Dynamic Manufacturing Capability Assessment of Industrial Robots Based on Feedback Information in Cloud Manufacturing

2017 ◽  
Author(s):  
Zeyu Zhang ◽  
Wenjun Xu ◽  
Quan Liu ◽  
Zude Zhou ◽  
Duc Truong Pham
Keyword(s):  
Manufacturing Industry ◽  
Dynamic Assessment ◽  
Cloud Manufacturing ◽  
Industrial Robots ◽  
Assessment Model ◽  
Advanced Technology ◽  
Distribution Model ◽  
Information Feedback ◽  
Optimal Solutions ◽  
Manufacturing Capability

With the development of information and computer network technology, cloud manufacturing has been developing rapidly, industrial robots (IRs) as a vital symbol and an advanced technology of manufacturing industry, in scheduling service, the constantly changing information data will result in the corresponding vary of the manufacturing capability. Under a fixed constraint of some capability service request, this will decrease the number of the optimal solutions and provide the inaccurate service to users. So it is important to make the manufacturing capability stable and obtain more optimal solutions to satisfy the constraint, thus the dynamic assessment of manufacturing capability based on information feedback is investigated in this paper. A set of indicators is established considering the IRs’ manufacturing capability and a new dynamic assessment model is proposed to achieve the actual data and the expected data information feedback, using the “normal distribution” model, which can correct the assessment weight. By the way, a case study is simulated in the MATLAB, which shows the reliability and reasonability of this method in evaluate the manufacturing capability in IR.

scholarly journals Dynamic Assessment of Sustainable Manufacturing Capability Based on Correlation Relationship for Industrial Cloud Robotics

2021 ◽  
Author(s):  
Sisi Tian ◽  
Xiaotong Xie ◽  
Wenjun Xu ◽  
Jiayi Liu ◽  
Xiaomei Zhang
Keyword(s):  
Dynamic Assessment ◽  
Sustainable Manufacturing ◽  
Indicator System ◽  
Assessment Model ◽  
Correlation Model ◽  
Correlation Relationship ◽  
Cloud Robotics ◽  
Manufacturing Capability ◽  
Service Correlation ◽  
Industrial Cloud

Abstract The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment is the key link in determining the optimal service composition to realize the value-added of ICRs resources. However, the traditional evaluation method ignores the positive and negative cooperative effects of the manufacturing capability correlation among the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and serious environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship to solve above problems. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which the evaluation indicators raw data is processed based on the service correlation model and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in assessment of sustainable manufacturing capability for ICRs.

scholarly journals Interactive, Collaborative Robots: Challenges and Opportunities

2018 ◽  
Author(s):  
Danica Kragic ◽  
Joakim Gustafson ◽  
Hakan Karaoguz ◽  
Patric Jensfelt ◽  
Robert Krug
Keyword(s):  
Manufacturing Industry ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Service Robots ◽  
Natural Interaction ◽  
Current State ◽  
Challenges And Opportunities ◽  
Important Open Problem ◽  
In The Beginning ◽  
Office Environments

Robotic technology has transformed manufacturing industry ever since the first industrial robot was put in use in the beginning of the 60s. The challenge of developing flexible solutions where production lines can be quickly re-planned, adapted and structured for new or slightly changed products is still an important open problem. Industrial robots today are still largely preprogrammed for their tasks, not able to detect errors in their own performance or to robustly interact with a complex environment and a human worker. The challenges are even more serious when it comes to various types of service robots. Full robot autonomy, including natural interaction, learning from and with human, safe and flexible performance for challenging tasks in unstructured environments will remain out of reach for the foreseeable future. In the envisioned future factory setups, home and office environments, humans and robots will share the same workspace and perform different object manipulation tasks in a collaborative manner. We discuss some of the major challenges of developing such systems and provide examples of the current state of the art.

Cloud-Based Intelligent User Interface for Cloud Manufacturing: Model, Technology, and Application

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Lei Ren ◽  
Jin Cui ◽  
Ni Li ◽  
Qiong Wu ◽  
Cuixia Ma ◽  
...  
Keyword(s):  
User Interface ◽  
Manufacturing Industry ◽  
Multimodal Interaction ◽  
Cloud Manufacturing ◽  
Domain Specific ◽  
Intelligent User Interface ◽  
Usability Problems ◽  
Key Characteristics ◽  
Industry Practice ◽  
Service Oriented

Cloud manufacturing is gradually transforming the way enterprises do business from traditional production-oriented manufacturing to service-oriented manufacturing. The development of cloud manufacturing in industry practice is closely related to domain-specific user experience. The huge amount of users with diverse roles and various requirements in manufacturing industry are facing great challenges of cloud system usability problems. Thus, user interface issues play a significant role in pushing this new area forward. In this paper, we discuss the key characteristics of intelligent user interface (IUI) for cloud manufacturing, i.e., naturality, smart mobility, self-configuration, and flexible customization. Further, a cloud-plus-IUI model for cloud end-users is presented. Then we discuss the enabling technologies, i.e., automatic configuration based on virtualization, context-aware adaption and recommendation, and multimodal interaction. Finally, we present SketchPart, a sketch-based pad system prototype for searching part drawings in the cloud, to show the advantages of the proposed cloud-plus-IUI solution.

Enhancing the Security of Cloud Manufacturing by Restricting Resource Access

2014 ◽  
Vol 11 (4) ◽  
Author(s):  
Xiaoyun Liu ◽  
Wu He ◽  
Li Xu ◽  
Gongjun Yan
Keyword(s):  
Conflict Of Interest ◽  
Manufacturing Industry ◽  
Geographic Location ◽  
Cloud Manufacturing ◽  
Research Topic ◽  
Access Time ◽  
Secret Key ◽  
Is Security ◽  
The Right ◽  
Resource Security

AbstractCloud manufacturing has recently become a hot research topic in the manufacturing industry. One of the key bottlenecks that hinders the development and application of cloud manufacturing is security. As the adoption and use of manufacturing cloud depends on its security mechanism to a large extent, we propose a new resource security method to enhance the security of cloud manufacturing services by providing resources exclusive access to cloud virtual machine and restricting cloud access from unauthorized users. To enable authorized users in the right location, right time and right network to access resources, a GeoAuthentication model that maps geographic location, access time, and subnetwork information into a secret key is proposed. We also propose conflict firewall in manufacturing cloud to separate users with conflict of interest.

scholarly journals A risk control framework for safe manufacturing workstations in view of COVID-19

2020 ◽  
Vol 327 ◽  
pp. 03007
Author(s):  
Ebly Sanchez ◽  
Knut Åkesson
Keyword(s):  
Problem Solving ◽  
Manufacturing Industry ◽  
Close Contact ◽  
Mitigation Strategies ◽  
Assessment Model ◽  
Protection Measures ◽  
Industrial Plants ◽  
Engineering Controls ◽  
Risk Levels ◽  
Control Framework

The manufacturing industry resumes operations after the COVID-19 pandemic supported by return-to-work guidelines, which are mostly personal protection measures for the workers and employees. In this paper, we propose a framework for assessing risk at the workstation level by linking the risk levels to possible mitigation strategies that can be implemented using standard operating procedures (SOP), 5S and problem-solving. Within industrial plants, operators work in close contact with coworkers and supervisors, and they are also sharing tools and machines. It is therefore, essential to develop strategies that reduce the operator’s exposure to viruses in the workplace. The purpose of this work is that when implemented, the risk assessment model and specifically how SOP, 5S and problem solving can be used to implement administrative and engineering controls resulting in a safe workplace and increasing level of confidence for the operators working within the plant.

Adaptive Industrial Robots Using Machine Vision

2018 ◽  
Author(s):  
Vladimir Kuts ◽  
Tauno Otto ◽  
Toivo Tähemaa ◽  
Khuldoon Bukhari ◽  
Tengiz Pataraia
Keyword(s):  
Machine Vision ◽  
Manufacturing Industry ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Engineering Industry ◽  
Test Environment ◽  
Operation System ◽  
Robotic Cells ◽  
Self Learning ◽  
Human Robot Collaboration

The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.

Modeling of Resources Capability for Manufacturing Equipments in Cloud Manufacturing

2012 ◽  
Vol 271-272 ◽  
pp. 447-451 ◽  
Author(s):  
Yuan Yuan Zhao ◽  
Quan Liu ◽  
Wen Jun Xu ◽  
Lu Gao
Keyword(s):  
Web Service ◽  
Cloud Manufacturing ◽  
Enterprise Group ◽  
Functional Capability ◽  
Manufacturing Equipment ◽  
Service Platform ◽  
Production Capability ◽  
Manufacturing Capability ◽  
Proposed Model ◽  
Functional Attribute

Being a kind of actual resources, manufacturing equipment resources (MERs) need to be virtualized and encapsulated into services. Our proposed works mainly focus on manufacturing capability of MERs that is consisted of two aspects: static functional capability and dynamic production capability, and relationship between related concepts so as to model MERs by ontology web language (OWL) that is based on semantic. In this paper, firstly, ontology based methodology within manufacture field is developed according to cloud manufacturing characters. Secondly, manufacturing capability is studied from functional attribute capability and production capability, then, the related concepts classes and relationship are analyzed, with the special properties defined to describe these classes based on semantic. Thirdly, the built in model is described by OWL (ontology web language) using protégé tool and an instance of MER is built based on the proposed model to express its manufacturing capability. Finally, this model is applied to Cloud MERs service platform, which is constructed for a given enterprise group, to provide MERs services. Moreover, Web Service is used in the platform to realize the sharing of the provided services.

Strategic Impacts of Advanced Manufacturing Technology on American Textile Industry

2018 ◽  
Vol 9 (2) ◽  
pp. 54-69 ◽  
Author(s):  
Gregory W. Ulferts ◽  
Terry L. Howard ◽  
Nicholas J. Cannon
Keyword(s):  
Textile Industry ◽  
Manufacturing Industry ◽  
The United States ◽  
Global Market ◽  
Manufacturing Technology ◽  
Advanced Technology ◽  
High Rate ◽  
Labor Costs ◽  
Textile Manufacturing ◽  
The U.S

This article describes how U.S. manufacturing was stricken when companies embraced outsourcing beginning in the 1990s as a strategy for taking advantage of lower labor costs in developing countries. The U.S. textile and apparel industries lost 76.5% of its workforce, or 1.2 million jobs, between 1990 and 2012. The catalyst which has renewed the interest in manufacturing textiles and apparel in the United States is the narrowing gap between the U.S. and Asian labor costs. The sector changed in response to technology and the global market, and both the number and type of employees demanded turned as well. The advanced technology currently drives the domestic textile industry. Despite a positive outlook on growth, it is unlikely that textile manufacturing will create the large number of jobs that it did in the past. Furthermore, it is only viable because of the technological improvements to its factories. The current production is designed to employ fewer workers in order be more productive and less dependent on labor costs. Nevertheless, the high demand for specialized and unique textiles in the U.S. and Europe will likely continue to drive improved manufacturing technology and performance. China's transition from a manufacturing economy to a service economy will increase its manufacturing operational costs, while probably growing demand for the sorts of specialized textiles on which American textile manufacturers tend to focus. If such manufacturers can increase their market shares in China and other Asian countries, while maintaining such markets in the U.S. and Europe, the American textile manufacturing industry will likely grow at a moderately high rate.

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