Balancing Flexibility and Lean in Manufacturing Environments

2017 ◽  
pp. 179-212
Author(s):  
Gwendolyn Holowecky ◽  
Ratna Babu Chinnam
Keyword(s):  
Manufacturing Environments

PCB Related Field Failures with ImAg Surface Finishes

2007 ◽  
Author(s):  
Anil Kurella ◽  
Aravind Munukutla ◽  
J.S. Lewis
Keyword(s):  
Emerging Markets ◽  
Galvanic Corrosion ◽  
Electrochemical Migration ◽  
Copper Sulphide ◽  
Electronic Components ◽  
Related Field ◽  
Surface Finishes ◽  
Immersion Silver ◽  
Manufacturing Environments

Abstract PCB surface finishes like Immersion silver (ImAg) are commonly used in Pb-free manufacturing environments following RoHS legislation. With this transition, however the numbers of field failures associated with electrochemical migration, copper sulphide corrosion, via barrel galvanic corrosion are on a steady rise. More often than not ImAg surfaces seem to assist these failing signatures. As computers penetrate into emerging markets with humid and industrialized environments there is a greater concern on the reliability and functionality of these electronic components.

scholarly journals Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

2021 ◽  
Vol 11 (7) ◽  
pp. 3188
Author(s):  
Xixiang Wang ◽  
Jiafu Wan
Keyword(s):  
Knowledge Sharing ◽  
Comparative Experiment ◽  
Mixed Flow ◽  
Processing Technologies ◽  
Experiment Platform ◽  
Low Degree ◽  
Manufacturing Resources ◽  
Ontological Model ◽  
Manufacturing Environments ◽  
Similar Processing

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform.

scholarly journals Surface Analysis of Chamber Coating Materials Exposed to CF4/O2 Plasma

Coatings ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 105
Author(s):  
Seung Hyun Park ◽  
Kyung Eon Kim ◽  
Sang Jeen Hong
Keyword(s):  
Photoelectron Spectroscopy ◽  
Yttrium Aluminum Garnet ◽  
Silicon Oxide ◽  
Ceramic Materials ◽  
High Plasma ◽  
Coating Materials ◽  
Continuous Increase ◽  
X Ray ◽  
Bonding Structure ◽  
Manufacturing Environments

Coating the inner surfaces of high-powered plasma processing equipment has become crucial for reducing maintenance costs, process drift, and contaminants. The conventionally preferred alumina (Al2O3) coating has been replaced with yttria (Y2O3) due to the long-standing endurance achieved by fluorine-based etching; however, the continuous increase in radio frequency (RF) power necessitates the use of alternative coating materials to reduce process shift in a series of high-powered semiconductor manufacturing environments. In this study, we investigated the fluorine-based etching resistance of atmospheric pressure-sprayed alumina, yttria, yttrium aluminum garnet (YAG), and yttrium oxyfluoride (YOF). The prepared ceramic-coated samples were directly exposed to silicon oxide etching, and the surfaces of the plasma-exposed samples were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. We found that an ideal coating material must demonstrate high plasma-induced structure distortion by the fluorine atom from the radical. For endurance to fluorine-based plasma exposure, the bonding structure with fluoride was shown to be more effective than oxide-based ceramics. Thus, fluoride-based ceramic materials can be promising candidates for chamber coating materials.

scholarly journals Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

2021 ◽  
pp. 095440542110144
Author(s):  
B Birch ◽  
CA Griffiths ◽  
A Morgan
Keyword(s):  
Speech Recognition ◽  
Voice Recognition ◽  
Human Robot Interaction ◽  
Hole Drilling ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Robot Interaction ◽  
Extraction Algorithm ◽  
Dynamic Time ◽  
Manufacturing Environments

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

scholarly journals Improving Manufacturing Applications of Machine Learning by Understanding Defect Classification and the Critical Error Threshold

2021 ◽  
Author(s):  
David Blondheim
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Error Threshold ◽  
Supervised Machine Learning ◽  
Secondary Process ◽  
Defect Classification ◽  
Critical Error ◽  
Manufacturing Applications ◽  
Applications Of Machine Learning ◽  
Manufacturing Environments

AbstractMachine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.

scholarly journals A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223114-223129
Author(s):  
Kevin Nagorny ◽  
Sebastian Scholze ◽  
Armando Walter Colombo ◽  
Jose Barata Oliveira
Keyword(s):  
Data Acquisition ◽  
Smart Manufacturing ◽  
Manufacturing Environments

Active Service Using Acquaintance Nodes in Intelligent Manufacturing Environments

2010 ◽  
Vol 33 ◽  
pp. 241-245
Author(s):  
R. Wang
Keyword(s):  
System Performance ◽  
Intelligent Manufacturing ◽  
Optimal System ◽  
Service Architecture ◽  
Active Service ◽  
Active Services ◽  
Manufacturing Network ◽  
Manufacturing Environments

An active service architecture called AAS (active service based on acquaintances) for intelligent manufacturing environments was proposed. The proposed architecture exploits stable relation and resourceful nodes, called acquaintances, which perform active services in the manufacturing network. The AAS architecture recognizes node heterogeneity in terms of mobility and capability. We analyze the characteristics of AAS architecture and operations for optimal system performance. By showing that AAS outperforms broadcast-based solutions in manufacturing environments, we validate that AAS is a flexible and adaptable architecture appropriate for dynamic manufacturing environments.

Robotic Assembly Cell Simulation

1992 ◽  
Author(s):  
Jian Liu ◽  
J. P. Sadler
Keyword(s):  
Computer Simulation ◽  
Computer Modeling ◽  
Collision Detection ◽  
Robotic Assembly ◽  
Research Activities ◽  
Assembly Cell ◽  
Cell Simulation ◽  
Manufacturing Environments

Abstract A flexible robotic assembly cell is described, and some of the research activities involving the cell and robot applications in manufacturing environments are presented. This research relies heavily on computer simulation. Assembly cell computer modeling, cell calibration, robot collision detection, and off-line programming are described in this paper.

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