Sharif Digital Repository

In transition from concept design to detail design, designers and engineers need to find specific materials to optimize performance of the systems. The large number of materials and the wide range of manufacturing processes cause engineers always to seek new materials selection methods. In this research work a new method so called QFD method, which employs materials indices, is introduced. The method enjoys a combination of Ashby's materials selection concepts with the Quality Function Deployment (QFD) tool. The QFD method, which is modified here to fit the materials selection field, provides the designer with the required weighting factors for the material indices. Incorporation of House of... 

Technology Identification involves developing a list of technologies which are, or may be, incorporated into products or processes. After reviewing Technology Assessment, Technology Strategy, Management of Technology and New Product Development in literature, four methods of Technology Identification are investigated: Value Chain of Technologies, Process-based Approach, Quality Function Deployment and Technology Mapping. A model facilitating decision making process is then proposed by which the most appropriate method to be employed is identified. The proposed model is examined in specific case of fuel cell technologies while preparing the Fuel cell Development Strategic Plan of Iran. 1... 

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then... 

A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and... 

Enhancing the quality of the product has always been one considerable concern of production process management, and this subject gave way to implementing so many methods including robust design. In this paper, robust design utilizes response surface methodology (RSM) considering the mean and variance of the response variable regarding system design, parameter design, and tolerance design. In this paper, customer requirements and robust design are regarded simultaneously to achieve enriched quality. Subsequently, with a non-linear programming, a novel method for integrating RSM and quality function deployment has been proposed to achieve robustness in design. The customer requirements are... 

Purpose - This paper aims to address the capability of reliability-centered maintenance (RCM), which is a preventive maintenance design tool. Design/methodology/approach - A model named Plant Function Deployment (PFD) is developed, in which the methodology of quality function deployment (QFD) is added to RCM to improve RCM capability in preserving the functions of the plants. Findings - The objective of preserving the plant functions with least resources, in RCM, is attained more efficiently if the methodology of QFD is added to RCM. Practical implications - PFD is a model for designing a preventive maintenance program which uses RCM and QFD methodologies. PFD organizes all the important... 

In this paper, in order to find an adequate method of monitoring the mean vector and covariance matrix of a production process simultaneously, first, some available univariate control methods were reviewed and evaluated. Then, the maximum exponentially weighted moving average method with a better potential application and good performances in terms of average time to signal (ATS) criterion was selected to be extended to the bivariate case. In the extended procedure, by proper transformation of the control parameters, the primary control space is transformed such that all control elements have the same probability distributions. In this case, only the maximum absolute value of the transformed... 

Statistical process control methods for monitoring processes with univariate or multivariate measurements are used widely when the quality variables fit to known probability distributions. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, it is assumed that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of... 

In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate-multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate-multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average...