ℜ such that F′(t) = G(t, F(t)) for all t ∈ (a, b) and each component function Gi: ℝ1+l → ℝ is independent of the last l − i variables (i = 1, …, l). If ℜ is o-minimal and F: (a, b) → ℝl is ℜ-Pfaffian, then (ℜ, F) is o-minimal (Proposition 7). We say that F: ℝ → ℝl is ultimately ℜ-Pfaffian if there exists r ∈ ℝ such that the restriction F ↾(r, ∞) is ℜ-Pfaffian. (In general, ultimately abbreviates “for all sufficiently large positive arguments”.)The structure ℜ is closed under asymptotic integration if for each ultimately non-zero unary (that is, ℝ → ℝ) function f definable in ℜ there is an ultimately differentiable unary function g definable in ℜ such that limt→+∞[g′(t)/f(t)] = 1- If ℜ is closed under asymptotic integration, then ℜ is o-minimal and defines ex: ℝ → ℝ (Proposition 2).Note that the above definitions make sense for expansions of arbitrary ordered fields.