Karlsruhe Institute of Technology

Assembly Line

LLM-based Control Code Generation using Image Recognition

πŸ“… Date:

✍️ Authors: Heiko Koziolek, Anne Koziolek

πŸ”– Topics: Generative AI, Large Language Model, ChatGPT, Programmable Logic Controller

🏒 Organizations: ABB, Karlsruhe Institute of Technology, Eastman Chemical

LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes. Previous attempts in control logic code generation lacked methods to interpret schematic drawings from process engineers. Recent LLMs now combine image recognition, trained domain knowledge, and coding skills. We propose a novel LLM-based code generation method that generates IEC 61131-3 Structure Text control logic source code from Piping-and-Instrumentation Diagrams (P&IDs) using image recognition. We have evaluated the method in three case study with industrial P&IDs and provide first evidence on the feasibility of such a code generation besides experiences on image recognition glitches.

Read more at arXiv

🧠 Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

πŸ“… Date:

✍️ Authors: Mustafa Demetgul, Qi Zheng, Ibrahim Nur Tansel, Jürgen Fleischer

πŸ”– Topics: Convolutional Neural Network, LSTM, Autoencoder, Machine Tool

🏒 Organizations: Karlsruhe Institute of Technology

CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs.

Read more at The International Journal of Advanced Manufacturing Technology

Deep Learning Boosts Robotic Picking Flexibility

πŸ“… Date:

πŸ”– Topics: robotics, federated learning

🏒 Organizations: Festo, Karlsruhe Institute of Technology, DarwinAI

Gripping and manipulating items of diverse shapes and sizes has long been one of the biggest challenges facing industrial robotics. The difficulty is perhaps best summed up by the Polanyi Paradox, which states that we β€œknow more than we can tell.” In essence, while it may be easy to teach machines to exhibit a high level of performance on tasks that require abstract reasoning such as running computations, it is substantially harder to grant them the sensory-motor skills of even a small child in all but the most standardized and predictable environments.

Read more at Automation World