GRA and ANN Integrated Approach for Photochemical Machining of Al/SiC Composite

Agrawal, Devendra; D.N, Kamble

doi:10.1016/j.matpr.2017.07.044

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In addition, the HFACS model of accident investigations is also an important qualitative approach to understanding and managing facility safety that is used by some government departments, such as the United States Coast Guard and the US Department of Defense [18]. To better understand the potential safe implications for avoiding the risks of picking and material-handling accidents in a warehouse facility [21], the HFACS model is used by this article and is described in detail as follows, including the five levels of human negligence [11,20,21]:…”

Section: Human Factors Analysis and Classification System (Hfacs)mentioning

confidence: 99%

“…The obtained data are usually expressed as the specific range of a grey interval number and are the upper and lower limits of the information separately. GRA-associated equations are described as follows [17,18]. Step 1.…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

“…It is a comparison-based method that conducts the comparisons in a particularly structured way by which less information is required, and the comparisons are also more consistent [16]. In addition, gray relational analysis (GRA) [17], a normalization evaluation technique, is extended to solve the complicated multi-performance optimization, whose theory is based on the random uncertainty of small samples developed into an evaluation technique to solve certain complex problems [18]. In the field of risk management of reliability and security, failure modes and effects analysis (FMEA) has been an important approach to find the risk priority number (RPN) based on four indicators, which comprise the severity of the failure effect (S), the probability of occurrence of the failure mode (O), the probability of failure detection (D), and the expected cost (E) [19].…”

Section: Introductionmentioning

confidence: 99%

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Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

2023

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Warehouse accidents have become of wide concern to the public because they often cause heavy economic losses and heavy casualties. Therefore, it is critical to foster the safety and reliability of warehouse facilities to reduce accidents. However, in the relevant literature, the increased safety of warehouse facilities has seldom been researched. This is a main research gap that the authors would like to supplement with the aim of enhancing warehouse facility reliability to improve risk management in a comprehensive and reliable way. The objectives of this research article are to collect the critical risks from the relevant literature and interviewees, to calculate the weights of four critical indicators (O, S, D, and E) by the BWM approach, and to rank the critical risks of picking and material-handling accidents in a warehouse facility through GRA, the HFACS model, and the FMEA approach. The ranking results show that cost is the most essential element and the expected cost > detection > severity > occurrence, according to the average value of experts’ comprehensive consideration of importance. Next, GRA is used to prioritize the critical risks of picking and material-handling accidents. The main contribution is that we identify 18 major critical risk accidents of the HFACS risk categories and make up for the insufficiency of past research that lacks an empirical analysis of the risks of picking and material-handling accidents in warehouse facilities.

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Section: Human Factors Analysis and Classification System (Hfacs)mentioning

confidence: 99%

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

2023

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“…Ramulu [6] optimized the wear rate of tool, material removal rate and roughness of surface in EDM of aluminium diboride which is reinforced with aluminium reinforced LM25 functionally graded composites. Agarwal et al [7] developed a hybrid (GRA-ANN) model to predict optimal setting of Electrochemical machining(ECM) parameters with highest GRG value for Material removal rate, under cut and Etch factor. The ANN component also delivers the acceptable performance for trends investigation for specified set of tests.…”

Section: Previous Workmentioning

confidence: 99%

Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites

Kharwar¹,

Verma²

2019

FME Transactions

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Glass fibre reinforced epoxy polymers (GFRP) composites have gathered enormous attraction because of their exceptional engineering properties such as superior proportion in strength-to-weight and enhanced durability. However, to develop a machined component is a difficult task due to nonhomogeneity and anisotropic behavior of GFRP. In this study a hybrid module, Grey relational analysis (GRA) embedded in artificial neural network (ANN) based on Taguchi approach for multicriteria optimization in Turning of GFRP materials has been carried out. The desired machining characteristics are minimum cutting force, minimum surface roughness & maximum rate of material removal & these have been used to calculate the Grey relational coefficient for individual response and then converted into the single response function i.e. Grey relational grade (GRG) value which was used for converting multiobjective problem into single objective problem and then well-trained ANN based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm has been used to predict the most favourable process parameters setting i.e. spindle speed (880 rpm), feed rate (0.05 mm/rev.), depth of cut (0.4 mm). The predicted GRG of process parameters via L 16 OA of Taguchi-based GRA with ANN has been improved by 8.5%.This setting has been selected based on highest GRG predicted by well-trained ANN which finally has been checked by the confirmatory test which produces satisfactory results.

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“…Their results were improved by using the above approaches [16]. Agarwal et al developed a hybrid (GRAANN) model to predict optimal setting of electrochemical machining (ECM) parameters with highest GRG value for material removal rate, under-cut and etch factor [17]. The artificial neural network (ANN) component also delivers an acceptable performance for trends investigation for a specified set of tests.…”

Section: Introductionmentioning

confidence: 99%

Exploration of GRA based multiobjective optimization of magnetic abrasive finishing process using simulated annealing

Kumar¹,

Gangwar²,

Singh³

2020

FME Transactions

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Magnetic abrasive finishing (MAF) process is an advanced super-finishing process that is capable of achieving surface-finish in micro to nano range. In this study a hybrid module, Taguchi based grey relational analysis (GRA) coupled with simulated annealing (SA) approach is developed for multiobjective optimization of micro-finished aluminium 6060 via MAF process. The performance responses 'change in roughness', 'raise in temperature' and 'change in hardness' are considered to minimize their impact on micro-finished aluminium 6060. These responses are used to calculate the grey relational coefficient for individual response and are then converted into a single response variable i.e. grey relational grade (GRG) value. The meta-heuristic based simulated annealing algorithm is used to predict the most desired process parameter setting i.e. working gap (2 mm), abrasive weight (15 g), voltage (6 V) and rotational speed (50 rpm). This setting has been selected based on the highest GRG value predicted by hybrid Taguchi based SA-GRA approach..

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GRA and ANN Integrated Approach for Photochemical Machining of Al/SiC Composite

Cited by 8 publications

References 8 publications

Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites

Exploration of GRA based multiobjective optimization of magnetic abrasive finishing process using simulated annealing

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