Karlsruhe Institute of Technology
Canvas Category Consultancy : Research : Academic
Being “The Research University in the Helmholtz Association”, KIT creates and imparts knowledge for the society and the environment. It is the objective to make significant contributions to the global challenges in the fields of energy, mobility, and information. For this, KIT employees cooperate in a broad range of disciplines in natural sciences, engineering sciences, economics, and the humanities and social sciences. KIT prepares its students for responsible tasks in society, industry, and science by offering research-based study programs. Innovation efforts at KIT build a bridge between important scientific findings and their application for the benefit of society, economic prosperity, and the preservation of our natural basis of life. KIT is one of the German universities of excellence.
Assembly Line
Ubitium Debuts First Universal RISC-V Processor to Enable AI at No Additional Cost, as It Raises $3.7M
For over half a century, general-purpose processors have been built on the Tomasulo algorithm, developed by IBM engineer Robert Tomasulo in 1967. It’s a $500B industry built on specialised CPU, GPU and other chips for different computing tasks. Hardware startup Ubitium has shattered this paradigm with a breakthrough universal processor that handles all computing workloads on a single, efficient chip - unlocking simpler, smarter, and more cost-effective devices across industries - while revolutionizing a 57-year-old industry standard.
Alongside this, Ubitium is announcing a $3.7 million in seed funding round, co-led by Runa Capital, Inflection, and KBC Focus Fund. The investment will be used to develop the first prototypes and prepare initial development kits for customers, with the first chips planned for 2026.
Pilot Plant Produces Climate-friendly Cement Clinker
Concrete production is one of the main sources of industrial greenhouse gas emissions. To limit such emissions, the Karlsruhe Institute of Technology (KIT) is developing net-zero circular concrete. With the launch of a pilot plant for belite cement clinker, an important step in its production has now been tested in practice.
To produce belite cement clinker, the pilot plant uses an all-electric heating system powered by renewable energy and a carbon dioxide atmosphere, reducing the energy required for the process. “We can manage with a process temperature of 1000 instead of 1400 degrees Celsius in the rotary kiln,” Stemmermann said. Compared with conventional clinker production, the overall energy consumption is 40 percent lower. “The CO2 unavoidably emitted as a result of the limestone reaction in the kiln is captured and then bound to the recycled concrete in the second step of the process,” Stemmermann said. This second step is to be integrated in a future expansion of the pilot plant, which is currently capable of producing 100 kilograms of clinker per day.
LLM-based Control Code Generation using Image Recognition
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.
🧠 Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data
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.
Deep Learning Boosts Robotic Picking Flexibility
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 “know 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.