Multi-Objective Optimization of Abrasive Water Jet Cutting Using MOGA

Radovanović, Miroslav

doi:10.1016/j.promfg.2020.04.241

Cited by 20 publications

(16 citation statements)

References 15 publications

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“…ANN and the regression model were used for surface roughness prediction in the AWJ cutting of AA 7075 aluminum alloy [ 9 ]. Additionally, different approaches have been applied for the investigation of surface roughness in the AWJ process, such as fuzzy logic in [ 10 ], the Taguchi-based analysis of variance method in [ 11 , 12 , 13 ], the multi-objective genetic algorithm (MOGA) in [ 14 ], and the regression method in [ 10 , 15 ]. Liu et al [ 16 ] developed quadratic regression models to predict the penetration depth and surface roughness in abrasive water jet turning of alumina ceramics using a response surface methodology with a Box–Behnken design.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [ 16 ] developed quadratic regression models to predict the penetration depth and surface roughness in abrasive water jet turning of alumina ceramics using a response surface methodology with a Box–Behnken design. Different kinds of materials were subjected to the AWJ process, among them, carbon steel S235 [ 14 ], Hardox steel [ 15 ], magnesium alloy [ 10 ], aluminum alloy [ 17 , 18 ], titanium alloy [ 19 ], marble [ 20 ], aluminum/magnesium hybrid metal matrix composites [ 11 ], a lanthanum phosphate/yttria composite [ 12 ], and Nimonic C236 superalloy [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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“…Later, Nandakumar, et al [97] has examined the nozzle head oscillating method in AWJ As illustrated in Figure 5, the waterjet pressure water travels at a high velocity and generates a Venturi effect or vacuum in the mixing chamber located beneath the orifice. A metered portion of abrasive particles enters through the abrasive inlet and is forced down with the waterjet stream in the mixing chamber [96]. The abrasive particles are mixed with the waterjet, creating an abrasive water jet.…”

Section: Nozzle Systemmentioning

confidence: 99%

“…The nozzle is a vulnerable component of an abrasive waterjet machine and is commonly composed of silicon carbide, tungsten carbide cobalt, boron carbide, composite, and ceramic materials. Varied materials possess diverse properties that enable a nozzle to lengthen its utilization and wear [96]. In comparison to metals, ceramic nozzles (SiC, Al 2 O 3, ZrO 2, B 4 C and Si 2 N 4 ) are universally used in line with mechanical properties, maximum hardness, a high melting point, and lesser resistance to heat shock.…”

Section: Nozzle Systemmentioning

confidence: 99%

Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review

et al. 2021

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Abrasive water jet machining has been extensively used for cutting various materials. In particular, it has been applied for difficult-to-cut materials, mostly metals, which are used in various manufacturing processes in the fabrication industry. Due to its vast applications, in-depth comprehension of the systems behind its cutting process is required to determine its effective usage. This paper presents a review of the progress in the recent trends regarding abrasive waterjet cutting application to extend the understanding of the significance of cutting process parameters. This review aims to append a substantial understanding of the recent improvement of abrasive waterjet machine process applications, and its future research and development regarding precise cutting operations in metal fabrication sectors. To date, abrasive waterjet fundamental mechanisms, process parameter improvements and optimization reports have all been highlighted. This review can be a relevant reference for future researchers in investigating the precise machining of metallic materials or characteristic developments in the identification of the significant process parameters for achieving better results in abrasive waterjet cutting operations.

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“…Cost of the material removal process is also an important aspect that should be taken into consideration, as it complies with one of the pillars of sustainability, namely the economic dimension. The total cost is calculated as the sum of five components, namely, the cost of abrasive particles, the cost of electric power, the cost of material, the cost of water, and the labor cost [53,54]: C total = C abr + C el + C mat + C wat + C labor (4) where Cabr = ca × ma × tm, C el = c e × P × tm, C mat = cm × MRR × ρ Ti × t m , C wat = c w × Q w × t m , and C labor = c l × t m . In the above equations, c a is the cost of abrasive per unit mass, which is equal to 3000 euro/tn in the case of garnet and 4000 euro/tn in the case of glass beads; t m is the machining time; c e is the cost of electricity, which is equal to 0.12 euro/kWh; c m is the cost of workpiece materials, which is equal to 45 euro/kg; ρ Ti is the density of titanium, equal to 4400 kg/m 3 ; c w is the cost of water, which is equal to 1.98 euro/m 3 ; and c l is the labor cost, equal to 8 euro/h.…”

Section: Sustainability Indicatorsmentioning

confidence: 99%

Experimental Study on the Sustainability Assessment of AWJ Machining of Ti-6Al-4V Using Glass Beads Abrasive Particles

et al. 2021

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Abrasive Waterjet Machining (AWJM) is considered a viable alternative to conventional machining processes, due to its capability of rendering even complex features on parts with high productivity. However, it is currently also important for manufacturing processes to comply with the various aspects of sustainability, by putting emphasis on the environmental dimension apart from the economic. Although AWJM generally is considered an inherently environmentally friendly process, it is required that thorough experimental studies be carried out to evaluate the sustainability of AWJM under various conditions. In the present work, AWJM experiments under various conditions were conducted on a Ti-6Al-4V workpiece in order to determine the optimal conditions leading to a high degree of sustainability in this process based on several indicators. The experiments were carried out using glass beads, which act as an eco-friendly abrasive. After the basic outcome of the experiment was analyzed to determine the correlations between process parameters and depth of penetration, kerf width, and kerf taper angle, sustainability analysis with the aid of Grey Relational Analysis (GRA) was conducted. The optimum solution provided a sufficiently high score regarding both the economic and environmental dimensions of sustainability.

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Multi-Objective Optimization of Abrasive Water Jet Cutting Using MOGA

Cited by 20 publications

References 15 publications

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review

Experimental Study on the Sustainability Assessment of AWJ Machining of Ti-6Al-4V Using Glass Beads Abrasive Particles

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