Minimization Of Rewok In Belt Industry Using Dmaic

The fast changing economic conditions such as global competition, declining profit margin, customer demand for high quality product, product variety and reduced lead–time etc. had a major impact on manufacturing industries. To respond to these needs a new paradigm in this area of manufacturing strategies is Six Sigma. The Six Sigma approach has been increasingly adopted world wide in the manufacturing sector in order to enhance productivity and quality performance and to make the process robust to quality variations. This paper discusses the quality and productivity improvement in a manufacturing enterprise through a case study. The paper deals with an application of Six Sigma DMAIC(Define–Measure- Analyze-Improve-Control) methodology in an industry which provides a framework to identify, quantify and eliminate sources of variation in an operational process in question, to optimize the operation variables, improve and sustain performance viz. process yield with well-executed control plans. Six Sigma improves the process performance (process yield) of the critical operational process, leading to better utilization of resources, decreases variations & maintains consistent quality of the process output

Complex low volume electronics simulation tool to improve yield and reliability

Assembly of Printed Circuit Boards (PCB) in low volumes and a high-mix requires a level of manual intervention during product manufacture, which leads to poor first time yield and increased production costs. Failures at the component-level and failures that stem from non-component causes (i.e. system-level), such as defects in design and manufacturing, can account for this poor yield. These factors have not been incorporated in prediction models due to the fact that systemfailure causes are not driven by well-characterised deterministic processes. A simulation and analysis support tool being developed that is based on a suite of interacting modular components with well defined functionalities and interfaces is presented in this paper. The CLOVES (Complex Low Volume Electronics Simulation) tool enables the characterisation and dynamic simulation of complete design; manufacturing and business processes (throughout the entire product life cycle) in terms of their propensity to create defects that could cause product failure. Details of this system and how it is being developed to fulfill changing business needs is presented in this paper. Using historical data and knowledge of previous printed circuit assemblies (PCA) design specifications and manufacturing experiences, defect and yield results can be effectively stored and re-applied for future problem solving. For example, past PCA design specifications can be used at design stage to amend designs or define process options to optimise the product yield and service reliability

Proceedings of the Fifteenth Annual Software Engineering Workshop

The Software Engineering Laboratory (SEL) is an organization sponsored by GSFC and created for the purpose of investigating the effectiveness of software engineering technologies when applied to the development of applications software. The goals of the SEL are: (1) to understand the software development process in the GSFC environment; (2) to measure the effect of various methodologies, tools, and models on this process; and (3) to identify and then to apply successful development practices. Fifteen papers were presented at the Fifteenth Annual Software Engineering Workshop in five sessions: (1) SEL at age fifteen; (2) process improvement; (3) measurement; (4) reuse; and (5) process assessment. The sessions were followed by two panel discussions: (1) experiences in implementing an effective measurement program; and (2) software engineering in the 1980's. A summary of the presentations and panel discussions is given

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Additive Manufacturing

The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven in-situ quality monitoring based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning model to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in an additive manufacturing process, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review