Knowledge-Integration Model for Networked Manufacturing

2011 ◽  
Vol 314-316 ◽  
pp. 2027-2032
Author(s):  
Jiao Jian Liu ◽  
Wen He Liao ◽  
Yu Guo ◽  
Wen Bin Wang
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Knowledge Sharing ◽  
Manufacturing Process ◽  
Knowledge Integration ◽  
Decision Making Process ◽  
Networked Manufacturing ◽  
Optimum Distribution ◽  
Integration Model ◽  
Key Technologies

In order to maximize knowledge sharing and reuse in networked manufacturing process and improve the rapidity and reliability of decision-making, a knowledge-integration model and its implementation methods are proposed in this paper. First, the requirement for knowledge integration in networked manufacturing is analyzed. On this basis, a knowledge-integration model is built, and then three key technologies are studied, namely knowledge representation and organization based on ontology, knowledge correlation analysis based on complex network and knowledge supply based on decision-making context. This model provides an effective way to realize the optimum distribution of knowledge in networked manufacturing process and to improve the efficiency of decision-making process.

scholarly journals Profiling mental health professionals in relation to perceived interprofessional collaboration on teams

2019 ◽  
Vol 7 ◽  
pp. 205031211984146
Author(s):  
Nicolas Ndibu Muntu Keba Kebe ◽  
François Chiocchio ◽  
Jean-Marie Bamvita ◽  
Marie-Josée Fleury
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Knowledge Sharing ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Knowledge Integration ◽  
Interprofessional Collaboration ◽  
Professional Role ◽  
Mutual Trust ◽  
Participation In Decision Making

Objectives: This study aims at identifying profiles of mental health professionals based on individual, interactional, structural and professional role characteristics related to interprofessional collaboration. Methods: Mental health professionals ( N = 315) working in primary health care and specialized mental health teams in four Quebec local service networks completed a self-administered questionnaire eliciting information on individual, interactional, structural and professional role characteristics. Results: Cluster analysis identified four profiles of mental health professionals. Those with the highest interprofessional collaboration scores comprised two profiles labeled “highly collaborative female professionals with fewer conflicts and more knowledge sharing and integration” and “highly collaborative male professionals with fewer conflicts, more participation in decision-making and mutual trust.” By contrast, the profile labeled “slightly collaborative professionals with high seniority, many conflicts and less knowledge integration and mutual trust” had the lowest interprofessional collaboration score. Another profile positioned between these groups was identified as “moderately collaborative female psychosocial professionals with less participation in decision-making.” Discussion and conclusion: Organizational support, participation in decision-making, knowledge sharing, knowledge integration, mutual trust, affective commitment toward the team, professional diversity and belief in the benefits of interdisciplinary collaboration were features associated with profiles where perceived interprofessional collaboration was higher. These team qualities should be strongly encouraged by mental health managers for improving interprofessional collaboration. Training is also needed to promote improvement in interprofessional collaboration competencies.

scholarly journals Optimization of Machining Parameters for Product Quality and Productivity in Turning Process of Aluminum

2021 ◽  
Vol 26 ◽  
Author(s):  
Sepideh Abolghasem ◽  
Nicolás Mancilla-Cubides
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Material Removal Rate ◽  
Manufacturing Process ◽  
Material Removal ◽  
Removal Rate ◽  
Decision Making Process ◽  
Machining Parameters ◽  
Turning Process ◽  
Productivity Rate

Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products.

scholarly journals Application of Artificial Intelligence Technology in Decision-Making of Mechanical Manufacturing Process

2021 ◽  
Vol 2143 (1) ◽  
pp. 012008
Author(s):  
Zhanfeng Li
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Manufacturing Process ◽  
Social Life ◽  
Raw Materials ◽  
Decision Making Process ◽  
Method Performance ◽  
Deformation Amount ◽  
Artificial Intelligence Technology ◽  
Basic Production

Abstract The traditional mechanical manufacturing process is to transform all raw materials into the final materials and products and directly into the international market all the production process, in this process we involved a lot of problems about decision-making methods, decision-making process is a most basic production technology activity, it is widely exists in the whole social life and each link of enterprise production. This paper studies the decision-making method of mechanical manufacturing process based on artificial intelligence, optimizes the process parameters of plastic integrated mechanical manufacturing process, and compares it with the traditional decision-making method. Finally, the experimental results are obtained that the traditional decision-based method is reduced by more than 10% in size error. But several experiments, the AI decision-making method appeared deviation, the error results are higher than the traditional decision-making method, which may be objective factors, but also reflects the possibility of instability, in the result of deformation. AI-based decision method performance is higher than the traditional decision-making method, reduce the deformation amount by 3.5%

Manufacturing Process Specification and Collaborative Decision-Making

2012 ◽  
Vol 251 ◽  
pp. 79-83
Author(s):  
Li Hong Qiao ◽  
Shu Ting Kao ◽  
Yi Xin Zhu
Keyword(s):  
Decision Making ◽  
Manufacturing Process ◽  
Process Model ◽  
Information Model ◽  
Information Modeling ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Process Information ◽  
Process Plan ◽  
Collaborative Decision

A majority of information in manufacturing is the process information which presents mobility. Such information reflects transforming process form materials to products. This research establishes a manufacturing process information model that presents uniqueness, validness, completeness and generality based in PSL. The model can provide necessary process information and related product, material and resource information required by systems for integrating and collaborative decision making. It then enables to build a collaborative manufacturing decision making process model which is capable of utilizing the process information model. By defining logic procedure, data wrapping and service invoking in the decision making process model, multi-manufacturing systems collaborative decision making will be implemented. Such method of manufacturing process information modeling and decision making process modeling is illustrated by examples of the collaborative creation of a part process plan and an assembly process plan considering actual manufacturing environment.

AAC Decision-Making and Mobile Technology: Points to Ponder

2014 ◽  
Vol 23 (2) ◽  
pp. 104-111 ◽  
Author(s):  
Mary Ann Abbott ◽  
Debby McBride
Keyword(s):  
Decision Making ◽  
Mobile Technology ◽  
Communication System ◽  
Augmentative And Alternative Communication ◽  
Evaluation Process ◽  
Decision Making Process ◽  
Alternative Communication ◽  
Ipod Touch ◽  
Feature Match

The purpose of this article is to outline a decision-making process and highlight which portions of the augmentative and alternative communication (AAC) evaluation process deserve special attention when deciding which features are required for a communication system in order to provide optimal benefit for the user. The clinician then will be able to use a feature-match approach as part of the decision-making process to determine whether mobile technology or a dedicated device is the best choice for communication. The term mobile technology will be used to describe off-the-shelf, commercially available, tablet-style devices like an iPhone®, iPod Touch®, iPad®, and Android® or Windows® tablet.

Women in Power and Decision making Positions : Conditions and Restraints

1970 ◽  
pp. 53-57
Author(s):  
Azza Charara Baydoun
Keyword(s):  
Decision Making ◽  
Political Participation ◽  
Political Life ◽  
The Political ◽  
Decision Making Process ◽  
Power Structures ◽  
Policies And Procedures ◽  
The Social ◽  
The Status ◽  
Slow Progress

Women today are considered to be outside the political and administrative power structures and their participation in the decision-making process is non-existent. As far as their participation in the political life is concerned they are still on the margins. The existence of patriarchal society in Lebanon as well as the absence of governmental policies and procedures that aim at helping women and enhancing their political participation has made it very difficult for women to be accepted as leaders and to be granted votes in elections (UNIFEM, 2002).This above quote is taken from a report that was prepared to assess the progress made regarding the status of Lebanese women both on the social and governmental levels in light of the Beijing Platform for Action – the name given to the provisions of the Fourth Conference on Women held in Beijing in 1995. The above quote describes the slow progress achieved by Lebanese women in view of the ambitious goal that requires that the proportion of women occupying administrative or political positions in Lebanon should reach 30 percent of thetotal by the year 2005!

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