Contents: Variety and Quality. Variability loss and tolerance. Determining tolerances. Tolerance design and experimental design. Offline and online quality control. Parameter design and tolerance design: case study. Experimental design for smaller is better characteristics. Experimental design for larger is better characteristics. Bypassing the S/N ratio: spring experiment. Experimental design for dynamic characteristics.

Introduction to quality engineering. designing quality into products a

In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Liao%20-%20Data%20mining%20techniques%20and%20applications%20A%20decade%20review%20from%202000%20to%202011%20-%202012.pdf

Review: Data mining techniques and applications - A decade review from 2000 to 2011

In this paper, an intelligent system to determine welding parameters for each pass and welding position in pipeline welding based on one database and FEM model, two BP neural network models and a C-NN model was developed and validated. The preliminary test of the system has indicated that the developed system could determine welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality, and reduce the cost of system integration.

Prediction of Welding Parameters for Pipeline Welding Using an Intelligent System

The exhaustion of natural resources has increased petroleum prices and the environmental impact of oil has stimulated the search for an alternative source of energy such as biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Biodiesel is synthesized by direct transesterification of vegetable oils, which is controlled by several inputs or process variables, including the dosage of catalyst, process temperature, mixing speed, mixing time, humidity and impurities of waste cooking oil that was studied in this case. Yield, turbidity, density, viscosity and higher heating value are considered as outputs. This paper used multi-response surface methodology (MRS) with desirability functions to find the best combination of input variables used in the transesterification reactions to improve the production of biodiesel. In this case, several biodiesel optimization scenarios have been proposed. They are based on a desire to improve the biodiesel yield and the higher heating value, while decreasing the viscosity, density and turbidity. The results demonstrated that, although waste cooking oil was collected from various sources, the dosage of catalyst is one of the most important variables in the yield of biodiesel production, whereas the viscosity obtained was similar in all samples of the biodiesel that was studied.

An Improvement in Biodiesel Production from Waste Cooking Oil by Applying Thought Multi-Response Surface Methodology Using Desirability Functions

For the past several decades, significant efforts have been devoted to modeling and analysis of rolling-element bearings to aid in their design and application. This paper reviewed the modeling and analysis of rolling-element bearings with emphasis on single-row ball and roller bearings. The application of bearing models was reviewed as well along with illustrative results.

Rolling-element bearing modeling: A review

This paper proposes a methodology that combines the Finite Element Method and multiple response surface optimization to search for the optimal operating conditions of a double-row Tapered Roller Bearing (TRB) that has a Preload (P), radial load (Fr), axial load (Fa) and torque (T). Initially, FE models based on a double-row TRB are built and validated in the basis of experimental data and theoretical models. Three of the most important parameters used in the design of TRB were obtained from a simulation of the FE models with a combination of several operating conditions that were previously selected in accordance with a design of experiments. The design parameters are: contact stress radio for both rows of rollers (S1 and S2), maximum deformation of the outer raceway (αmax), and the difference between the gaps of the inner raceways (Δδ) or misalignment. Based on the results of the FE simulations, quadratic regressions models are generated that use the response surface method to predict the design parameters when new operating condition are applied. Then, a multi-response optimization study based on these models and using desirability functions is conducted. It is concluded that the accuracy of the results demonstrates that this methodology may be used to search for the optimal operating condition in a double-row TRB.

Optimization of operating conditions for a double-row tapered roller bearing

One of the main objectives when designing welded products is to reduce strains and deformations Strains can cause excessive angular distortion This results in a welded product that does not meet acceptable tolerances The geometry of the weld bead height and width depends on the input parameters speed, voltage and current, and provides the welded joint with strength and quality As welded products become increasingly complex, deformations become more difficult to predict as they depend greatly on the welding sequence This paper shows how a combination of the Finite Element Method, Genetic Algorithms and Regression Trees may be used to design and optimize complex welded products Initially, Artificial Neural Networks and Regression Trees that are based on heuristic methods and evolutionary algorithms were used in predicting the weld bead geometry according to the input parameters Then, thermo-mechanical Finite Element models were created to obtain the temperature field and the angular distortion using the weld bead geometry that the best predictive models generated Finally, optimization techniques that are based on Genetic Algorithms were used to validate these Finite Element models against experimental results, and to subsequently find the optimal welding sequence to use in the manufacture of complex welded products

Combining soft computing techniques and the finite element method to design and optimize complex welded products

Double-row Tapered roller bearings (TRBs) are mechanical devices that are designed to support high axial, radial and torque loads. This combination of loads produces high contact stresses on the bearing raceways that are difficult to predict and validate experimentally, and can cause defects like pitting and fatigue spalling. In response, theoretical models have been proposed by many researchers to calculate the approximate distribution of contact stresses over the bearing raceways. More recently, numerical methods that are based on the Finite element method (FEM) have been used to obtain the contact stresses, although this method requires that the mesh size first be adjusted. This paper shows a process for adjusting a double-row TRB Finite element (FE) model. It is based on generating successive nonlinear FE submodels to calculate the distribution of contact stresses. A theoretical model and contact pressure sensors were used to adjust and validate the Finite element (FE) model.

Determination of the contact stresses in double-row tapered roller bearings using the finite element method, experimental analysis and analytical models

This paper demonstrates that combining regression trees with the finite element method (FEM) may be a good strategy for modelling highly non-linear mechanical systems. Regression trees make it possible to model FEM-based non-linear maps for fields of stresses, velocities, temperatures, etc., more simply and effectively than other techniques more widely used at present, such as artificial neural networks (ANNs), support vector machines (SVMs), regression techniques, etc. These techniques, taken from Machine Learning, divide the instance space and generate trees formed by submodels, each adjusted to one of the data groups obtained from that division. This local adjustment allows good models to be developed when the data are very heterogeneous, the density is very irregular, and the number of examples is limited. As a practical example, the results obtained by applying these techniques to the analysis of a vehicle axle, which includes a preloaded bearing and a wheel, with multiple contacts between components...

R. Lostado

Papers

Optimization of operating conditions for a double-row tapered roller bearing

Combining soft computing techniques and the finite element method to design and optimize complex welded products

Determination of the contact stresses in double-row tapered roller bearings using the finite element method, experimental analysis and analytical models

Combining regression trees and the finite element method to define stress models of highly non-linear mechanical systems:

Prediction models for calculating bolted connections using data mining techniques and the finite element method