Toward a Modeling Framework for Organizational Competency

Part 6: Computational Systems ApplicationsInternational audienceCompetency modeling framework serves as a; (a) very important basis for the explanation of a generic competency modeling approach, (b) base element in the consolidation of existing knowledge in this area, (c) tool for model developers on selecting appropriate competency models, and (d) basis for competency modeling. This research uses literature review approach to propose a modeling framework for organizational competency. The proposed modeling framework has been developed based on the most relevant well known competency models. The research suggests that organizational competency can be categorized into three groups; individual competency, enterprise competency and collaboration-oriented competency. For modeling each of these groups, it is essential that the modeling process have to be aligned with model developer purpose (Modeling perspective), thus the model developing process will be based on the same segmentation model. Furthermore, competencies have to be model at different levels of abstraction (modeling intent)

A manufacturing model to enable knowledge maintenance in decision support systems

The product development process, within a typical manufacturing company, utilises huge amounts of knowledge related to manufacturing and design activities. Knowledge based systems are increasingly being used to support manufacturing and design decisions. These systems are important tools for obtaining a competitive advantage and leverage using company "know-how". However, it is important to define suitable knowledge structures in the creation of these decision support systems. Due to the significant volume of knowledge generated in the manufacturing and design stage, there is a need to create structures and methods that readily manage and maintain the knowledge in order to a) assure the long-term use of these systems b) improve the company's competitiveness. The research reported in this thesis explores and defines a Manufacturing Facility Information and Knowledge Model (MFIKM) allowing a) the ability to store and manage various types of knowledge, b) the capturing of valuable new knowledge using a knowledge maintenance method. The understanding of an information and knowledge infrastructure using different types of knowledge categorisation has been explored. The major emphasis has been placed on understanding the facility knowledge structure related to processes and resources supporting process planning decisions. Using a knowledge maintenance life cycle as a method to maintain knowledge, it was possible to capture new and valuable machining knowledge using different types of representations. Knowledge models and methods are essential in the definition of structures to support manufacturing decisions allowing knowledge management and maintenance. It has been shown that the knowledge structures defined for the new model can serve as a source and repository for different types of knowledge allowing the support of manufacturing decisions with up-to-date knowledge. The framework defined enables the structuring of facility knowledge, processes, and resources, as super classes; improving the understanding of the relationships and dependencies among them, and allowing accessibility depending on the characteristics of each. A UML tool helped in the creation of new structures detailing attributes for the classes defined. An experimental system has been implemented using the object-oriented database ObjectStore© and the Visual C++ programming environment. The MFIKM has been explored using scenarios from machining knowledge to successfully demonstrate the feasibility of knowledge maintenance supporting process planning decisions using the knowledge structures defined.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Energy consumption parameter analysis of industrial robots using design of experiment methodology

During the last decade, industrial robot installations have increased greatly, contributing to higher energy consumption at the process level. To align with Industry 4.0 objectives to reduce energy consumption, further Industrial Internet of Things, and advance Smart Manufacturing, this paper explores energy consumption optimisation of process-level industrial robots through Design and Analysis of Experiment methodologies. The operating parameters of a Kawasaki ZZX130L, 6 DOF model industrial robot, investigated herein are speed, acceleration, payload, and temperature using a linear factorial experiment analysis. It is this paper’s goal to determine which of the explored parameters contribute most to energy consumption. Statistical analysis of data was conducted using Minitab 19, an industry standard tool, and it was found that linear speed and acceleration contributed to nearly 95% of energy consumption in any of the first three joints of the Kawasaki robot, with the other factors, payload and temperature, contributing the rest. It remains important to explore other factors contributing to EC and seek to minimise them in any way possible

Enterprise Competency Modelling in Practice-an Exploratory Case Study

AbstractCompetency modelling is a standardized way to found an enterprise area of experts. Identifying and managing competences acquired by an enterprise and further representing them in a structured manner provide important knowledge for ‘know-how’ approach. The purpose of this paper is developing a competency based knowledgebase for an enterprise using case study approach. The developed competency knowledgebase for the case study provides information important to decision-making, and can act as an indicator for an enterprise's willingness to engage in robust collaboration

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies

An Energy Consumption Approach in a Manufacturing Process using Design of Experiments

Modern manufacturing facilities are facing several challenges, such as increasing demand of products with higher flexibility created in the shortest time. Manufacturers must also deal with the efficient use of energy, emission reduction and comply with increasing requirements in sustainability, leading to the development of more efficient processes and systems. A novel environmentally benign manufacturing approach is presented, where production processes and systems move towards a reduced carbon footprint impact. At the factory level, especially in machining, nearly 90% of carbon footprints occur due to the electricity demands of machine tools. At the machining stage, electrical demand is associated with machine start-to-stop, and significantly higher amounts of non-cutting energy are consumed compared with the actual material removal energy in end-milling, resulting in a low efficiency process. The purpose of this paper is to explore machining strategies by analysing energy consumption using Design of Experiments at the material removal rate, to compare cutting trajectories according to parameters, such as spindle speed, feed rate, depth of cut per pass and total depth of cut. It is essential to investigate how different geometrical designs and machining parameters can influence energy consumption in milling operations, and seek potential ways to minimize it

Digital twin using Siemens PLCS and PLM software ::a manufacturing material system case study

In this paper a digital twin of a manufacturing material system will be built in Tecnomatix-Siemens PLM (Product Life-cycle Management) software. The virtual cell will interact with a Programmable Logic Controller (PLC), specifically the Siemens S7-1500 Digital manufacturing tools are important new technologies to support the creation of complex manufacturing processes. The present work will guide not only the creation of the virtual prototype, but also to validate the PLC code inside the virtual model. The purpose of this is to enable faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs. The virtual environment allows us to test various programs in different scenario situations and check for defects before it is implemented on the physical system

Intelligent E-commerce logistics platform using hybrid agent based approach

This paper proposes a hybrid agent-based approach for the scheduling and synchronization of e-commerce logistics parks (EcLP). This is accomplished by integrating intelligent distribution centers within the e-commerce environment. The proposed platform has been developed based on an agent technology, which not only serves for decentralization and synchronization purposes but also it has been optimized for the transportation and logistics of the overall system. Moreover, mobile agent-based communication mechanisms between the hardware agents and the software agents were developed, and the proposed hybrid agent-based platform was implemented and tested based on a case study. Following this, the results were compared to a conventional system based on four main indicators

Blockchain-based ubiquitous manufacturing: a secure and reliable cyber-physical system

With product customisation and emerging business opportunities, small and medium manufacturing enterprises (SMEs) must find ways to collaborate and share competency in a trustable manner to survive a turbulent market. Therefore, service industry turns to the manufacturing industry and SMEs migrate to cloud manufacturing (CM) and ubiquitous manufacturing. However, existing platforms use centralised networking, which suffers from security, scalability and big-data problems. In this paper, we propose a blockchain-based platform as a trustable network to eradicate third-party problems, which can improve the scalability, security and big-data problems for SMEs. Our proposed platform is developed based on a consortium blockchain which provides a peer-to-peer communication network between the end user and the service provider. We improve existing consensus mechanism and communication protocol based on a cyber-physical system (CPS), via an autonomous agent. Firstly, we provide a review of cloud manufacturing, ubiquitous manufacturing and blockchain-based manufacturing approaches by highlighting the main problems. Then, the proposed platform, blockchain ubiquitous manufacturing (BCUM), is explained, based on its architecture, consensus algorithm and CPS, with the help of autonomous agent communication. The proposed platform has been developed for 3D printing companies which are geographically distributed and tested based on network performance and three practical scenarios

An approach to develop a digital twin for industry 4.0 systems: manufacturing automation case studies

The new paradigm of digital manufacturing and the concept of Industry 4.0 has led to the integration of recent manufacturing advances with modern information and communication technologies. Therefore, digital simulation tools fused into production systems can improve time and cost-effectiveness and enable faster, more flexible, and more efficient processes to produce higher-quality goods. The advancement of digital simulation with sensory data may support the credibility of production systems and improve the efficiency of production planning and execution processes. In this paper, an approach is proposed to develop a Digital Twin of production systems in order to optimize the planning and commissioning process. The proposed virtual cell interacts with the physical system with the help of different Digital Manufacturing Tools (DMT), which allows for the testing of various programs in a different scenario to check for any shortcomings before it is implemented on the physical system. Case studies from the different production systems are demonstrated to realize the feasibility of the proposed approach