Optimization of machining parameters to improve the surface quality

Ribeiro, José Carlos Rabelo; Braz-César, Manuel; Lopes, H.

doi:10.1016/j.prostr.2017.07.182

Cited by 59 publications

(31 citation statements)

References 17 publications

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“…Using the obtained results of S/N and ANOVA analyses, the optimal combination of the control factors and their levels can be predicted. The goal of the present work is the maximization of tensile and flexural strength, and, for this reason, the category of quality characteristics for S/N ratio is thehigher-the-better, which is defined by (Ribeiro, César, and Lopes 2017):…”

Section: Resultsmentioning

confidence: 99%

The Influence of Manufacturing Factors in the Short-Fiber Non-Woven Chestnut Hedgehog Spine-Reinforced Polyester Composite Performance

Ribeiro

Rocha

Queijo

2019

Journal of Natural Fibers

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One of the most important agricultural products in the northeast of Portugal is sweet chestnuts. In this region only, several tons of this product are produced every year. Sweet chestnuts are enclosed in a hedgehog covered with a large number of short and fine spines and containing more than one nut. This hedgehog, with long spines, does not have any commercial value, and they are considered an agricultural waste that is left in the fields to be incorporated into the soil. In this work, the authors propose to study the influence of some factors in the short-fiber non-woven chestnut hedgehog spine-reinforced polyester composite performance. For this purpose, the tensile properties of chestnut spines were determined by implementing a design of experiments (DOE) approach developed by Taguchi. Thus, the factors that will be controlled are the fiber dispersion which is defined by fiber/matrix mass ratio or weight fraction, the interfacial strength that could be improved using an alkali-silane treatment and, finally, the moisture level in the fibers. For each factor, three levels were used, an L 9 orthogonal array of experiments was implemented to evaluate tensile and flexural properties. These properties were determined based on results of 54 tests, and the maximum mean values for tensile and flexural strength were 23.5 MPa and 33.6 MPa, respectively. The most influential factor for maximizing the tensile strength was the volume fraction of fiber with a contribution of 30%, and most influential factor for maximizing the flexural strength was the moisture level with a contribution of 65%. The determined mean value of tensile strength of the spines was 148 MPa. 摘要葡萄牙东北部最重要的农产品之一是甜栗子。这个地区每年只生产几吨这种产品. 甜栗子被包围在一只刺猬里，刺猬身上长满了许多又短又细的刺，里面有不止一个坚果. 这种长着长刺的刺猬没有任何商业价值，它们被认为是一种农业废弃物，留在地里，以便并入土壤. 本文研究了短纤维无纺栗子刺猬刺增强聚酯复合材料性能的影响因素. 为此，采用田口的实验设计方法测定了板栗尖晶石的拉伸性能。因此，将要控制的因素是由纤维/基体质量比或重量分数定义的纤维分散性、使用碱-硅烷处理可提高的界面强度以及纤维中的水分水平.对于每一个因素，使用三个水平，实施L9 正交试验阵列来评估拉伸和弯曲性能. 这些性能是根据54个试验的结果确定的，拉伸强度和弯曲强度的最大平均值分别为 23.5 MPa 和 33.6 MPa. 最大化拉伸强度的最有影响的因素是纤维的体积分数，贡献率为30%，最大抗弯强度的影响因素是湿度水平，贡献率为65%。测定的尖晶石抗拉强度平均值为148mpa.

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Section: Resultsmentioning

confidence: 99%

The Influence of Manufacturing Factors in the Short-Fiber Non-Woven Chestnut Hedgehog Spine-Reinforced Polyester Composite Performance

Ribeiro

Rocha

Queijo

2019

Journal of Natural Fibers

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“…In [13] authors analyzed the Ra on the milling of hardened steel by using Taguchi method and ANOVA Technique. The study also provides the role of each machining parameter and their interface for Ra.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Machining Parameters of Aluminum Alloy BS-1474 2014 A using Response Surface Methodology

Javaid

Iqbal

Hassan

2020

Preprint

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High surface quality, optimum Material Removal Rate (MRR) and Tool-Chip Interface temperature (T c ) have significant importance in machining process. Similarly, dimensional accuracy in machining process also relies heavily on machining parameters. In machining operations, there are a number of parameters which have direct or indirect effect on the Surface Roughness (Ra) and MRR of the product. The surface roughness and MRR in turning process are affected by spindle speed (SS), feed rate (FR) and depth of cut (DOC). The optimization of turning parameters will be very helpful in improving quality of manufacturing and machining cost. In order to have an improved product, extensive research has been carried out to optimize machining process. The current research is focused at Response Surface Methodology (RSM) of turning process of Aluminum alloy (BS-1474 2014 A) by using variable sets of machining parameters i.e., SS, FR and DOC with carbide tipped tool. Multiple experiments were performed on CNC Lathe machine by using different combinations of process parameters. Response surface methodology was applied to reach theoretical values of the responses parameters (i.e, Ra, MRR, T c ) and an agreement was observed between actual machining results and methodology values. Design Expert-7 was used as a statistical tool to come to a conclusion on whether achieved results are optimum for turning process or otherwise. For a close correlation, comparison between hypothetical and investigational data is also the part of this research. Significant agreement between theoretically optimized machining parameters and experimental data has been observed.

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“…The concept of S/N ratio transformation introduced by Taguchi is to account the scatter in the output response of repeated tests for each test run in the orthogonal array and provide a single value to perform analysis of variance (ANOVA) and examine the significance of the input process variables. Many researchers have utilized the S/N ratio transformation while obtaining the optimal solution of several manufacturing processes such as machining of a hardened steel cylindrical work-piece [23,24] and commercial mild steel [25]; Dry end milling operation of Al-6063 [26]; AISI 1020 mild steel bar in turning operation [27]; high-speed cold rolling-beating processing parameters [28]; electrochemical machining for Al/B 4 C composites [29,30]; EDM for Incoloy 600 [31]; WEDM for AISI H13 steel [32], stainless steel [33] and titanium alloy [34,35]; MIG welding for AM-40 aluminium alloy [36]; and friction stir welding process of aluminium alloy [37][38][39]. However, the additive law [9] estimates the deterministic output response from the mean values of the ANOVA table and unable to provide the expected range of the output response.…”

Section: Introductionmentioning

confidence: 99%

Optimal laser welding process parameters and expected weld bead profile for P92 steel

2019

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P92 steel is classified as one of the creep strength enhanced ferritic steels, whose welding plays a crucial role in power industries. The best choice for such materials is the laser welding in open atmosphere with an inert gas shield. Compared to the frequently being used Taguchi based grey relational analysis, a simple and systematic approach known as the modified Taguchi design of experiments is utilized for P92 steel to obtain the optimized laser welding process parameters and the expected range of output responses. The complicated non-linear relationship between the laser welding process parameters (viz., laser power, welding speed and focal position) and the welding bead geometry (such as depth of penetration, weld width and heat affected zone width) is developed through empirical relations, which are validated by comparing with existing test results. Most of the test results are found to be within the expected range. From the ANOVA analysis the process parameter, viz. the focal position is found to have negligible variation on the overall mean value of the output responses and hence, the process designer can opt for any one the set levels of the focal position during welding operations.

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Optimization of machining parameters to improve the surface quality

Cited by 59 publications

References 17 publications

The Influence of Manufacturing Factors in the Short-Fiber Non-Woven Chestnut Hedgehog Spine-Reinforced Polyester Composite Performance

The Influence of Manufacturing Factors in the Short-Fiber Non-Woven Chestnut Hedgehog Spine-Reinforced Polyester Composite Performance

Optimization of Machining Parameters of Aluminum Alloy BS-1474 2014 A using Response Surface Methodology

Optimal laser welding process parameters and expected weld bead profile for P92 steel

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