scholarly journals Demand Prediction and Allocation Optimization of Manufacturing Resources

2021 ◽  
Vol 20 (4) ◽  
pp. 790-801
Author(s):  
J. L. Meng
Keyword(s):  
Demand Prediction ◽  
Manufacturing Resources

scholarly journals Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

2021 ◽  
Vol 11 (7) ◽  
pp. 3188
Author(s):  
Xixiang Wang ◽  
Jiafu Wan
Keyword(s):  
Knowledge Sharing ◽  
Comparative Experiment ◽  
Mixed Flow ◽  
Processing Technologies ◽  
Experiment Platform ◽  
Low Degree ◽  
Manufacturing Resources ◽  
Ontological Model ◽  
Manufacturing Environments ◽  
Similar Processing

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform.

scholarly journals Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-On-A-Chip-Devices

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1320
Author(s):  
Julia Sophie Böke ◽  
Daniel Kraus ◽  
Thomas Henkel
Keyword(s):  
Fluid Management ◽  
Lab On A Chip ◽  
Microfluidic System ◽  
Flow Rates ◽  
Demand Prediction ◽  
Fast Running ◽  
Microfluidic Network ◽  
Fluid Properties ◽  
Network Simulations ◽  
User Friendly

Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.

scholarly journals Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

2021 ◽  
Vol 13 (12) ◽  
pp. 6596
Author(s):  
Riccardo Ceccato ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Travel Demand ◽  
Risk Mitigation ◽  
Great Part ◽  
Mitigation Measures ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Demand Prediction ◽  
Travel Decisions ◽  
The University

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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