A Model Management Platform for Industry 4.0 – Enabling Management of Machine Learning Models in Manufacturing Environments

2020 ◽  
pp. 403-417 ◽  
Author(s):  
Christian Weber ◽  
Pascal Hirmer ◽  
Peter Reimann
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Model Management ◽  
Learning Models ◽  
Management Platform ◽  
Manufacturing Environments ◽  
Machine Learning Models

scholarly journals A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

2021 ◽  
Author(s):  
Mert Gülçür ◽  
Ben Whiteside
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Injection Moulding ◽  
Industry 4.0 ◽  
Supervised Machine Learning ◽  
Process Conditions ◽  
Learning Models ◽  
Micro Injection Moulding ◽  
Machine Learning Models

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

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.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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