Optimization of Slider Crank Mechanism Using Design of Experiments and Multi-Linear Regression
Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3rd degree to 1st degree and suggested valid predictions and stable explanations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316420Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 935
 Abdul Samad. K., Zainol. N., The use of factorial design for ferulic acid production by co-culture: Industrial Crops and Products 95 (2017) 202–206.
 Pawlak. A., Rosienkiewicz. M., Chlebus. E., Design of experiments approach in AZ31 powder selective laser melting process optimization: Archives of Civil and Mechanical Engineering 17 (2017) 9–18.
 Kala. M., Shaikh. M. V., Nivsarkar. M., Development and optimization of psychological stress model in mice using 2 level full factorial design: Journal of Pharmacological and Toxicological Methods 82 (2016) 54–61.
 Vasandani. P., Mao. Z. H., Jia. W., Sun. M., Design of simulation experiments to predict turboelectric generator output using structural parameters: Simulation Modeling Practice and Theory 68 (2016) 95–107.
 Giasin. K., Soberanis. S.A., Hodzic. A., Evaluation of cryogenic cooling and minimum quantity lubrication effects on machining GLARE laminates using design of experiments: Journal of Cleaner Production 135 (2016) 533-548.
 Mitra. A. C., Kiranch and. G. R, Soni. T., Banerjee. N., Design of Experiments for Optimization of Automotive Suspension System Using Quarter Car Test Rig: Procedia Engineering 144 (2016) 1102–1109.
 D’Ambrosio. S., Ferrari. A., Potential of multiple injection strategies implementing the after shot and optimized with the design of experiments procedure to improve diesel engine emissions and performance: Applied Energy 155 (2015) 933–946.
 Li. J., Yang. W. M., Goh. T. N., An. H., Maghbouli. A., Study on RCCI (reactivity controlled compression ignition) engine by means of statistical experimental design: Energy 78 (2014) 777-787.
 Tashtoush. G. M., AlWidyan. M. I., Albatayneh. A. M., Factorial analysis of diesel engine performance using different types of biofuels: Journal of Environmental Management 84 (2007) 401–411.
 Da Silva. L. C., De Melo. A. C., Machado. L. R., Da Silva. M. B., Souza J´unior. A. M., Application of factorial design for studying the burr behavior during face milling of motor engine blocks. Journal of Materials Processing Technology 179 (2006) 154–160.
 Trezona. R. I., Pickles. M. J, Hutchings. I. M., A full factorial investigation of the erosion durability of automotive clear coats: Tribology International 33 (2000) 559–571.
 Palkar. R. R., Shilapuram. V., Detailed parametric design methodology for hydrodynamics of liquid–solid circulating fluidized bed using design of experiments: Particuology 31 (2017) 59–68.
 Kim. W., Jeon. Y., Kim. Y., Simulation-based optimization of an integrated day lighting and HVAC system using the design of experiments method: Applied Energy 162 (2016) 666–674.
 Dong. S., Sartaj. M., Statistical analysis and optimization of ammonia removal from landfill leachate by sequential microwave/aeration process using factorial design and response surface methodology: Journal of Environmental Chemical Engineering 4 (2016) 100–108.
 Kim. Y., Jeon. E. S., Establishment of regression model for estimating shape parameters for vacuum-sealed glass panel using design of experiments: Vacuum 121 (2015) 113-119.
 Njoya. D., Hajjaji. M., Quantification of the effects of manufacturing factors on ceramic properties using full factorial design: Journal of Asian Ceramic Societies 3 (2015) 32–37.
 Jafari. H., Idris. M. H., Shayganpour. A., Evaluation of significant manufacturing parameters in lost foam casting of thin-wall Al−Si−Cu alloy using full factorial design of experiment: Trans. Nonferrous Met. Soc. China 23(2013) 2843−2851.
 Zhang. Q., Anyakin. M., Zhuk. R., Pan. Y., Kovalenko. V., Yao. J., Application of Regression Designs for Simulation of Laser Cladding: Physics Procedia 39 (2012) 921–927.
 Hajjaji. N., Renaudin. V., Houas. A., Pons. M. N., Factorial design of experiment (DOE) for parametric exergetic investigation of a steam methane reforming process for hydrogen production. Chemical Engineering and Processing 49 (2010) 500–507.