Industrial trucks play a crucial role in modern logistics, but their effective operation often depends on highly skilled human operators. Although there are already approaches for automated control, these do not have the flexibility, speed of action, comprehension, and experience of human drivers to cope with complex situations. This paper deals with pallet object recognition, which is fundamentally important for the implementation of a pose estimation algorithm. Based on this, this paper lays the foundation for a complete automation solution for industrial trucks.
Artificial intelligence, intralogistics, driverless transport vehicles
Can denture models be additively manufactured from recycled plastic? Is the quality still sufficient for medical purposes even after several recycling cycles? The IPH is investigating this together with the LMU Clinical Centre in the ‘RecycAligner’ research project.
3D printing, additive manufacturing, recycling, dental technology
The utilization of simulations for the development of path planning algorithms for autonomous indoor multicopters is of primary importance. It offers a secure and cost-effective setting for the testing and optimization of algorithms. This article considers and examines the currently used simulation options with regard to their suitability for the development of path planning algorithms for autonomous indoor multicopters. The use of autonomous multicopters represents an innovative solution to simplify the process of layout recording and inventories. This article focuses on the voxel-based simulation VSim, developed and named by the author. In light of the extant literature, the article elucidates the simulation environments that are most commonly utilized. Subsequently, a selection of simulations is compared with VSim. The time efficiency and resource usage of the simulation environments are examined based on more than 1,500 test runs. Furthermore, the observations of the test executions are described in detail, and finally, the simulations with all investigated parameters are compared. Additionally, the potential for parallelization is explored and discussed.
Layout, Path planning, Recording, Aircraft, Optimization, Testing
Due to the significantly increasing demand for plastic components, it has become necessary to investigate polymer recycling solutions to eliminate their adverse environmental impact. The focus of this study is to examine the feasibility of recycling polypropylene and a thermoplastic elastomer up to five times using additive manufacturing. This study also focuses on the production and evaluation of the quality of hybrid components based on polypropylene and thermoplastic elastomers. A thermomechanical recycling approach is used, which involves subjecting polymers to thermal and mechanical processes to obtain a usable material form after each recycling cycle. Additive manufacturing was used to produce specimens using the material in both filament and granular form. The thermal, mechanical, and rheological properties of the specimens were characterized by means of various analytical techniques, including tensile test, impact test, optical microscopy, Fourier-transform infrared spectroscopy, thermogravimetric analysis, dynamic scanning calorimetry, and rheological tests in order to study the degradation characteristics of the recycled polymers. The results generally indicate that the chosen recycling procedure causes only slight alterations in the material properties by means of thermal and rheological tests, while impacting mechanical properties and printability.
Additive manufacturing, recycling, polypropylene, thermoplastic elastomers
In many companies, processes and IT are closely interlinked via systems such as ERP and MES. As a result, general changes also have a direct impact on IT systems. Over time, new or changed requirements may no longer be adequately covered by the functionality provided by the system. Continuous requirements and risk management can prevent this problem by creating transparency with regard to the relevant change drivers. This article explains potential change drivers.
IT systems, ERP system, MES, manufacturing, change drivers
The economy is facing challenges that require sustainable economic activity. Automation offers great potential in this respect, as it can promote energy efficiency, resource conservation and social improvements. Nevertheless, existing sustainability assessment methods often inadequately represent specific requirements for automation solutions. IPH is therefore developing a method to support SMEs in effectively implementing sustainable automation solutions.
sustainability, sustainability assessment, automation solutions, social, economic, ecological
A constantly increasing number of product variants, shorter product life cycles and ever faster changing factories are increasing the demands on intralogistics. This is accompanied by the need for ever more efficient transport logistics with high flexibility and scalability of the transport systems used. In practice, communication problems can already occur today, which have a negative impact on the expected logistics performance. If the number of participating AGVs is increased in the future, there will also be major scalability problems. New concepts rely on swarms of automated guided vehicles (AGVs) to improve the communication of AGVs on the one hand and to increase logistics performance on the other.
Radio communication, decentralized control, automated guided vehicles, network coding
The relevance and added value of artificial intelligence (AI) and machine learning (ML) have increased significantly in recent years. Extensive potential has emerged, particularly in the area of production. However, the high complexity of ML and the lack of evidence of its added value often mean that particularly small and medium-sized enterprises (SMEs) do not engage further with its introduction and use. For this reason, a holistic guide has been developed that accompanies manufacturing SMEs from the identification of suitable use cases and maturity level analysis through to the implementation of measures and continuous improvement processes, providing the required concepts.
Machine learning, implementation strategy, guide, production, maturity level
Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.
Cross-wedge rolling, Hybrid components, Process monitoring, AI-based image recognition
This article examines how force sensors must be positioned to detect incorrect positioning of forging blanks. For this purpose, simulative investigations are carried out on a die. Positions for possible sensor placement are applied in a grid pattern. The recorded force values of the respective sensors are analyzed to identify those sensors that are particularly suitable for reliably detecting incorrect positioning.
industry 4.0, digitalisation, process monitoring
Artificial intelligence (AI) can be used to reliably recognise errors, reduce defective parts and increase component quality. In the 'AutoPress' project, researchers have developed a system of sensors and AI that recognises 95 to 98 per cent of all process deviations.
Artificial intelligence, AI, process monitoring, error detection
The combination of several materials in one component can contribute to increased performance. Herein, three types of hybrid components are manufactured using two cladding processes and one joining process. The resulting workpieces are then formed and tested to determine the potential of the different material combinations. Two types of workpieces are produced to investigate multilayer claddings made of different materials, which serve to positively adjust the residual stress. The workpieces are tested using microstructural images and hardness measurements to characterize the microstructure and properties of the intermediate layers. In addition, residual stress measurements are carried out to determine the residual stress ratios. Compressive residual stresses are present in the subsurface of the welded and subsequently formed layer, which will improve the service life in case of rolling load conditions. The third type of workpiece is a combination of aluminum alloy and steel with a cladding layer that combines the performance of the cladding material in the bearing seat with the weight reduction of the aluminum alloy. Scanning Electron Microscopy (SEM) measurements are used to determine whether the application of the cladding has an influence on the intermetallic phase seam in the joining zone of aluminum alloy and steel.
Hybrid components, tailored forming, joining process, material combination, aluminium, steel
Aim: We explore the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure by demonstrating how engineers can utilize the ORKG in innovative ways for communication and (re-)use.
Method: For a use case from the Collaborative Research Center 1153 “Tailored Forming”, we collect, extract, and analyze scientific knowledge on 10 Tailored Forming Process Chains (TFPCs) from five publications in the ORKG. In particular, we semantically describe the TFPCs, i.a., regarding their steps, manufacturing methods, measurements, and results. The usefulness of the data extraction topics, their organization, and the relevance of the knowledge described is examined by an expert consultation with 21 experts.
Results: Based on the described knowledge, we build and publish an ORKG comparison as a detailed overview for communication. Furthermore, we (re-)use the knowledge and answer eight competency questions asked by two domain experts. The validation shows a clear agreement of the 21 experts regarding the examined usefulness and relevance.
Conclusions: Our use case shows that the ORKG as a ready-to-use infrastructure with services supports researchers, including engineers, in sustainably organizing FAIR scientific knowledge. The direct use of the ORKG by engineers is feasible, so the ORKG is a promising infrastructure for innovative ways of communicating and (re-)using FAIR scientific knowledge in engineering sciences, thus advancing this research field.
Tailored forming, scientific knowledge, communication, knowledge graph
The IPH – Institut für Integrierte Produktion Hannover gGmbH is developing a method for contactless detection of rotational angle and torque on unmodified steel shafts in the "Modimo" research project. This approach utilizes natural surface features of the shaft, employing optical methods to determine the rotational angle. The phase shift of the rotational angles allows for the calculation of the applied torque. This technique aims to enable precise monitoring and control of drives and generators without the need for elaborate mechanical modifications to the shaft.
torque, sensors, optical metrology
As the number of product variants continues to grow, the need for flexibility in intralogistics is becoming increasingly apparent. One potential solution to this challenge is the use of cellular automated guided vehicles, which can be variably interconnected depending on the size of the product to be transported. This article presents an optimization model for solving a vehicle routing problem for cellular automated guided vehicles. Furthermore, a recursive method is presented that determines an optimal transport sequence based on the solution of the model. The optimization model is implemented in a specially developed model environment and solved for a dynamic, illustrative use case. Subsequently, logistical target variables are evaluated in order to assess the solution of the optimization model. The exemplary application of the optimization model demonstrates the feasibility of modeling cellular transportation with automated guided vehicles and evaluating its performance based on logistical target variables.
AGV, cellular transport units, optimization model, simulation, logistical target values
This work explores the challenges of fully automating in-house goods transport in environments where industrial trucks like forklift trucks remain necessary due to undefined load carrier positions and shapes. Imitation Learning (IL) is identified as a promising solution for vehicle control in repetitive tasks, yet its application in intralogistics is challenging by the dynamic complexity of industrial trucks and the large dimensional space involved. A Robot Operating System 2 (ROS2) framework is introduced, enabling the acquisition of driving data from both simulation environments and real-world demonstrators. The study also presents a network architecture combining a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network, facilitating end-to-end learning from spatial and temporal image data. The framework's effectiveness is evaluated using a dataset of expert driving maneuvers to assess the generalization potential of the IL-trained network in vehicle control in different scenarios. The research aims to demonstrate the utility of the proposed framework for data acquisition and validate IL as a control approach for industrial trucks that require generalization.
Imitation Learning, industrial truck automation, intralogistics, ROS2, load handling
Constantly increasing production volumes and new challenges in production environmentswith the same amount of space are forcing manufacturing companies to deal with the planning of production layouts. The problem often is a non-existent or outdated production layout plan. Autonomous multicopters can help by simplifying layout capture. That's why a voxel-based simulation is investigated to develop and train path planning algorithms with and without artificial intelligence. First, the temporal behavior and the resource utilization of the simulation is investigated. Then, the time factor of simulation is compared to real time and what advantages companies and developers have when using it.
UAS, digital twin, simulation, voxel-based, layout design
A major advantage of flashless precision forging is the saving of resources by avoiding excess material. Especially materials with good flow properties, such as aluminum, sometimes lead to the formation of thin flash. Seals have been developed and experimentally tested to prevent thin flash, i.e. the flow of material into tool gaps. The study presented shows the effectiveness of different sealing materials and the ideal sealing geometry.
thin flash, Sealing concept, precision forging, forming technology, process optimization
It was investigated how a grain refinement can be introduced into a cylindrical rolled part with cross rolling in a flatjaw design. For this purpose, a non-circular shape was rolled in and out again. A theoretical preliminary investigation was used to define a suitable parameter field for experimental evaluation. Metallographic investigations showed that cross rolling had a positive effect on the microstructure.
process design, forming technology
Around half of the currently 30,000 active wind turbines in Germany will reach the end of their service life by 2030, which is generally defined by the manufacturer as 20 years of operation. The most common strategy for the subsequent use of a wind farm is repowering, provided this is (legally) possible at the respective location. One option for dealing with old turbines is to resell them. At the time of repowering, in Germany after an average of around 17 years, the wind turbines usually still have a remaining operating time of several years before critical parts such as generators fail.
This article presents a machine learning model for predicting the resale value of used wind turbines. This model can be used to approximately predict the resale value of comparable wind turbines based on certain input parameters such as the power output or the age of the wind turbines. The model was trained using an adjusted data set from an online trading platform for wind turbines. The necessary pre-processing steps such as the removal of extreme outlier values and the addition or replacement of missing or incorrect wind turbinespecific data from a second data source using a self-developed matching algorithm are presented. Finally, the prediction accuracy of different ML algorithms is tested using test data to find the best method for predicting the resale value of wind turbines.
Renewable energies, wind turbines, resale values, machine learning, data science
