Innovative Design of Manufacturing Systems

1999 ◽  
Author(s):  
Val Tsourikov ◽  
Igor Devoino
Keyword(s):  
Functional Analysis ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Qualitative Data ◽  
Semantic Knowledge ◽  
Concept Selection ◽  
Innovative Design ◽  
Knowledge Based ◽  
Semantic Knowledge Base ◽  
Innovative Concept

Abstract This paper focuses on a knowledge-based approach to innovative design of manufacturing systems and processes. Innovative design includes three major steps: Functional Analysis of the manufacturing process; Innovative Concept Search in the semantic knowledge base of technical and scientific effects and New Concept selection using quantitative as well as qualitative data.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

scholarly journals Ontology-Driven Knowledge-Based Health-Care System, An Emerging Area – Challenges And Opportunities – Indian Scenario

2014 ◽  
Vol XL-8 ◽  
pp. 239-246 ◽  
Author(s):  
A. Sunitha ◽  
G. Suresh Babu
Keyword(s):  
Decision Making ◽  
Semantic Knowledge ◽  
Public Healthcare ◽  
Policy Makers ◽  
Knowledge Based ◽  
Healthcare Decision Making ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Decision Making System ◽  
Semantic Knowledge Base

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2020 ◽  
pp. 1097-1120
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael S. Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

scholarly journals A Review of Engineering Research in Sustainable Manufacturing

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Karl R. Haapala ◽  
Fu Zhao ◽  
Jaime Camelio ◽  
John W. Sutherland ◽  
Steven J. Skerlos ◽  
...  
Keyword(s):  
Decision Making ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Sustainable Manufacturing ◽  
Supply Chain Design ◽  
Planning And Scheduling ◽  
Engineering Research ◽  
Challenges And Opportunities ◽  
Recent Developments ◽  
Planning Development

Sustainable manufacturing requires simultaneous consideration of economic, environmental, and social implications associated with the production and delivery of goods. Fundamentally, sustainable manufacturing relies on descriptive metrics, advanced decision-making, and public policy for implementation, evaluation, and feedback. In this paper, recent research into concepts, methods, and tools for sustainable manufacturing is explored. At the manufacturing process level, engineering research has addressed issues related to planning, development, analysis, and improvement of processes. At a manufacturing systems level, engineering research has addressed challenges relating to facility operation, production planning and scheduling, and supply chain design. Though economically vital, manufacturing processes and systems have retained the negative image of being inefficient, polluting, and dangerous. Industrial and academic researchers are re-imagining manufacturing as a source of innovation to meet society's future needs by undertaking strategic activities focused on sustainable processes and systems. Despite recent developments in decision making and process- and systems-level research, many challenges and opportunities remain. Several of these challenges relevant to manufacturing process and system research, development, implementation, and education are highlighted.

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