Mathematical modelling and optimization of surface quality and productivity in turning process of AISI 12L14 free-cutting Steel

Fathallah, Brahim Ben; Saidi, Riadh; Dakhli, Chams Eddine; Belhadi, Salim; Yallese, Mohamed Athmane

doi:10.5267/j.ijiec.2019.3.001

Cited by 15 publications

(15 citation statements)

References 26 publications

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“…Detailed derivations of T min in Equation (25) are presented as follows. From Equation (24) we have:…”

Section: Discussionmentioning

confidence: 99%

“…or, the fabrication cycle length T must be greater than T min , as shown in Equation (25) or (A13) as follows:…”

Section: Discussionmentioning

confidence: 99%

“…A proposed polynomial dynamic programming algorithm solved the problem. Extra studies [22][23][24][25] investigated different features of imperfect fabrication systems and their consequent actions.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating Outsourcing Strategy and Quality Assurance into a Multiproduct Manufacturer–Retailer Coordination Replenishing Decision

Chiu

Yeh

et al. 2020

Mathematics

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This study explores the multiproduct manufacturer-retailer coordination replenishing decision featuring outsourcing strategy and product quality assurance. Globalization has generated enormous opportunities. Consequently, transnational firms now face tough competition in global markets. To stay competitive, a firm should meet the client’s multi-item and quality requirements under capacity constraints and optimize the intra-supply chain system to allow the timely distribution of finished goods under minimum system cost. The outsourcing option is considered to release machine loadings and reduce cycle time effectively. All items fabricated are screened for quality, and reworkable and scrap items are separated. Any reworked items that fail the quality reassurance screening are discarded, whereas all outsourced products are quality-guaranteed by the provider. A fixed-quantity multi-shipment plan is used when the whole finished lot is quality-ensured to help present-day transnational firms gain competitive advantage by making efficient and cost-effective multiproduct manufacturing and delivering decisions. Mathematical modeling is built to portray the system’s characteristics, and conventional differential calculus is used to solve and derive the optimal operating policy for the proposed problem. Simultaneously, we find the optimal delivery frequency and common cycle time for the problem mentioned above. A simulated numerical example and sensitivity analysis demonstrate the research result’s capability and applicability. Our precise analytical model can reveal/highlight the impact of deviations in quality- and outsourcing-related features on the optimal operating policy and several performance indicators that help managerial decision-making.

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“…Detailed derivations of T min in Equation (25) are presented as follows. From Equation (24) we have:…”

Section: Discussionmentioning

confidence: 99%

“…or, the fabrication cycle length T must be greater than T min , as shown in Equation (25) or (A13) as follows:…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating Outsourcing Strategy and Quality Assurance into a Multiproduct Manufacturer–Retailer Coordination Replenishing Decision

Chiu

Yeh

et al. 2020

Mathematics

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“…Mathematical modelling is one of the technique to find out the machining characteristics with respect to machining parameters by a sequential formula generated among them. From the research by lodge in 1907 [13] to recent research of 2019 by Fathallah [14], mathematical modelling generation cleared the path for the industrial team to predict the surface roughness using these mathematical formulas. Utilizing the experimental values, the mathematical equation has been generated in Minitab 18 software.…”

Section: Fig5 Variation Of Surcace Roughness Wrt Depth Of Cutmentioning

confidence: 99%

Experimental Studies on Machining EN45 Steel under Dry and MQL using Uncoated Inserts

Satynarayana¹,

Reddy²,

Srikar³

et al. 2020

E3S Web Conf.

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In owe to the global concern of environment growth and green production acts with use biodegradable and low consumption of lubrications the present paper deals use of MQL in turning EN 45 steel. Literacy over the use of effective production practices increases the efficiency of surface integrity and economical effect of production. With this aspect, the present study deals with of surface roughness characteristic generated while turning EN 45 graded spring steel with uncoated CNMG insert under MQL condition. A total of 27 experiments are performed on a CNC lathe under both Dry and MQL conditions. Surface roughness produced with varying in machining parameter of cutting speed (75, 100, 125 m/min), feed rate (0.1, 0.2, 0.3 mm/rev) and depth of cut (0.3, 0.6, 0.9 mm). Effect of induvial parameter over surface roughness is been clearly pictured out with the graphical representation with comparison under both DRY and MQL conditions. A mathematical model was generated with the experimental results for prediction of roughness within the limits of parameters. ANOVA analysis, feed shows a high contribution towards the surface roughness.

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“…Kovak et al [28] applied the fuzzy logic and regression analysis for modelling of the surface roughness in a face milling operation while Campatelli et al [29] employed the Response Surface Methodology (RSM) for the optimization of the power consumption during machining operations. In addition, Djavanroodi et al [30] used the ANN for the modelling of an equal channel angular pressing process while Fathallah et al [31] carried out the mathematical modelling and optimization of surface quality and productivity during the turning process of AISI 12L14 free-cutting steel. From these works, the modelling and optimization techniques present applicable solutions for the correlation and determination of the optimum process conditions during the machining operations.…”

Section: Introductionmentioning

confidence: 99%

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

et al. 2020

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Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

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Mathematical modelling and optimization of surface quality and productivity in turning process of AISI 12L14 free-cutting Steel

Cited by 15 publications

References 26 publications

Incorporating Outsourcing Strategy and Quality Assurance into a Multiproduct Manufacturer–Retailer Coordination Replenishing Decision

Incorporating Outsourcing Strategy and Quality Assurance into a Multiproduct Manufacturer–Retailer Coordination Replenishing Decision

Experimental Studies on Machining EN45 Steel under Dry and MQL using Uncoated Inserts

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

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