Showing 7 results for Dimensional
M. Haji-Ramazanali , M. Shafiee ,
Volume 18, Issue 2 (4-2007)
Abstract
M. and M.
Abstract: Existence and uniqueness of solution for singular 2-D systems depends on regularity condition. Simple regularity implies regularity and under this assumption, the generalized wave model (GWM) is introduced to cast singular 2-D system of equations as a family of non-singular 1-D models with variable structure.These index dependent models, along with a set of boundary constraint relations, forming the admissible subspace, led to the recursive solution of the GWM.
Mr Sachin Mahakalkar, Dr Vivek Tatwawadi, Mr Jayant Giri, Dr Jayant Modak,
Volume 26, Issue 1 (3-2015)
Abstract
Response surface methodology (RSM) is a statistical method useful in the modeling and analysis of problems in which the response variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a corrugated box production process. The purpose of this research is to create response surface models through regression on experimental data which has been reduced using DA to obtain optimal processing conditions. Studies carried out for corrugated sheet box manufacturing industries having man machine system revealed the contribution of many independent parameters on cycle time. The independent parameters include anthropometric data of workers, personal data, machine specification, workplace parameters, product specification, environmental conditions and mechanical properties of corrugated sheet. Their effect on response parameter cycle time is totally unknown. The developed model was simulated and optimized with the aid of MATLAB R2011a and the computed value for cycle time is obtained and compared with experimental value. The results obtained showed that the correlation R, adjusted R2 and RMS error were valid.
Mangesh Phate, Shraddha Toney, Vikas Phate,
Volume 30, Issue 1 (3-2019)
Abstract
In the Wire EDM of oil hardening die steel materials is a complicated machining process. Hence to find out the best set of process parameters is an important step in wire EDM process. Multi-response optimization of machining parameters was done by using analysis called desirability function analysis coupled with the dimensional analysis approach. In the present work, based on Taguchi’s L27 orthogonal array, number experiments were conducted for OHNS material. The WEDM process parameters such as, pulse on time , pulse off time, input current, wire feed rate and the servo voltage are optimized by multi-response considerations such as material removal rate and surface roughness. Based on desirability analysis, the most favorable levels of parameters have been known. The significant contribution of parameters is determined by dimensional analysis. The experimental results show that the results obtain by using DA approach has a good agreement with the measured responses. The correlation up to 99% has been achieved between the developed model and the measured responses by using dimensional analysis approach.
In the Wire EDM of oil hardening die steel materials is a complicated machining process. Hence to find out the best set of process parameters is an important step in the wire EDM process. Multi-response optimization of machining parameters was done by using analysis called desirability function analysis coupled with the dimensional analysis approach. In the present work, based on Taguchi’s L27 orthogonal array, number experiments were conducted for OHNS material. The WEDM process parameters such as pulse on time, pulse off time, input current, wire feed rate, and the servo voltage are optimized by multi-response considerations such as material removal rate and surface roughness. Based on desirability analysis, the most favorable levels of parameters have been known. The significant contribution of parameters is determined by dimensional analysis. The experimental results show that the results obtain by using DA approach has a good agreement with the measured responses. The correlation up to 99% has been achieved between the developed model and the measured responses by using dimensional analysis approach.
Mangesh Phate, Shraddha Toney, Vikas Phate,
Volume 31, Issue 2 (6-2020)
Abstract
In the present work, a model based on dimensional analysis (DA) coupled with the Taguchi method to analyze the impact of silicon carbide (SiC) has been presented. The wire cut electrical discharge machining (WEDM) performance of aluminium silicon carbide (AlSiC) metal matrix composite (MMC) has been critically examined. To formulate the DA based models, total 18 experiments were conducted using Taguchi’s L18 mixed plan of experimentation. The input data used in the DA models are a pulse on time, pulse off time, wire feed rate, % SiC, wire tension, flushing pressure etc. According to these process parameters, DA models for the surface roughness and the material removal rate was predicted. The formulated DA models have shown a strong correlation with the experimental data. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters.
Pavlo Hryhoruk, Nila Khrushch, Svitlana Grygoruk,
Volume 31, Issue 4 (11-2020)
Abstract
Addressing socio-economic development issues are strategic and most important for any country. Multidimensional statistical analysis methods, including comprehensive index assessment, have been successfully used to address this challenge, but they don't cover all aspects of development, leaving some gap in the development of multidimensional metrics. The purpose of the study is to construct a latent metric space based on the use of multidimensional scaling. Based on statistics showing the economic development of Ukrainian regions, two-dimensional space of latent scales was constructed and Ukrainian's regions were positioned in this space. The results were interpreted meaningfully. This use of multidimensional statistical analysis confirms its usefulness for measuring the economic development of regions and allows their comprehensive assessment and comparison.
Oleh Kuzmin, Oksana Zhyhalo, Kateryna Doroshkevych,
Volume 31, Issue 4 (11-2020)
Abstract
Innovative capacity as a potential ability of an enterprise to innovative development is manifested in the process of formation and realization of an innovative product, which can be embodied in various forms. In the article innovation capacity is considered as a complex concept that covers the innovative output of the enterprise and the reserve for providing innovative capacity, which can make the difference between the innovative capacity and the current state of the innovative output of the enterprise.
In order to improve the level of management processes in the enterprise, the article improves the method of evaluation the innovative capacity, which is based on the use of a three-dimensional space model of the dependence of the innovative capacity on the level of loading vectors of technique of the enterprise (X-axis), applied innovative technologies (Y-axis) and resources (Z-axis) using AHP-model (analytical-hierarchical process model) and certain functional dependencies that indicate the state of innovative capacity of the enterprise and allow to identify the reserve for providing innovative capacity.
The system of indicators designed to measure the enterprise's innovation capacity is developed on the basis of the AHP-model (analytical-hierarchical process model), which contains two levels: 1) partial indicators designed to assess the level of loading of vectors of the three-dimensional space model of the enterprise's innovation capacity; 2) generalized indicators by which the level of innovation capacity is determined. The article uses the relative weight of indicators, which is calculated by forming a matrix of judgments and evaluating the components of the vector of its priorities.
Ali Salmasnia, Mohammad Reza Maleki, Esmaeil Safikhani,
Volume 34, Issue 2 (6-2023)
Abstract
In some applications, the number of quality characteristics is larger than the number of observations within subgroups. Common multivariate control charts to monitor the variability of such high-dimensional processes are unsuitable because the sample covariance matrix is not positive semi-definite and invertible. Moreover, the impact of gauge imprecision on detection capability of multivariate control charts under high-dimensional setting has been clearly neglected in the literature. To overcome these shortcomings, this paper develops a ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process in the presence of measurement system errors. The developed control chart departures from the assumption of sparse variability shifts in which the assignable cause can only affects a few elements of the covariance matrix. Then, to compensate for the adverse impact of gauge impression, the developed chart is extended by employing multiple measurements on each sampled item. Simulation studies are carried out to study the impact of imprecise measurements on detectability of the developed monitoring scheme under different shift patterns. The results show that the gauge inability negatively affects the run-length distribution of the developed control chart. It is also found that the extended chart under multiple measurements strategy can effectively reduce the error impact.
