scholarly journals Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process

2021 ◽  
Vol 11 (15) ◽  
pp. 6832
Author(s):  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Ensemble Learning ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Data Communication ◽  
Predictive Maintenance ◽  
Learning Performance ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Total Quality

Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learning algorithms with adaptive learning to make a boosted decision tree more intelligent. The algorithm predicts main PdM issues, such as product failure or unqualified manufacturing equipment, in advance, thus improving the machine-learning performance. Herein, semiconductor and blister packing machine data are used separately in manufacturing data analytics. The former data help in predicting yield failure in a semiconductor manufacturing process. The blister packing machine data are used to predict the product packaging quality. Experimental results indicate that the proposed method is accurate, with an area under a receiver operating characteristic curve exceeding 96%. Thus, the proposed method provides a practical approach for PDM in semiconductor manufacturing processes and blister packing machines.

An Open Web-Based Repository for Capturing Manufacturing Process Information

2016 ◽  
Author(s):  
William Z. Bernstein ◽  
Mahesh Mani ◽  
Kevin W. Lyons ◽  
K. C. Morris ◽  
Björn Johansson
Keyword(s):  
Manufacturing Process ◽  
Recent Progress ◽  
Performance Assessments ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Process Information ◽  
Web Based ◽  
Model Consistency ◽  
Effective Models

With recent progress in developing more effective models for representing manufacturing processes, this paper presents an approach towards an open web-based repository for storing manufacturing process information. The repository is envisioned to include several new use cases in the context of information use in smart manufacturing. This paper examines several key benefits through usage scenarios engaging existing engineering activities. Based on the scenarios, the desired characteristics of an open web-based repository are presented, namely that it will be (1) complementary to existing practices, (2) open and net-centric, (3) able to enforce model consistency, (4) modular (5) extensible, and (5) able to govern contributions. A repository will support and motivate the ubiquitous and extended use of standardized representations of unit manufacturing processes in order to promote consistency of performance assessments across industries and provide a tangible, data-driven perspective for analysis-related activities. Furthermore, the paper presents additional benefits and possible applications that could result from a shared manufacturing repository.

scholarly journals Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

2021 ◽  
Vol 11 (21) ◽  
pp. 9945
Author(s):  
Ray-I Chang ◽  
Chia-Yun Lee ◽  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Industry 4.0 ◽  
Mean Squared Error ◽  
Production Quality ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Third Party ◽  
Manufacturing Processes ◽  
Textile Manufacturing ◽  
Iot Applications

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

A Review of Manufacturing Process Control

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Robert G. Landers ◽  
Kira Barton ◽  
Santosh Devasia ◽  
Thomas Kurfess ◽  
Prabhakar Pagilla ◽  
...  
Keyword(s):  
Process Control ◽  
Manufacturing Process ◽  
Raw Materials ◽  
The Body ◽  
Smart Manufacturing ◽  
Future Research ◽  
Manufacturing Processes ◽  
Monitoring And Control ◽  
Substantial Impact ◽  
Manufacturing Process Control

Abstract Smart manufacturing concepts are being integrated into all areas of manufacturing industries, from the device level (e.g., intelligent sensors) to the efficient coordination of business units. Vital components of any manufacturing enterprise are the processes that transform raw materials into components, assemblies, and finally products. It is the manufacturing process where smart manufacturing is poised to make substantial impact through process control, i.e., the intelligent manipulation of process variables to increase operation productivity and part quality. This article discusses three areas of manufacturing process control: control-oriented modeling, sensing and monitoring, and the design and construction of controllers. The discussion will center around the following manufacturing processes: machining, grinding, forming, joining, and additive. While many other important processes exist, the discussions of control of these mechanical manufacturing processes will form a framework commonly applied to these processes and the discussion will form a framework to provide insights into the modeling, monitoring, and control of manufacturing processes more broadly. Conclusions from these discussions will be drawn, and future research directions in manufacturing process control will be provided. This article acknowledges the contributions of two of the pioneering researchers in this field, Dr. Yoram Koren and Dr. Galip Ulsoy, who have made seminal contributions in manufacturing process control and continued to build the body of knowledge over the course of many decades.

scholarly journals APPLYING ONLINE PEER ASSESSMENT WITH TOTAL QUALITY MANAGEMENT TO ELEVATE PROJECT-BASED LEARNING PERFORMANCE

2015 ◽  
Vol 14 (3) ◽  
pp. 379-390 ◽  
Author(s):  
Shu-Hsuan Chang ◽  
Li-Chih Yu ◽  
Yen-Kuang Kuo ◽  
Yi-Ting Mai ◽  
Jen-De Chen
Keyword(s):  
Quality Management ◽  
Total Quality Management ◽  
Continuous Improvement ◽  
Peer Assessment ◽  
Project Based Learning ◽  
Learning Performance ◽  
Total Quality ◽  
Team Members ◽  
Significant Difference ◽  
Online Peer Assessment

Undergraduate science, technology, engineering, and mathematics (STEM) curriculum emphasize project-based learning (PBL) with peer assessment/on-line peer assessment (PA/OPA). Many studies have stressed that students did not improve over two rounds in PBL with OPA studies and PBL with PA have to adopt team mutual cooperation to reap effective learning. This study proposed an innovative approach that incorporate OPA with TQM as a macro-level instructional tool to guide students in teacher's directing of collaborative project development as well as seeking continuous improvement to elevate Project-Based Learning Performance in a STEM course. The effects of OPA with TQM were examined through an experiment with PBL performances hypotheses. A total of 63 junior students in an university of Taiwan voluntarily participated in this study and a quasi-experimental approach with a two-group design was adopted. The results revealed that the team members using the OPA with TQM approach tended to have higher design skill performance, better cohesive teamwork and creative problem solving attitude. Thus, the proposed approach facilitated team members to collaborate for seeking continuous improvement. However, no significant difference was reported on the influence of enhancing students’ design concept. Implication and suggestions for educators to promote the PBL with OPA and TQM were also provided in the study. Key words: collaborative learning, online peer assessment, project-based learning, STEM course, Total Quality Management.

scholarly journals Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

2021 ◽  
Author(s):  
Mohammadhossein Ghahramani
Keyword(s):  
Semiconductor Manufacturing ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Predictive Analysis ◽  
Manufacturing Processes ◽  
Decomposition Approach

<div>The dataset used in this work is obtained from a semiconductor factory, SECOM (Semiconductor Manufacturing) dataset and is publicly available online.</div>

scholarly journals Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

2021 ◽  
Author(s):  
Mohammadhossein Ghahramani
Keyword(s):  
Semiconductor Manufacturing ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Predictive Analysis ◽  
Manufacturing Processes ◽  
Decomposition Approach

<div>The dataset used in this work is obtained from a semiconductor factory, SECOM (Semiconductor Manufacturing) dataset and is publicly available online.</div>

scholarly journals The Effects of Implementing 5S as the Foundation for Work Improvement on the Workplace

2018 ◽  
Vol 1 (1) ◽  
pp. 451-455
Author(s):  
Celina Bartnicka
Keyword(s):  
Quality Management ◽  
Human Resources ◽  
Total Quality Management ◽  
Six Sigma ◽  
Manufacturing Process ◽  
Lean Manufacturing ◽  
Continuous Process ◽  
Technical Aspect ◽  
Total Quality ◽  
Organizational Aspect

Abstract The article presents the main aspects which require attention when optimizing and improving the manufacturing process, i.e.: the human resources aspect, the technical aspect and the organizational aspect. Concepts such as the Total Quality Management, Six Sigma or Lean Manufacturing claim that work improvement should be a continuous process. The foundation for implementing the improvement of processes, regardless of the concept, are well-organized and orderly workplaces as well as standardization, which can be achieved thanks to the Japanese 5S method.

Sign in / Sign up

Export Citation Format

Share Document