Opportunistic Meta-Learning: A Case Study for Quality Assurance in Industry 4.0 Environments

2020 ◽  
Author(s):  
Simon Reichhuber ◽  
Sven Tomlorde
Keyword(s):  
Quality Assurance ◽  
Industry 4.0 ◽  
Meta Learning

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.

scholarly journals The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

Industry 4.0 Impact on Evolution of Product Development: The Bicycle Saddle Case Study

2020 ◽  
Author(s):  
Mohsen Memaran ◽  
Cristiana Delprete ◽  
Eugenio Brusa ◽  
Abbas Razavykia ◽  
Paolo Baldissera
Keyword(s):  
Product Development ◽  
Industry 4.0

scholarly journals Design Framework Based on TEC21 Educational Model and Education 4.0 Implemented in a Capstone Project: A Case Study of an Electric Vehicle Suspension System

2021 ◽  
Vol 13 (11) ◽  
pp. 5768
Author(s):  
Hugo A López ◽  
Pedro Ponce ◽  
Arturo Molina ◽  
María Soledad Ramírez-Montoya ◽  
Edgar Lopez-Caudana
Keyword(s):  
Electric Vehicle ◽  
Industry 4.0 ◽  
Engineering Students ◽  
Traditional Education ◽  
Educational Model ◽  
Capstone Project ◽  
Future Challenges ◽  
Vehicle Suspension System ◽  
Competencies Development

Nowadays, engineering students have to improve specific competencies to tackle the challenges of 21st-century-industry, referred to as Industry 4.0. Hence, this article describes the integration and implementation of Education 4.0 strategies with the new educational model of our university to respond to the needs of Industry 4.0 and society. The TEC21 Educational Model implemented at Tecnologico de Monterrey in Mexico aims to develop disciplinary and transversal competencies for creative and strategic problem-solving of present and future challenges. Education 4.0, as opposed to traditional education, seeks to provide solutions to these challenges through innovative pedagogies supported by emerging technologies. This article presents a case study of a Capstone project developed with undergraduate engineering students. The proposed structure integrates the TEC21 model and Education 4.0 through new strategies and laboratories, all linked to industry. The results of a multidisciplinary project focused on an electric vehicle racing team are presented, composed of Education 4.0 elements and competencies development in leadership, innovation, and entrepreneurship. The project was a collaboration between academia and the productive sector. The results verified the students’ success in acquiring the necessary competencies and skills to become technological leaders in today’s modern industry. One of the main contributions shown is a suitable education framework for bringing together the characteristics established by Education 4.0 and achieved by our educational experience based on Education 4.0.

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