Optimisation of cutting parameters of new material orthotic insole using a Taguchi and response surface methodology approach

Anggoro, Paulus Wisnu; Purharyono, Y.; Anthony, Abet Adhy; Tauviqirrahman, Mohammad; Bayuseno, A.P.; Jamari, J.

doi:10.1016/j.aej.2021.08.083

Cited by 21 publications

(10 citation statements)

References 29 publications

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“…Knowledge of these cutting parameters is the most dominant factor influencing the ability to develop CNC programs, with a practical contribution of 75.36%. Other results of this study indicate that knowledge of cutting parameters affects the ability to make CNC programs (Anggoro et al, 2022). Cutting parameters will help improve students' ability to make CNC programs.…”

Section: Discussion 1) the Effect Of The Ability To Read Engineering ...mentioning

confidence: 67%

Analysis of the Ability to Develop CNC Machine Part Programs for Vocational High School Students in Indonesia

Kurniawan,

S. Wijanarka

2023

EJEDU

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The aims of this study are (1) to analyze the effect of the ability to read technical drawings on the ability to make CNC program parts; (2) to analyze the effect of geometry skills on the ability to make CNC program parts; (3) to analyze the effect of cutting parameter knowledge on the ability to make CNC program parts; and (4) analyze the effect of the ability to read technical drawings, geometry skills, and knowledge of cutting parameters simultaneously on the ability to make CNC program parts. This research is quantitative correlational research. The subjects of the study were 360 students of SMK class XII in the field of Mechanical Engineering in the city of Palembang, Indonesia, using a random sampling technique by Isaac and Michael equations so that a total sample of 187 students was obtained. The data collection method uses the test method. The data analysis used is a correlational statistical analysis between the three dependent variables and one independent variable. The results of the study: (1) The ability to read technical drawings has a positive and significant effect on the ability to make CNC programs by 11.17%; (2) Geometry ability has no significant effect on the ability to make CNC programs; (3) Knowledge of cutting parameters has a positive and significant effect on the ability to make CNC programs by 75.36%; and, (4) the ability to read technical drawings, geometry skills, and knowledge of cutting parameters have a positive and significant effect on the ability to make CNC programs by 89.9%.

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Section: Discussion 1) the Effect Of The Ability To Read Engineering ...mentioning

confidence: 67%

Analysis of the Ability to Develop CNC Machine Part Programs for Vocational High School Students in Indonesia

Kurniawan,

S. Wijanarka

2023

EJEDU

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“…The RSM is a mathematical and statistical technique for the optimization of a process whose response or output is affected by various factors or variables. The dependent variables are the responses or output while the independent variables are the input or the predictor variable (Anggoro et al, 2022). The utilization of RSM technique for modeling, prediction, and optimization of biodiesel yield was investigated by Wahidin et al (2018), Yesilyurt et al (2019), andAnwar et al (2018).…”

Section: Application Of Machine Learning Technologies In Optimization...mentioning

confidence: 99%

Application of machine learning technologies in biodiesel production process—A review

Awogbemi

Kallon²

2023

Front. Energy Res.

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The search for renewable, affordable, sustainable, and ecologically benign fuels to substitute fossil-based diesel fuels has led to increased traction in the search for biodiesel production and utilization in recent times. Biodiesel, a form of liquid biofuel, has been found to alleviate environmental degradation, enhance engine performance, and reduce emissions of toxic gases in transportation and other internal combustion engines. However, biodiesel production processes have been dogged with various challenges and complexities which have limited its expected progression. The introduction of data-based technologies is one of the remedies aimed at deescalating the challenges associated with biodiesel synthesis. In this study, the application of machine learning (ML) –based technologies including artificial neural network (ANN), response surface methodology (RSM), adaptive network-based fuzzy inference system (ANFIS), etc. As tools for the prediction, modeling, and optimization of the biodiesel production process was interrogated based on the outcomes of previous studies in the research domain. Specifically, we review the influence of input variables like alcohol: oil molar ratio, catalyst concentration, reaction temperature, residence time, and agitation speed on the biodiesel yield (output variable). The outcome of this investigation shows that the usage of ANN, RSM, ANFIS, and other machine learning technologies raised biodiesel yield to between 84% and 98% while the statistical verification shows that the Pearson correlation coefficient and coefficient of determination are close to 1. Going forward, more targeted and collaborative research is needed to escalate the use of innovative technologies for the entire biodiesel value chain to enhance production efficiency, ensure economic feasibility, and promote sustainability.

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“…Modeling the output parameters of a technological process presents an important task because it allows prediction [42]. Generally, the prediction models found are of significant interest in the optimization stage of cutting conditions [43], [44]. In our case study, the relationship between the output responses and the input factors was established by linear regression equations, presented as mathematical models Eq.…”

Section: Response Modellingmentioning

confidence: 99%

Statistical analysis, modeling and multi-objective optimization of parameters intermittent turning process of AISI D3

Khelfaoui

Yallese

Ouelaa

et al. 2023

JMES

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Intermittent machining is characterized by its complex and irregular context. This intermittency causes machining to occur under difficult conditions that greatly influence the technological performance parameters. The aim of the present work is to evaluate the effects of input parameters, cutting speed, Vc, depth of cut, ap, tool nose radius, r and feed rate, f, on surface roughness, Ra, tangential cutting force, Fz, motor power consumption, Pm, cutting power, Pc and material removal rate (MRR), during intermittent turning (IT) of AISI D3 tool steel. Machining was performed with a triple CVD coated carbide tool (AI2O3/TiC/TiCN) by adopting a Taguchi L9 (3^4) experimental design. The ANOVA and RSM methods were used to analyze the effects of cutting factors on the outputs parameters resulting in statistical prediction models. In addition, a multi-objective optimization of the cutting conditions exploiting the desirability function (DF) was done according to four cases of relative importance corresponding to different industrial contexts. Furthermore, the grey relational analysis (GRA) method was applied and compared with the DF method. The results show that the optimal regime found by the DF method, (r =1.6mm, Vc= 240 m/min, f = 0.084 mm/rev and ap = 0.64 mm), favors Ra and MRR. On the other hand, for the GRA method, the combination of (r = 0.4 mm, Vc = 240 m/min f = 0.08 mm/rev and ap = 0.3 mm) favors the minimization of Fz, Pm and Pc. This work presents an originality because the results found are very useful in the field of optimization for a better control of the process IT.

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Optimisation of cutting parameters of new material orthotic insole using a Taguchi and response surface methodology approach

Cited by 21 publications

References 29 publications

Analysis of the Ability to Develop CNC Machine Part Programs for Vocational High School Students in Indonesia

Analysis of the Ability to Develop CNC Machine Part Programs for Vocational High School Students in Indonesia

Application of machine learning technologies in biodiesel production process—A review

Statistical analysis, modeling and multi-objective optimization of parameters intermittent turning process of AISI D3

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