scholarly journals Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yihai He ◽  
Zhenzhen He ◽  
Linbo Wang ◽  
Changchao Gu
Keyword(s):  
System Reliability ◽  
Preventive Maintenance ◽  
Manufacturing Process ◽  
Manufacturing System ◽  
Optimization Strategy ◽  
Reliability Modeling ◽  
Dynamic Reliability ◽  
Inspection Data ◽  
The Impact ◽  
Quality Variation

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system.

scholarly journals A New Analytic Method of Multistate Single-component System Reliability Model

2018 ◽  
Vol 175 ◽  
pp. 03058
Author(s):  
Xie Jingwei ◽  
Huang Peng ◽  
Liu Gang
Keyword(s):  
System Reliability ◽  
Preventive Maintenance ◽  
Single Component ◽  
Matrix Analysis ◽  
Phase Type Distribution ◽  
Numerical Application ◽  
Reliability Modeling ◽  
Component System ◽  
Mean Time Between Failures ◽  
Single Component System

For the reliability modeling of multistate single-component system, single maintenance bench provides the preventive maintenance and alternative maintenance services on the basis of system performance level following the stochastic detection strategy. Phase-type distribution is employed in place of exponential distribution and other typical distributions to describe the stochastic time variable in the reliability modeling process in a unified manner. Through matrix analysis, the analytic expressions for reliability indicators including system steady-state availability, mean time between failures (MTBF) and failure rate of system are obtained. A numerical application is presented to verify the applicability of the model and demonstrate the influence of preventive maintenance threshold and preventive maintenance rate on system reliability.

Integrated maintenance/spare parts management for manufacturing system according to variable production rate impacting the system degradation

2020 ◽  
Vol 28 (1) ◽  
pp. 72-84 ◽  
Author(s):  
Sofiene Dellagi ◽  
Wajdi Trabelsi ◽  
Zied Hajej ◽  
Nidhal Rezg
Keyword(s):  
Production Rate ◽  
Preventive Maintenance ◽  
Manufacturing System ◽  
Planning Horizon ◽  
Sensitivity Study ◽  
Spare Parts ◽  
Rate Variation ◽  
Maintenance Strategy ◽  
Spare Parts Management ◽  
The Impact

This study develops an analytical model in order to determine an optimal integrated maintenance plan and spare parts management. We consider a manufacturing system, producing only one type of product, over a finite planning horizon H equal to the sum of all production periods and the production quantity of each period is known. This system is subject to a continuously increasing degradation rate. That is why a preventive maintenance strategy is adopted in order to face the increasing failure rate. We noted that contrarily to the majority of studies in literature, we take into account the impact of the production rate variation on the manufacturing system degradation and consequently on the adopted optimal maintenance strategy. In addition, the real need of spare parts relative to the scheduled maintenance actions is taken into account. In fact, the purpose of our study consists at determining the optimal preventive maintenance frequency and the optimal quantity of spare parts to order by minimizing a total cost, including maintenance and spare parts management. Numerical examples are presented along with a sensitivity study in order to prove the use of the developed model for deriving the optimal integrated strategy for any instance of the problem.

scholarly journals Scalability analysis of selected structures of a reconfigurable manufacturing system taking into account a reduction in machine tools reliability

2021 ◽  
Vol 23 (2) ◽  
pp. 242-252
Author(s):  
Arkadiusz Gola ◽  
Zbigniew Pastuszak ◽  
Marcin Relich ◽  
Łukasz Sobaszek ◽  
Eryk Szwarc
Keyword(s):  
Manufacturing Process ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Production Capacity ◽  
Cost Effective ◽  
Simulation Software ◽  
Reconfigurable Manufacturing ◽  
Product Demand ◽  
The Impact

Scalability is a key feature of reconfigurable manufacturing systems (RMS). It enables fast and cost-effective adaptation of their structure to sudden changes in product demand. In principle, it allows to adjust a system's production capacity to match the existing orders. However, scalability can also act as a "safety buffer" to ensure a required minimum level of productivity, even when there is a decline in the reliability of the machines that are part of the machine tool subsystem of a manufacturing system. In this article, we analysed selected functional structures of an RMS under design to see whether they could be expanded should the reliability of machine tools decrease making it impossible to achieve a defined level of productivity. We also investigated the impact of the expansion of the system on its reliability. To identify bottlenecks in the manufacturing process, we ran computer simulations in which the course of the manufacturing process was modelled and simulated for 2-, 3-, 4- and 5-stage RMS structures using Tecnomatix Plant Simulation software.

scholarly journals Machine Learning-Based on Assessment of the Impact of the Manufacturing Process on Battery Electrode Heterogeneity

Energy and AI ◽  
2021 ◽  
pp. 100090
Author(s):  
Marc Duquesnoy ◽  
Iker Boyano ◽  
Larraitz Ganborena ◽  
Pablo Cereijo ◽  
Elixabete Ayerbe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Battery Electrode ◽  
The Impact

scholarly journals Dynamic Effect of Perfect Preventive Maintenance on System Reliability and Cost Using HiP-HOPS

2010 ◽  
Vol 43 (17) ◽  
pp. 204-209 ◽  
Author(s):  
Shawulu H. Nggada ◽  
David J. Parker ◽  
Yiannis I. Papadopoulos
Keyword(s):  
System Reliability ◽  
Preventive Maintenance ◽  
Dynamic Effect

scholarly journals The impact of molded pulp product process on the mechanical properties of molded Bleached Chemi-Thermo-Mechanical Pulp

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Claire Dislaire ◽  
Yves Grohens ◽  
Bastien Seantier ◽  
Marion Muzy
Keyword(s):  
Mechanical Properties ◽  
Water Uptake ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Water Immersion ◽  
Young Modulus ◽  
Drying Time ◽  
Mechanical Pulp ◽  
Hygroscopic Properties ◽  
The Impact

AbstractThis study was carried out using bleached softwood Chemi-Thermo-Mechanical Pulp to evaluate the influence of Molded Pulp Products’ manufacturing process parameters on the finished products’ mechanical and hygroscopic properties. A Taguchi table was done to make 8 tests with specific process parameters such as moulds temperature, pulping time, drying time, and pressing time. The results of these tests were used to obtain an optimized manufacturing process with improved mechanical properties and a lower water uptake after sorption analysis and water immersion. The optimized process parameters allowed us to improve the Young’ Modulus after 30h immersion of 58% and a water uptake reduction of 78% with the first 8 tests done.

Experimental Assessment of Roughness Changes in the Cutting Surface and Microhardness Changes of the Material S 355 J2 G3 after Being Cut by Non-Conventional Technologies

2011 ◽  
Vol 314-316 ◽  
pp. 1944-1947 ◽  
Author(s):  
Jozef Maščeník ◽  
Stefan Gaspar
Keyword(s):  
Mechanical Properties ◽  
Production Process ◽  
Laser Plasma ◽  
Manufacturing Process ◽  
Production Quality ◽  
Water Jet ◽  
Cutting Edge ◽  
Experimental Assessment ◽  
Cutting Surface ◽  
The Impact

Production of components, necessary for the construction of the machine resp. or device is a demanding manufacturing process. One of the possibilities of increasing efficiency and production quality is the introduction of unconventional technologies to the production process. Knowing the dependence of the impact of non-conventional technologies on the mechanical properties of products and their subsequent verification is an important aspect when designing and manufacturing them. The article deals with the impact of used unconventional technology, that means laser, plasma and water jet on the roughness of a cutting edge and microhardness of material S 355 J2 G3.

Bayesian Networks Application in Multi-State System Reliability Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2590-2595 ◽  
Author(s):  
Sheng Zhai ◽  
Shu Zhong Lin
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
System Reliability ◽  
Prior Probability ◽  
State System ◽  
Weak Links ◽  
Uncertain Reasoning ◽  
Reliability Modeling ◽  
Line System ◽  
Reliability Indices

Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

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