64 research outputs found
Data analytics for time constraint adherence prediction in a semiconductor manufacturing use-case
Semiconductor manufacturing represents a challenging industrial environments, where products require more than several hundred operations, each representing the technical state-of-the-art. Products vary greatly in volume, design and required production processes and, additionally, product portfolios and technologies change rapidly. Thus, technologically restricted rapid product development, stringent quality related clean room requirements and high precision manufacturing equipment application enforce operational excellence, in particular time constraints adherence. Product specific time constraints between two or more successive process operations are an industry-specific challenge, as violations lead to additional scrapping or reworking costs. Time constraint adherence is linked to dispatching and currently manually assessed. To overcome this error-prone manual task, this article presents a data-based decision process to predict time constraint adherence in semiconductor manufacturing. Real-world historical data is analyzed and appropriate statistical models and scoring functions derived. Compared to other relevant literature regarding time constraint violations, the central contribution of this article is the design, generation and validation of a model for product quality-related time constraint adherence based on a real-world semiconductor plant
Machine Learning in Manufacturing towards Industry 4.0: From âFor Nowâ to âFour-Knowâ
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, âFour-Knowâ (Know-what, Know-why, Know-when, Know-how) and âFour-Levelâ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments
This book presents the collection of fifty two papers which were presented on the First International Conference on BUSINESS SUSTAINABILITY â08 - Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments, held in Ofir, Portugal, from 25th to 27th of June, 2008. The main motive of the meeting was the growing awareness of the importance of the sustainability issue. This importance had emerged from the growing uncertainty of the market behaviour that leads to the characterization of the market, i.e. environment, as turbulent. Actually, the characterization of the environment as uncertain and turbulent reflects the fact that the traditional technocratic and/or socio-technical approaches cannot effectively and efficiently lead with the present situation. In other words, the rise of the sustainability issue means the quest for new instruments to deal with uncertainty and/or turbulence.
The sustainability issue has a complex nature and solutions are sought in a wide range of domains and instruments to achieve and manage it. The domains range from environmental sustainability (referring to natural environment) through organisational and business sustainability towards social sustainability. Concerning the instruments for sustainability, they range from traditional engineering and management methodologies towards âsoftâ instruments such as knowledge, learning, creativity. The papers in this book address virtually whole sustainability problems space in a greater or lesser extent. However, although the uncertainty and/or turbulence, or in other words the dynamic properties, come from coupling of management, technology, learning, individuals, organisations and society, meaning that everything is at the same time effect and cause, we wanted to put the emphasis on business with the intention to address primarily the companies and their businesses.
From this reason, the main title of the book is âBusiness Sustainabilityâ but with the approach of coupling Management, Technology and Learning for individuals, organisations and society in Turbulent Environments.
Concerning the First International Conference on BUSINESS SUSTAINABILITY, its particularity was that it had served primarily as a learning environment in which the papers published in this book were the ground for further individual and collective growth in understanding and perception of sustainability and capacity for building new instruments for business sustainability. In that respect, the methodology of the conference work was basically dialogical, meaning promoting dialog on the papers, but also including formal paper presentations. In this way, the conference presented a rich space for satisfying different authorsâ and participantsâ needs. Additionally, promoting the widest and global learning environment and participativeness, the Conference Organisation provided the broadcasting over Internet of the Conference sessions, dialogical and formal presentations, for all authorsâ and participantsâ institutions, as an innovative Conference feature.
In these terms, this book could also be understood as a complementary instrument to the Conference authorsâ and participantsâ, but also to the wider readershipsâ interested in the sustainability issues.
The book brought together 97 authors from 10 countries, namely from Australia, Finland, France, Germany, Ireland, Portugal, Russia, Serbia, Sweden and United Kingdom. The authors ârangedâ from senior and renowned scientists to young researchers providing a rich and learning environment.
At the end, the editors hope and would like that this book will be useful, meeting the expectation of the authors and wider readership and serving for enhancing the individual and collective learning, and to incentive further scientific development and creation of new papers.
Also, the editors would use this opportunity to announce the intention to continue with new editions of the conference and subsequent editions of accompanying books on the subject of BUSINESS SUSTAINABILITY, the second of which is planned for year 2011.info:eu-repo/semantics/publishedVersio
Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)
Proceedings of the CPSL 202
Proceedings of the Conference on Production Systems and Logistics: CPSL 2022
[no abstract available
Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry
Problem Statement: Predictive Analytics (PA) may effectively support semiconductor
industry (SI) companies in order to manage the special challenges in SI value chains. To
discover the implications of PA, the realistic benefits as well as its limitations of its
application to semiconductor manufacturing, it is necessary to assess in which ways the
application of PA affects the production system (PS) performances. However, based on the
literature survey, the influences of PA on the various performance characteristics of an SI PS
are not as clear as expected for the efficiently operative application. Besides, the existing
performance models are not effective to predict the impacts of PA on the SI PS
performances. Therefore, the overall aim of this thesis is to analyse and evaluate the
impacts of PA on the SI PS performances and to identify under which conditions a PA
application would generate the most significant performance improvements. The focus of this
thesis is predictive maintenance (PdM).
Research Methodology: Based on a post-positivist philosophy, the thesis applies a
deductive research approach using mixed-methods for data collection. The research design
has the following stages: (1) theory, (2) hypothesis, (3) state of research, (4) case study and
(5) verification.
Main Achievements: (1) The systematic literature review is carried out to identify the gaps
of the existing research and based on these findings, a conceptual framework is proposed
and developed. (2) The existing performance models are analysed and evaluated against
their applicability to this study. (3) A causal loop model for SI PS is generated based on the
assessment of experts with industrial engineering and equipment maintenance expertise. (4)
An expert system is developed and evaluated in order to investigate transitive and
contradictory effects of PdM on SI PS performances. (5) A simulation model is developed
and validated for investigating the strengths and limitations of PdM regarding SI PS
performances under different circumstances.
Results: The results of the logical inference study show that PdM has 34 positive effects as
well as 4 contradictory effects on SI PS performance characteristics. Based on the various
simulation experiments, it has been found that (1) âMean Time to Repairâ decreases only if
PdM supports proportionate reduction of failures and repair times. (2) Logistics performance
improves only if the underlying workcenter is limited in capacity or the four partners are nonsynchronous.
(3) PdM supports optimal cost decreases for workcenters where the degree of
exhausting wear limits can be most effectively improved and (4) the degree of yield
improvement gained by PdM is dependent on the operation scrap rate. However, (5) if a
workcenter has overcapacity, PdM will potentially worsen PS performances, even if the
particular workcenter performance can be improved. These new insights advance existing
knowledge in production managements when adopting predictive technologies at SI PS in
order to improve PS performances. The findings above enable SI practitioners to justify a PdM investment and to select suitable workcenters in order to improve SI PS performances
by applying the proposed PdM.
Contributions: The main contributions of this PhD project can be divided into practical
application and theoretical work.
The contributions from the theoretical perspective are:
1) The critical review and evaluation of the state of the research for PA in the context of
semiconductor manufacturing and the models for predicting and evaluating SI PS
performances.
2) A new framework for investigating the implications of PA on the challenges such as
gaining high utilizations and controlling the variability in production processes in SI
value chains.
3) The new knowledge about transitive and contradictory effects of PdM on SI PS
performances, which indicates that PdM can be used to improve PS performances
beyond a single machine.
4) The new knowledge about strengths and limitations of PdM in order to improve SI
PS performances under particular circumstances.
The contributions from the practical application perspective are:
1) A practical method for identifying workcenters where PdM delivers the most
significant benefits for SI PS performances.
2) An expert system that provides a comprehensive knowledge base about causes and
effects within SI PS in order to justify a PdM investment.
3) A concise review of important PA applications, their capabilities for the wafer
fabrication and the most suited PA methods. These findings can be adopted by SI
practitioners
NASA Tech Briefs, August 1993
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