Resilience Measures of Manufacturing Systems Under Disruptions

2014 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Production Condition ◽  
Unreliable Machines ◽  
System A ◽  
Multi Stage ◽  
Stage System ◽  
Normal Production ◽  
System Resilience ◽  
Production Losses

Unexpected disruptive events always interrupt normal production condition and cause production losses in the manufacturing system. A resilient system is capable of settling itself to the steady-state quickly after the disruption, and compensating for the lost production by using a relatively little overtime. In this paper, we define throughput settling time (TST) and overtime to recover (OTTR) as two resilience measures to analyze multi-stage serial-parallel systems with unreliable machines and finite intermediate buffers. We perform an exact analysis for a two-stage system and develop an approximation method for general multi-stage systems. Numerical case studies are conducted to investigate the system resilience under different configurations.

Modelling and Design of Closed-Loop Manufacturing Systems

2013 ◽  
Vol 378 ◽  
pp. 367-374 ◽  
Author(s):  
Andrey A. Kutin ◽  
Mikhail Turkin
Keyword(s):  
Analytical Method ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Closed Loop ◽  
Production Systems ◽  
Finite Buffers ◽  
Unreliable Machines

This paper introduces an analytical method for evaluating the performance of closed loop manufacturing systems with unreliable machines and finite buffers. The method involves transforming an arbitrary loop into one without thresholds and then evaluating the transformed loop using a new set of decomposition equations. It is more accurate than existing methods and is effective for a wider range of cases. The convergence reliability, and speed of the method are also discussed. In addition, observations are made on the behavior of closed loop production systems under various conditions. Finally, the method is used in a case study to design a flexible manufacturing system for production of aerospace parts.

Mission Reliability Modeling of Manufacturing Processes and System

2012 ◽  
Vol 248 ◽  
pp. 450-455 ◽  
Author(s):  
Yan Chao Liu ◽  
Wen Jun Zhang ◽  
Gui Cui Fu ◽  
Nan Li
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Quality Parameters ◽  
Reliability Modeling ◽  
Inspection Errors ◽  
System A ◽  
Modeling Process ◽  
Discrete Manufacturing System ◽  
Two Factors ◽  
Quality Of Products

Mission reliability model is important for evaluation of the production capability of a manufacturing system. The manufacturing system mission reliability refers to the ability that the manufacturing system completes the production mission under specified conditions and within the specified time. The production mission includes two factors: quality of products and the productivity. Calculation of traditional evaluation parameters like Cpk and Ppk excludes the abnormal interruption of production and inspection errors. For some manufacturing systems with low degree of automation in the field of domestic weapons, both production disruptions and misjudgments in inspection processes have an impact on the mission reliability of the manufacturing system. A method of mission reliability modeling of discrete manufacturing system is proposed in this paper. The manufacturing system is composed of several processes. Abnormal interruption of production, inspection errors and substandard quality parameters of the products are involved in the modeling of the process mission reliability. The sequential and concurrent relationship between the processes is also taken into account in the modeling process.

Digital Twin of manufacturing systems: a case study on increasing the efficiency of reconfiguration

2020 ◽  
Vol 68 (6) ◽  
pp. 435-444 ◽  
Author(s):  
Behrang Ashtari Talkhestani ◽  
Michael Weyrich
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Added Value ◽  
Future Research ◽  
System Availability ◽  
Qualitative And Quantitative ◽  
Digital Twin ◽  
System A ◽  
Set Up

AbstractThe added value of a Digital Twin for reconfiguring manufacturing systems promises an increase in system availability, a reduction in set-up and conversion times and enables the manufacturing of customer-specific products. To evaluate this claim, this paper selects an architecture of the Digital Twin and realizes it on the basis of an application scenario for a cyber-physical manufacturing system. A case study is used to test the reconfiguration of a manufacturing system by comparing two different methods, one without and one with use of the Digital Twin. In this paper, the process steps of both reconfigurations are described and discussed in detail and a qualitative and quantitative evaluation of the reconfiguration results is presented. Finally, this paper gives an outlook on future research on intelligent automation of manufacturing systems using the Digital Twin.

CONCEPTUAL MODEL OF THE PHYSICAL STRUCTURE OF MANUFACTURING SYSTEMS

2006 ◽  
Vol 16 (04) ◽  
pp. 643-651
Author(s):  
MYRIAM NOUREDDINE
Keyword(s):  
Conceptual Model ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Logical Structure ◽  
Physical Structure ◽  
Second Step ◽  
Abstraction Level ◽  
System A ◽  
Simple Format ◽  
High Abstraction Level

This paper deals with the generation of a conceptual model of the physical structure of any manufacturing system. The obtained conceptual model shows a clear and linear view of a given manufacturing system. A generic notation is used to guarantee the scalability and the portability of the model. This model maintains a high abstraction level without ambiguity and in a simple format. The generation of a conceptual model for a given manufacturing system is obtained through two steps. The first step describes both the physical structure and the logical structure of the manufacturing system. The second step gives the generation principle of the conceptual model. The approach is illustrated using an example.

Dynamic Maintenance Strategy With Q-Learning for Workstations in a Flow Line Manufacturing System

2010 ◽  
Author(s):  
Sagar Kamarthi ◽  
Abe Zeid ◽  
Yusuf Ozbek
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flow Line ◽  
Maintenance Cost ◽  
Q Learning ◽  
Intelligent Maintenance ◽  
Dynamic Maintenance ◽  
Maintenance Systems ◽  
Modern Manufacturing ◽  
Production Losses

Every machine or equipment in a manufacturing facility is subject to failure due to deterioration based on cumulative wear, crack growth, erosion, etc. This failure will cause production losses and delays resulting in high costs. As the modern manufacturing systems are getting more and more complex, intelligent maintenance schemes must replace the old labor intensive planned maintenance systems to ensure that equipment continues to function. If the maintenance decision is based on the state of the system rather than its age, this leads to the choice of a Condition Based Maintenance (CBM) policy to prevent catastrophic unexpected machine breakdowns and increase the availability of individual machines, but it also introduces randomness into the manufacturing operation. This paper presents a Q-Learning model to dynamically group maintenance actions on different machines and execute them simultaneously, so that one can reduce maintenance cost and increase the efficiency of the manufacturing system.

Danish Experience with Emergent Hydrophyte Treatment Systems (EHTS) and Prospects in the Light of Future Requirements on Outlet Water Quality

1990 ◽  
Vol 22 (3-4) ◽  
pp. 65-72 ◽  
Author(s):  
H.-H. Schierup ◽  
H. Brix
Keyword(s):  
Wastewater Treatment ◽  
Total Nitrogen ◽  
Total Phosphorus ◽  
Wastewater Treatment Plants ◽  
Sewage Treatment ◽  
Reed Beds ◽  
Effluent Quality ◽  
Multi Stage ◽  
Run Off ◽  
Stage System

Since 1983 approximately 150 full-scale emergent hydrophyte based wastewater treatment plants (reed beds) have been constructed in Denmark to serve small wastewater producers. The development of purification performance for 21 plants representing different soil types, vegetation, and hydraulic loading rates has been recorded. Cleaning efficiencies were typically in the range of 60-80% reduction for BOD, 25-50% reduction for total nitrogen, and 20-40% reduction for total phosphorus. The mean effluent BOD, total nitrogen and total phosphorus concentrations of the reed beds were 19 ± 10, 22 ± 9 and 6.7 ± 3.2 mg/l (mean ± SD), respectively. Thus, the general Danish effluent standards of 8 mg/l for N and 1.5 mg/l for P for sewage plants greater than 5,000 PE cannot be met by the present realised design of EHTS. The main problem observed in most systems is a poor development of horizontal hydraulic conductivity in the soil which results in surface run-off. Since the political demands for effluent quality will be more strict in the future, it is important to improve the performance of small decentral sewage treatment plants. On the basis of experiences from different types of macrophyte based and conventional low-technology wastewater treatment systems, a multi-stage system is suggested, consisting of sedimentation and sand filtration facilities followed by basins planted with emergent and submergent species of macrophytes and algal ponds.

scholarly journals Research on Double-Stage and Multi-Stage Capacitive Deionization Absorption Air-Conditioning System

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 395
Author(s):  
Feng Cheng ◽  
Boqing Ding ◽  
Xiuwei Li
Keyword(s):  
Air Conditioning ◽  
Pure Water ◽  
Good Choice ◽  
Coefficient Of Performance ◽  
Capacitive Deionization ◽  
Joint Work ◽  
Thermal Regeneration ◽  
Air Conditioning System ◽  
Multi Stage ◽  
Stage System

An absorption air-conditioning system is a good choice for green buildings. It has the superiority in the utilization of renewable energy and the refrigerant is environment-friendly. However, the performance of the traditional absorption system has been restricted by the energy waste in the thermal regeneration process. Capacitive deionization (CDI) regeneration is proposed as a potential method to improve system efficiency. In the new method-based air-conditioning system, strong absorbent solutions and pure water are acquired with the joint work of two CDI units. Nevertheless, the practical CDI device is composed of a lot of CDI units, which is quite different from the theoretical model. To reveal the performance of multiple CDI units, the model of the double/multi-stage CDI system has been developed. Analysis has been made to expose the influence of some key parameters. The results show the double-stage system has better performance than the single-stage system under certain conditions. The coefficient of performance (COP) could exceed 4.5, which is higher than the traditional thermal energy-driven system, or even as competitive as the vapor compression system. More stages with proper voltage distribution better the performance. It also provides the optimization method for the multi-stage CDI system.

scholarly journals A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

2021 ◽  
Vol 18 (4) ◽  
pp. 1662
Author(s):  
Mohammed Alkahtani ◽  
Muhammad Omair ◽  
Qazi Salman Khalid ◽  
Ghulam Hussain ◽  
Imran Ahmad ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Production Rate ◽  
Manufacturing System ◽  
Working Environment ◽  
Data Set ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Ripple Effects ◽  
Multi Stage

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

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