INVENTORY CONTROL SYSTEM : Optimization of production system and reliability

The main idea of this research to solve the problem of inventory management for the paper industry SPM PVT limited. The aim of this research was to find a methodology by which the inventory of raw material could be kept at minimum level by means of buffer stock level.The main objective then lies in finding the minimum level of buffer stock according to daily consumption of raw material, finding the Economic Order Quantity (EOQ) reorders point and how much order will be placed in a year to control the shortage of raw material.In this project, we discuss continuous review model (Deterministic EOQ models) that includes the probabilistic demand directly in the formulation. According to the formula, we see the reorder point and the order up to model. The problem was tackled mathematically as well as simulation modeling was used where mathematically tractable solution was not possible.The simulation modeling was done by Awesim software for developing the simulation network. This simulation network has the ability to predict the buffer stock level based on variable consumption of raw material and lead-time. The data collection for this simulation network is taken from the industrial engineering personnel and the departmental studies of the concerned factory. At the end, we find the optimum level of order quantity, reorder point and order days.

Supervision as control system : the design of supervision as a regulatory instrument in the social services sector in Sweden

In this article, the prevailing official view of supervision as a regulatory instrument is examined as it applies to the social services sector in Sweden. The study is based on a comparison of the views expressed on the design of supervision as a regulatory instrument by two government commissions, the Supervision Commission and the Commission on Supervision within the Social Services (UTIS), and on the positions taken by the Government regarding the definitions of the concept of supervision proposed by these commissions. The view of supervision as a regulatory instrument expressed in these policy documents is analysed with the help of a theoretical framework describing the components, their functions and the governance characteristics of control systems. In the framework separate interrelated characteristics of the components are identified and summarized into two models of control systems. The analysis shows that supervision in the Swedish social services sector can be described in terms of both a disciplinary and non-disciplinary system. By its system theoretical basis and the identification of interrelated characteristics the study contributes to a broadened understanding of the construction and functions of supervision as a regulatory instrument and of how supervision within the Swedish social sector is meant to be designed.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Kvalitetskontroll av utskrifter från Kodak Approval XP4

When using the digital halftone proofing systems, a closer print match can be achieved compared to what earlier couldbe done with the analogue proofing systems. These proofing systems possibilities to produce accurate print match canas well lead to producing bad print matches as several print related parameters can be adjusted manually in the systemby the user. Therefore, more advanced knowledge in graphic arts technology is required by the user of the system.The prepress company Colorcraft AB wishes to control that their color proofs always have the right quality. This projectwas started with the purpose to find a quality control metod for Colorcraft´s digital halftone proofing system(Kodak Approval XP4).Using a software who supports spectral measuring combined with a spectrophotometer and a control bar, a qualitycontrol system was assembled. This system detects variations that lies out of the proofing system´s natural deviation.The prerequisite for this quality control system is that the tolerances are defined with consideration taken to the proofingsystems natural deviations. Othervise the quality control system will generate unnecessecary false alarms and thereforenot be reliable.