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 A Reference Schema for the Unit Manufacturing Process Information Model

2019 ◽  
Vol 124 ◽  
Author(s):  
William Z. Bernstein ◽  
David Lechevalier
Keyword(s):  
Manufacturing Process ◽  
Information Model ◽  
Manufacturing Processes ◽  
Environmental Aspects ◽  
Process Information ◽  
Web Based ◽  
Reference Implementation

This document presents supporting documentation for a reference implementation of the UnitManufacturing Process (UMP) information model presented in ASTM E3012, Standard Guide for Characterizing Environmental Aspects of Manufacturing Processes. A version of this schema is used inthe UMP Builder, a web-based toolkit for recording and storing UMP models.

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.

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.

UMP Builder: Capturing and Exchanging Manufacturing Models for Sustainability

2018 ◽  
Author(s):  
William Z. Bernstein ◽  
David Lechevalier ◽  
Don Libes
Keyword(s):  
Environmental Analysis ◽  
Environmental Sustainability ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Information Model ◽  
Process Models ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Initial Layer ◽  
Early Use

Targeting the improvement of environmental analysis of manufacturing systems, ASTM 3012-16 provides guidelines for formally characterizing manufacturing processes. However, the difficulty that has arisen in the early use of the standard illustrates the need for intuitive tools for helping modeling experts to conform to the specified information model. In response, we present the Unit Manufacturing Process (UMP) Builder, a browser-based tool integrating symbolic mathematical and guided textual inputs, helping to consistently record and exchange manufacturing process models for environmental sustainability. The tool provides an initial layer of governance and verification with respect to the conformance to ASTM 3012-16. In this paper, we (1) detail the requirements with developing such a tool, (2) propose an improved schema to represent UMP models accommodating data-driven techniques, and (3) demonstrate the tool using a contributed model from an open challenge for modeling manufacturing processes.

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 AI-based modeling and data-driven evaluation for smart manufacturing processes

2020 ◽  
Vol 7 (4) ◽  
pp. 1026-1037 ◽  
Author(s):  
Mohammadhossein Ghahramani ◽  
Yan Qiao ◽  
MengChu Zhou ◽  
Adrian O Hagan ◽  
James Sweeney
Keyword(s):  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Processes

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>

Development of a Part-Focused Manufacturing Process Ontology: Exploring Use and Applications

2015 ◽  
Author(s):  
Lucas Mesmer ◽  
Andrew Olewnik
Keyword(s):  
Manufacturing Process ◽  
Development Process ◽  
Manufacturing Processes ◽  
Process Ontology ◽  
Web Based ◽  
Engineering Knowledge ◽  
Limited Knowledge

Numerous ontologies have been introduced to represent and activate engineering knowledge across the development process. However, many of the ontologies in the manufacturing domain provide limited usability for individuals with limited knowledge of manufacturing processes available within the domain. Similarly, web-based e-sourcing portals lack the ability to adequately pair components whose developers have limited knowledge of manufacturing processes with manufacturers who are able to produce their product. Motivated by these current gaps, an ontology is proposed which is designed to enable both users with a limited knowledge and those with a preexisting knowledge of manufacturing to identify potential processes. The Part-focused Manufacturing Process Ontology (PMPO) is designed around the concept that manufacturing processes can be selected based upon desired features and attributes of a component/product. This differs from past ontologies that model the manufacturing process domain on the characteristics of the resources utilized during the process. Further, the ontology is populated with some manufacturer data and its functionality demonstrated using various example situations.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

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