scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 641-646
Author(s):  
Peter Burggräf ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro R. Pérez M. ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Lessons Learned

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

Lessons Learned as Organizational Project Memories

2010 ◽  
pp. 97-114 ◽  
Author(s):  
Raul M. Abril ◽  
Ralf Müller
Keyword(s):  
Quality Assurance ◽  
Organizational Learning ◽  
Goodness Of Fit ◽  
Case Study Research ◽  
Past Research ◽  
Third Parties ◽  
Lessons Learned ◽  
Study Research ◽  
Common Understanding

This chapter suggests established research approaches to capture and validate project lessons learned. Past research indicates that due to the temporal nature of projects, improper management of knowledge, especially lessons learned, constitutes a risk for present and future projects. The authors argue that case study research is appropriate for developing lessons learned and that an inductive methodology can be used to generate hypotheses. These hypotheses are validated through an analysis of their Goodness of Fit into learning related business questions. Quality assurance in a lessons learned process should include a formalism to avoid loosing knowledge in the coding process, a formalism to avoid equivocality in the knowledge transfer to third parties, and validation techniques for the identified knowledge items. Furthermore, the authors argue that a common understanding should be achieved before organizational learning influences decisions and/or actions.

scholarly journals Industry 4.0 and the New Simulation Modelling Paradigm

Organizacija ◽  
2017 ◽  
Vol 50 (3) ◽  
pp. 193-207 ◽  
Author(s):  
Blaž Rodič
Keyword(s):  
Industry 4.0 ◽  
Real Life ◽  
Multiple Case Study ◽  
Simulation Modelling ◽  
Lessons Learned ◽  
New Paradigm ◽  
Scientific Publications ◽  
Case Study Methodology ◽  
Multiple Case

Abstract Background and Purpose: The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R&D cases involving academia and industry. Design: We introduce the Industry 4.0 paradigm, presents its background, current state of development and its influence on the development of the simulation modelling paradigm. Further, we present the multiple case study methodology and examine several research and development projects involving automated industrial process modelling, presented in recent scientific publications and conclude with lessons learned. Results: We present the research problems and main results from five individual cases of adoption of the new simulation modelling paradigm. Main lesson learned is that while the new simulation modelling paradigm is being adopted by big companies and SMEs, there are significant differences depending on company size in problems that they face, and the methodologies and technologies they use to overcome the issues. Conclusion: While the examined cases indicate the acceptance of the new simulation modelling paradigm in the industrial and scientific communities, its adoption in academic environment requires close cooperation with industry partners and diversification of knowledge of researchers in order to build integrated, multi-level models of cyber-physical systems. As shown by the presented cases, lack of tools is not a problem, as the current generation of general purpose simulation modelling tools offers adequate integration options.

scholarly journals Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
László Nagy ◽  
Tamás Ruppert ◽  
János Abonyi
Keyword(s):  
Industry 4.0 ◽  
Lessons Learned ◽  
Building Industry ◽  
Manufacturing Processes ◽  
Multilayer Network ◽  
Industrial Case Study ◽  
Wire Harness ◽  
Manufacturing Management ◽  
Harness Assembly

Effective information management is critical for the development of manufacturing processes. This paper aims to provide an overview of ontologies that can be utilized in building Industry 4.0 applications. The main contributions of the work are that it highlights ontologies that are suitable for manufacturing management and recommends the multilayer-network-based interpretation and analysis of ontology-based databases. This article not only serves as a reference for engineers and researchers on ontologies but also presents a reproducible industrial case study that describes the ontology-based model of a wire harness assembly manufacturing process.

scholarly journals In-Service Training Management—A Case Study in Vietnam

2020 ◽  
Vol 13 (10) ◽  
pp. 1
Author(s):  
Anh Nguyen
Keyword(s):  
Quality Assurance ◽  
Lessons Learned ◽  
Input Process ◽  
Quality Assurance Process ◽  
Service Training ◽  
Process Output ◽  
It Training ◽  
Assurance Process ◽  
Training Programmes

In-service training is an integral part of an educational system as teachers always need to update their educational and professional knowledge for a more effective teaching delivery. Ensuring the effectiveness of the in-service training programmes is, however, very challenging. In this article, we introduce the use of the CIPO model, i.e., Context-Input-Process-Output, for in-service training management. We extend the classical CIPO model by integrating into it a quality assurance process. We conduct a case study in Vietnam over a sample of 163 teachers and develop an IT training course to demonstrate how the new model can be used in teacher training management. Finally, we discuss about the lessons learned and recommendations.

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