Benjamin Maschler

788 total citations · 1 hit paper
17 papers, 492 citations indexed

About

Benjamin Maschler is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Benjamin Maschler has authored 17 papers receiving a total of 492 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Industrial and Manufacturing Engineering, 6 papers in Artificial Intelligence and 5 papers in Control and Systems Engineering. Recurrent topics in Benjamin Maschler's work include Flexible and Reconfigurable Manufacturing Systems (8 papers), Digital Transformation in Industry (5 papers) and Machine Learning and ELM (4 papers). Benjamin Maschler is often cited by papers focused on Flexible and Reconfigurable Manufacturing Systems (8 papers), Digital Transformation in Industry (5 papers) and Machine Learning and ELM (4 papers). Benjamin Maschler collaborates with scholars based in Germany and United States. Benjamin Maschler's co-authors include Michael Weyrich, Nada Sahlab, B. Lindemann, Nasser Jazdi, Hannes Vietz, Behrang Ashtari Talkhestani, Andreas Löcklin, Timo Müller, Daniel Ewert and Hasan Tercan and has published in prestigious journals such as Computers in Industry, at - Automatisierungstechnik and OPUS Publication Server of the University of Stuttgart (University of Stuttgart).

In The Last Decade

Benjamin Maschler

17 papers receiving 468 citations

Hit Papers

A survey on anomaly detection for technical systems using... 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A survey on anomaly detection for technical systems using LSTM networks
    2021 242 citations B. Lindemann, Benjamin Maschler et al. Computers in Industry profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Benjamin Maschler Germany 11 204 127 118 79 61 17 492
Nada Sahlab Germany 7 149 0.7× 252 2.0× 95 0.8× 105 1.3× 53 0.9× 20 565
Fahed Mostafa Australia 7 116 0.6× 159 1.3× 94 0.8× 81 1.0× 35 0.6× 7 416
Ying Guo China 11 107 0.5× 248 2.0× 77 0.7× 93 1.2× 102 1.7× 65 545
Esra’a Alkafaween Jordan 7 118 0.6× 55 0.4× 57 0.5× 43 0.5× 85 1.4× 9 499
Diana Manjarrés Spain 12 229 1.1× 66 0.5× 93 0.8× 104 1.3× 142 2.3× 26 644
Miguel A. Prada Spain 14 95 0.5× 47 0.4× 121 1.0× 123 1.6× 116 1.9× 54 507
Rami Abielmona Canada 12 124 0.6× 76 0.6× 55 0.5× 166 2.1× 67 1.1× 73 505
Najdan Vuković Serbia 11 240 1.2× 128 1.0× 116 1.0× 30 0.4× 74 1.2× 15 539
Jinghuan Guo China 4 154 0.8× 50 0.4× 63 0.5× 45 0.6× 62 1.0× 6 393
Daqing Wu China 6 123 0.6× 56 0.4× 103 0.9× 30 0.4× 43 0.7× 11 400

Countries citing papers authored by Benjamin Maschler

Since Specialization
Citations

This map shows the geographic impact of Benjamin Maschler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Benjamin Maschler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Maschler more than expected).

Fields of papers citing papers by Benjamin Maschler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Benjamin Maschler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Benjamin Maschler. The network helps show where Benjamin Maschler may publish in the future.

Co-authorship network of co-authors of Benjamin Maschler

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Maschler. A scholar is included among the top collaborators of Benjamin Maschler based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Benjamin Maschler. Benjamin Maschler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Müller, Timo, et al.. (2023). Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management. Procedia CIRP. 118. 270–275. 1 indexed citations
2.
Maschler, Benjamin, et al.. (2022). Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries. Procedia CIRP. 112. 513–518. 16 indexed citations
3.
Maschler, Benjamin, et al.. (2022). Insights and Example Use Cases on Industrial Transfer Learning. Procedia CIRP. 107. 511–516. 13 indexed citations
4.
Maschler, Benjamin, Timo Müller, Andreas Löcklin, & Michael Weyrich. (2022). Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems. Procedia CIRP. 112. 220–225. 10 indexed citations
5.
Maschler, Benjamin, et al.. (2021). Deep industrial transfer learning at runtime for image recognition. at - Automatisierungstechnik. 69(3). 211–220. 15 indexed citations
6.
Maschler, Benjamin, et al.. (2021). Deep learning based soft sensors for industrial machinery. Procedia CIRP. 99. 662–667. 9 indexed citations
7.
Jazdi, Nasser, Behrang Ashtari Talkhestani, Benjamin Maschler, & Michael Weyrich. (2021). Realization of AI-enhanced industrial automation systems using intelligent Digital Twins. Procedia CIRP. 97. 396–400. 37 indexed citations
8.
Maschler, Benjamin, et al.. (2021). Towards establishing formal verification and inductive code synthesis in the PLC domain. 1–8. 2 indexed citations
9.
Lindemann, B., Benjamin Maschler, Nada Sahlab, & Michael Weyrich. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry. 131. 103498–103498. 242 indexed citations breakdown →
10.
Maschler, Benjamin, et al.. (2021). Transfer learning as an enabler of the intelligent digital twin. Procedia CIRP. 100. 127–132. 46 indexed citations
11.
Maschler, Benjamin, et al.. (2021). Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing. Procedia CIRP. 104. 452–457. 28 indexed citations
12.
Maschler, Benjamin, Hannes Vietz, Nasser Jazdi, & Michael Weyrich. (2020). Continual Learning of Fault Prediction for Turbofan Engines using Deep Learning with Elastic Weight Consolidation. 959–966. 36 indexed citations
13.
Maschler, Benjamin, et al.. (2020). User-friendly, requirement based assistance for production workforce using an asset administration shell design. Procedia CIRP. 91. 402–406. 15 indexed citations
14.
Maschler, Benjamin, et al.. (2020). Distributed Cooperative Deep Transfer Learning for Industrial Image Recognition. Procedia CIRP. 93. 437–442. 17 indexed citations
15.
Maschler, Benjamin, et al.. (2020). Anwendungsfälle und Methoden der künstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0. OPUS Publication Server of the University of Stuttgart (University of Stuttgart). 1 indexed citations
16.
Maschler, Benjamin, Nasser Jazdi, & Michael Weyrich. (2019). Maschinelles Lernen für intelligente Automatisierungssysteme mit dezentraler Datenhaltung am Anwendungsfall Predictive Maintenance. OPUS Publication Server of the University of Stuttgart (University of Stuttgart). 3 indexed citations
17.
Weyrich, Michael, et al.. (2018). Testen vernetzter Systeme für die Industrie 4.0. atp magazin. 60(3). 26–33. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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