A Computer Program to Calculate Two-Stage Short-Run Control Chart Factors for (X,MR) Charts

This paper is the second in a series of two papers that fully develops two-stage short-run (X, MR) control charts. This paper describes the development and execution of a computer program that accurately calculates first- and second-stage short-run control chart factors for (X, MR) charts using the equations derived in the first paper. The software used is Mathcad. The program accepts values for number of subgroups, alpha for the X chart, and alpha for the MR chart both above the upper control limit and below the lower control limit. Tables are generated for specific values of these inputs and the implications of the results are discussed. A numerical example illustrates the use of the program.

Second Stage Short Run (X,vc) and (X,sc) Control Charts

In their'70 paper titled "Mean and Variance Control Chart Limits Based on a Small Number of Subgroups" (Journal of Quality Technology, Volume 2, Number 1, pp. 9-16), Yang and Hillier originally derived equations for calculating the factors required to determine second stage short run control limits for ) v,X c( and )s ,X( c charts. Two issues have restricted the applicability of this particular control chart methodology. These are the limited tabulated values of factors Yang and Hillier present and no example to illustrate the use of the methodology. This paper addresses the first issue by presenting a computer program that accurately calculates the factors regardless of the values of the required inputs. An example shows how to incorporate the methodology into a two stage short run control charting procedure. The computer program is available at http://program.20m.com.

Variability In Soil Testing

Many factors can influence the accuracy of soil test results, ranging from field sampling technique, sample preparation, and quality control in the laboratory. Many people expect that if a field is sampled more than once, the soil test results should be identical. When identical results are not obtained from successive sampling, much concern about soil test reliability is of ten expressed. We have analyzed soil test results from some controlled field experimental sites which help provide an understanding of variability which can occur naturally in the field, how various field sampling techniques influence soil test readings obtained, and how laboratories duplicate readings from the same samples tested on different dates. The examples discussed here represent only a few of the many scenarios which can affect soil test results

Two-Stage Short-Run (X, MR) Control Charts

This article is the first in a series of two articles that applies two-stage short-run control charting to (X, MR) charts. Theory is developed and then used to derive the control chart factor equations. In the sequel, the control chart factor calculations are computerized and an example is presented

Brief Note: The Status of Ermine (Mustela erminea) in Ohio

Author Institution: Ohio Cooperative Fish and Wildlife Research Unit, The Ohio State University and Ohio Department of Natural Resources, Division of WildlifeThe status of ermine as regular inhabitants of Ohio has been unclear. Previously, only three records were known from the state. In this study, size 0 leghold traps and 8 x 8 x 25 cm Sherman traps were used to capture five ermine in Ashtabula and Trumbull Counties during summer 1987 and two in Trumbull County in 1988. Ermine are permanent residents in at least Trumbull County

A Computer Program to Calculate Two-Stage Short-Run Control Chart Factors for (X,MR) Charts

This paper is the second in a series of two papers that fully develops two-stage short-run (X, MR) control charts. This paper describes the development and execution of a computer program that accurately calculates first- and second-stage short-run control chart factors for (X, MR) charts using the equations derived in the first paper. The software used is Mathcad. The program accepts values for number of subgroups, α for the X chart, and α for the MR chart both above the upper control limit and below the lower control limit. Tables are generated for specific values of these inputs and the implications of the results are discussed. A numerical example illustrates the use of the program

Durability and Damage Tolerance of High Temperature Polymeric Composites

Modern durability and damage tolerance predictions for composite material systems rely on accurate estimates of the local stress and material states for each of the constituents, as well as the manner in which the constituents interact. In this work, an number of approaches to estimating the stress states and interactions are developed. First, an elasticity solution is presented for the problem of a penny-shaped crack in an N-phase composite material system opened by a prescribed normal pressure. The stress state around such a crack is then used to estimate the stress concentrations due to adjacent fiber fractures in composite materials. The resulting stress concentrations are then used to estimate the tensile strength of the composite. The predicted results are compared with experimental values. In addition, a cumulative damage model for fatigue is presented. Modifications to the model are made to include the effects of variable amplitude loading. These modifications are based upon the use of remaining strength as a damage metric and the definition of an equivalent generalized time. The model is initially validated using results from the literature. Also, experimental data from APC-2 laminates and IM7/K3B laminates are used in the model. The use of such data for notched laminates requires the use of an effective hole size, which is calculated based upon strain distribution measurements. Measured remaining strengths after fatigue loading are compared with the predicted values for specimens fatigued at room temperature and 350 F (177 C)

Plasma oscillations

The equivalence of the Landau and Van Kampen treatments of the initial value problem for plasma oscillations is demonstrated. Using completeness and orthogonality theorems for the normal modes, and integral representation for the solution of the initial value problem is obtained which is shown to be identical with that obtained by modifying the integration contour in Landau's Laplace Transform solution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32456/1/0000540.pd

Development and Analysis of an Advertising Model Concept

Industrial Engineering and Managemen