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Hajo Wiemer

Head of the “Ceramics” Innovation Center

TU Dresden

Germany

Research Interests

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About

Hajo Wiemer is the Head of Department for Process Informatics and Machine Data Analysis at TU Dresden, Germany. His research focuses on fields such as additive manufacturing, machine learning for materials design, and data analysis in engineering contexts. Recent publications include work on automatic information extraction from scientific publications and the application of AutoML in production engineering. He is actively contributing to advancements in collaborative materials science and engineering projects.

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Collaborators (5)

Alexander Dementyev

GERMANY

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Michael Moeckel

Professor

Aschaffenburg University of Applied Sciences

GERMANY

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Eugen Boos

Technische Universität Dresden

GERMANY

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Martina Zimmermann

Professor in Mechanics of Materials and Failure Analysis

Technische Universität Dresden

GERMANY

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Lars Penter

Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU

GERMANY

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Hajo Wiemer

Research Interests

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About

Articles (27)

State-Centric Framework for Canonical Representation and Change Analysis of Evolving Documents in Information Systems

Hierarchical Hash-Based Change Detection for Near-Real-Time Instruction Updates in Manufacturing

Effects of cross-factory horizontal and cross-stage vertical data integration on predictive readiness for data-driven manufacturing optimization

A Modular Solution Concept for Self-Configurable Electronic Lab Notebooks: Systematic Theoretical Demonstration and Validation Across Diverse Digital Platforms

Automatic Information Extraction from Scientific Publications Based on the Use Case of Additive Manufacturing

Benchmarking and Lessons Learned from Using SharePoint as an Electronic Lab Notebook in Engineering Joint Research Projects

Sharing Research Data in Collaborative Material Science and Engineering Projects

Leveraging multi-task learning regressor chains for small and sparse tabular data in materials design

Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies

Application of time series snippets

Quantitative Assessment of Data Volume Requirements for Reliable Machine Learning Analysis

A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science

Time Series Management in Data Acquisition and Analysis for Prototyping in Production Engineering

Input parameter specific uncertainty quantification of deep learning models using monte carlo dropout

Recent developments in the field of additive manufacturing of ceramic-based multi-material components open the door to highly functionalized components

An Engineer-Friendly Terminology of White, Black and Grey-Box Models

AutoML Applied to Time Series Analysis Tasks in Production Engineering

Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine

Investigation of alternative attention modules in transformer models for remaining useful life predictions

Additive Manufacturing of Ceramics opens the door to Components for Biomanufacturing Applications with high Functionality

Need for UAI–Anatomy of the Paradigm of Usable Artificial Intelligence for Domain-Specific AI Applicability

Benchmarking AutoML for regression tasks on small tabular data in materials design

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

A holistic quality assurance approach for machine learning applications in cyber‐physical production systems

A proposal for a systematization and taxonomy of methods to rectify thermally induced errors on existing machine tools

Process data-based knowledge discovery in additive manufacturing of ceramic materials by multi-material jetting (CerAM MMJ)

Thermo-elastic structural analysis of a machine tool using a multi-channel absolute laser interferometer

Collaborators (5)

Alexander Dementyev

Michael Moeckel

Eugen Boos

Martina Zimmermann

Lars Penter

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