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Peter Burggräf

Univ.-Prof. Dr.-Ing. Peter Burggräf

Büroadresse

BU-A 101
Ebene 1
Siegener Straße 152
57223
Kreuztal

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Till Saßmannshausen, Peter Burggräf, Marc Hassenzahl, Carl René Sauer

Effects of AI explanations on trust and reliance: a study in job shop scheduling

From human to machine: high-impact tasks for AI in production management - an expert study to reshape decision-making

P. Burggräf, F. Steinberg, C.R. Sauer, P. Nettesheim, N. Müller, L. Baeck

Improve Quality Control with Predictive Analytics: Using Machine Learning and IO-Link Sensors for Early Detection of Defects in Modern Production

Philipp Nettesheim, Peter Burggräf, Fabian Steinberg

A design concept for data-driven brewing: sensor-based system architecture and ML applications for sustainability in micro-breweries

From theory to application: investigating the generalizability of facility layout problems using a deep reinforcement learning approach

Carl René Sauer, Peter Burggräf, Fabian Steinberg

Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly

Carl René Sauer, Peter Burggräf, Fabian Steinberg

A systematic review of machine learning for hybrid intelligence in production management

Norman Müller, Peter Burggräf, Fabian Steinberg, Carl René Sauer, Maximilian Schütz

An analytical review of predictive methods for delivery delays in supply chains

Integrating artificial intelligence into energy management: A case study on energy consumption data analysis and forecasting in a German manufacturing company

Patricia M. Dold, Praveen Nadkarni, Meiko Boley, Valentin Schorb, Lili Wu, Fabian Steinberg, et al.

Event-based vision in laser welding: An approach for process monitoring

Carl René Sauer, Peter Burggräf

Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Ognjen Radisic-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weisser

Predicting schedule adherence of engineering changes – a case study on effectivity date adherence prediction using machine learning