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In the field of drilling there is increasing interest in topics such as degradation of drilling tools and estimation of penetration speed, as well as efforts to optimize geometrical parameters and drilling processes. The current study was based on an original experimental setup that estimates the actual operating conditions of drilling tools and pr...
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In India, Himalayan region experiences least temperature during winter season. Temperature within range of − 25 °C is common at high-altitude areas like Jammu and Kashmir (India). A large number of experiments have been conducted in the past to investigate the consequences of addition of waste materials and different fibres on engineering propertie...
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... One of the benefits of the Taguchi method is the consideration of noise factors, in this case, factors that cannot be controlled (Rathi and Salunke, 2012;Yang et al., 2008), in this study, we have considered only controllable factors. This experiment uses the S/N ratio with the larger-the-better approach to analyzing the signal-to-noise ratio (S/N) as shown by (Wicaksono, Budiyantoro, and Rochardjo, 2019;Khentout, Kezzar, and Khochemane, 2019) using equation (1). Then after the S/N ratio is obtained, it is analyzed using DOE Taguchi to find the correlation between the responses and the processing parameter as the variable. ...
... The results of ANOVA testing in identifying significant control factors and F-ratios in this study are presented in Table 7. ANOVA is widely used in experiments for statistical analysis in uncovering parameters that significantly influence the response variable (N.J. Rathod et al., 2021). Hence, ANOVA is employed to ascertain the impact of machining factors on different responses (Khentout et al., 2002). The ANOVA analysis determines the significant elements that impact the final machining outcome by evaluating the sum of squares (SS), the computed variance of squares, and the F test ratio at a 95% confidence level (Sayeed et al., 2015). ...
The use of CNC milling machines in the industrial sector greatly contributes to the production of high-quality products that align with consumers' desired shapes. Currently, the precise machining parameters for manufacturing a product are determined through a time-consuming and costly process of trial and error. Most prior significant studies have examined the variables that can impact the duration of machining time. However, different machining conditions require different control factors. The key objective of this study is to enhance the machining parameters and identify the crucial factors that influence the duration of machining in the production of foot prostheses. The experiment was conducted using a 3-axis CNC milling machine with five machine parameters: spindle speed, feed rate, step over, depth of cut, and toolpath strategy. The Taguchi method with orthogonal array L2735 was chosen as an optimization method. The optimum machine parameters are analyzed using signal-to-noise (S/N) ratio and ANOVA. The analysis shows that spindle speed is the most influential variable on machine time. The next factor is the depth of cut, feed rate, and toolpath strategy, and the last is step over.
... ANOVA has been used predominantly in experiments for statistical analysis and to reveal cutting parameters that significantly affect response variables as well as performance characteristics (Rathod et al., 2021). In addition, ANOVA can determine the effect of machining parameters on various responses, including power consumption, cutting force, and material removal rate (MRR), etc. (Khentout et al., 2019). In the analysis, 60 Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method the sum of squares (SS) and variance of square are calculated, and the F-test ratio at the 95% confidence level is employed to determine the significant factors that affect machining (Ahmed et al., 2015). ...
In this paper, the application of dry machining as
part of contributions for development of a sustainable environment in the
machining industry is explained. Achieving a good surface roughness product is
one the most important factors that must be considered in the metal machining
process. Surface quality control is a complicated process, and a reliable
technique is required during machining operation. Currently, an appropriate
cutting conditions in most of machining cases are determined by trial and
error, which leads to time increased, energy consumption, and manufacturing
costs. Most of the previous studies have investigated factors that affect
surface roughness, but different machining conditions require the control of
different factors. In this study, experiments were conducted to optimize
cutting parameters and determine the factors significant for the surface
roughness quality. Machining experiments were conducted on a vertical milling
machine using square non-coated two flutes HSS Co end mill with selected
cutting parameters on aluminum 6061. This
study focused on the surface roughness in one direction and combined with the Taguchi design method. Signal-to-noise (S/N) ratio
and analysis of variance (ANOVA) were employed to examine and reveal the
factors that are significant in affecting surface roughness quality. The
analysis result revealed that cutting speed exerts the highest effect on
surface roughness, followed by feed rate and depth of cut. Finally, the
combination of dry machining performance and an eco-friendly environment would
result in competitive sustainable growth of the machining industry.
... The mathematical model was built using experiment design. One of the experimental design applications using Taguchi (Khentout et al., 2019). The first step was determining success factors to build the mathematical model, which could determine what kinds of factors positively and negatively affect market share. ...
Previous studies have not agreed
on factors that affect the success of different kinds of products. These
studies have used independent variables with a different number of success
factors to examine several products, and some of these variables have been
redundant. No standardized factor predicts the success of various products;
thus, no generalizable result has provided a reference for further studies or
use recommendations for practices. Therefore, this study produced a model that
not only examined specific products and scope but also used a number of
standardized success factors by building a mathematical model of the success
factors that affect the success of various products. The study utilized 304
products from the Indonesian market as well as design of
experiment to build the mathematical model. The results suggested that six
standardized success factors affected the success of various products: (1)
price, (2) product performance, (3) brands, (4) aesthetic design, (5) services
and (6) marketing. Services and marketing (i.e., appropriately timed marketing)
were positively correlated and proportional to the increase of market share.
Therefore, focusing on the services and marketing factors that will drive the
success of a product is important for many companies. The factors that
positively drive success can be determined by characteristic sales in the
Indonesian market.
Semarang city has the amount of waste that piled up in the Landfill as much as 850 tons/day with the amount of unmanaged waste as is 100 tons/day. This phenomenon needs to be identified and evaluated for the flow of waste disposal from upstream to downstream. Therefore, the purpose of this study is to identify the flow of the waste supply chain and simulate it to determine the performance of waste handling in an area. The research method begins with a supply chain questionnaire which will then be statistically tested to be used as input for supply chain flow generation and simulation using Arena software. Based on the identification of the supply chain, the flow of the waste supply chain in the Wonosari area of Semarang is from households to the garbage dump to be taken to the landfill as far as 10 km. From the landfill, it is further processed into a selling value product. In this study, waste from the landfill is also the input material for the paving block production workshop (WPBP FT) at the Faculty of Engineering, Dian Nuswantoro University. Plastic waste will be used as a mixed material for making paving blocks. The supply chain simulation results using Arena show that the performance is good.
