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
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
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
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 continuously growing number of product variants increases the demands on the flexibility of intralogistics transportation. One way to achieve greater flexibility 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 explains the characteristics of cellular automated guided vehicles and the relationships between influencing variables of the cellular transport system and economic and logistical target variables.
Intralogistics, automated guided vehicles, cellular transport units
Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.
additive manufacturing, material extrusion, quality classes, image processing, Process monitoring
Hybrid components, made of multiple materials, can meet the increasing demands for lightweight construction and functional integration in the automotive and aircraft industry. Hybrid semi-finished components are produced by applying a high-alloy cladding to a low-alloy base material before hot-forming and machining the workpiece. Throughout this process chain, workpiece deviations in the form of material distribution and material properties can occur that influence the component’s lifetime. This paper investigates whether such workpiece deviations can be detected within the process chain by analyzing process signals obtained from subsequent process steps. For this purpose, artificial workpiece deviations were introduced to hybrid semi-finished workpieces made of C22.8/X45CrSi9-3. Then, process signals during forming and machining were analyzed to determine their sensitivity to the artificial deviations. The results revealed that deviations in cladding size can be effectively monitored using signals from both forming and machining. Cladding position deviations can only be detected during machining, while forming signals are more responsive to detecting the introduced hardness deviations of approx. 100 HV0.1.
Laser hot-wire cladding, Cross-wedge rolling, Machining, Monitoring, Workpiece deviations
The Collaborative Research Center 1153 is investigating an innovative process chain for the production of hybrid components. The hybrid workpieces are first joined and then formed by cross-wedge rolling. Pinion shafts were manufactured to investigate the behavior of the joining zone under increased complexity of the forming process. For this purpose, six types of workpieces produced by three types of joining processes were formed into pinion shafts. The reference process provides a shaft with a smooth bearing seat. It was found that the increased complexity did not present any challenges compared to the reference processes. A near-net shape geometry was achieved for the pinions made of steel.
hybrid components, cross-wedge rolling, hot forming, laser beam welding, LHWD welding
Limited visibility during the operation of forklifts is one of the most significant sources of danger in in-plant material handling. Existing systems record concealed areas via cameras and display them directly on monitors in the operator's cab. The operator has to temporarily turn his attention to a screen and is unable to perceive the real information necessary for the driving task. We developed the first augmented reality based driver assistance system for safety improvement in intralogistics. The results show the capability to eliminate view restrictions directly in the operator's field of view and create the illusion of transparent vehicle components.
augmented reality, assistance system, intralogistics
Although factory planning is widely recognized as a way to significantly enhance manufacturing productivity, the associated costs in terms of time and money can be prohibitive. In this paper, we present a solution to this challenge through the development of a Software-in-the-loop (SITL) framework that leverages an Unmanned Aircraft System (UAS) in an autonomous capacity. The framework incorporates simulated sensors, a UAS, and a virtual factory environment. Moreover, we propose a Deep Reinforcement Learning (DRL) agent that is capable of collision avoidance and exploration using the Dueling Double Deep Q-Network (3DQN) with prioritized experience replay.
Artificial Intelligence, reinforcement learning, Unmanned Aircraft Systems
A new process chain for the manufacturing of load-adapted hybrid components is presented. The "Tailored Forming” process chain consists of a deposition welding process, hot forming, machining and an optional heat treatment. This paper focuses on the combination of laser hot-wire cladding with subsequent hot forming to produce hybrid components. The applicability is investigated for different material combinations and component geometries, e.g. a shaft with a bearing seat or a bevel gear. Austenitic stainless steel AISI 316L and martensitic valve steel AISI HNV3 are used as cladding materials, mild steel AISI 1022M and case hardening steel AISI 5120 are used as base materials. The resulting component properties after laser hot-wire cladding and hot forming such as hardness, microstructure and residual stress state are presented. In the cladding and the heat-affected zone, the hot forming process causes a transformation from a welding microstructure to a fine-grained forming microstructure. Hot forming significantly affects the residual stress state in the cladding the resulting residual stress state depends on the material combination.
laser hot-wire cladding, cladding, hot forming, residual stress, tailored forming
Geometry, design, and processing in addition to the thermoelectric material properties have a significant influence on the economic efficiency and performance of thermoelectric generators (TEGs). While conventional BULK TEGs are elaborate to manufacture and allow only limited variations in geometry, printed TEGs are often restricted in their application and processing temperature due to the use of organic materials. In this work, a proof-of-concept for fabricating modular, customizable, and temperature-stable TEGs is demonstrated by applying an alternative laser process. For this purpose, low temperature cofired ceramics substrates were coated over a large area, freely structured and cut without masks by a laser and sintered to a solid structure in a single optimized thermal post-processing. A scalable design with complex geometry and large cooling surface for application on a hot shaft was realized to prove feasibility.
thermoelectric, printed electronic, laser structuring, printed ceramics, spray coating
The digital development of spaces within the city of Hannover by means of a digital image makes it possible to cover the usage needs of spaces more efficiently and in line with the requirements. The crea-tion of a digital image, which develops new possibilities for access to public space, requires the use of different sensors such as LiDAR sensors and tracking cameras. In order to select suitable sensors that can be used with UAS, the requirements for the overall system are first defined, which are derived in functional requirements for the sensor technology. Subsequently, the degree of fulfilment of the functional requirements by the different sensors
5G, UAS, digital image, digital twin
In order to use laser transmission welding (LTW) for additively manufactured parts such as prototypes, small series, or one-off products, an enhanced process knowledge is needed to overcome the difficulties in the part composition resulting from the additive manufacturing process itself. In comparison to an injection molding process for thermoplastic parts, the additive manufacturing process fused deposition modeling leads to an inhomogeneous structure with trapped air inside the volume.
In this paper, a neural network-based expert system is presented that provides the user with process knowledge in order to improve the weld seam quality of laser welded additively manufactured parts. Both additive manufacturing and LTW process are assisted by the expert system. First, the designed expert system supports the user in setting up the additive manufacturing process to increase the transmissivity. During welding, the additive manufacturing and LTW process parameters are used to predict the weld seam strength. To create the database for the expert system, specimens of transparent and black polylactide are additively manufactured. In order to change the transmissivity at an emission wavelength of 940?nm of the diode laser used, the manufacturing parameters for the transparent parts are varied. The transmissivity of the parts is measured with a spectroscope. The transparent samples are welded to the black samples with laser powers between 8 and 14?W in the overlap configuration and shear tensile tests are performed. In this work, the predictions of the transmissivity and the shear tensile force are demonstrated with an accuracy of more than 88.1% of the neural networks used for the expert system.
Additive manufacturing, laser transmission welding, neural networks, expert system
The temporally and spatially accurate display of information in augmented reality (AR) systems is essential for immersion and operational reliability when using the technology. We developed an assistant system using a head-mounted display (HMD) to hide visual restrictions on forklifts. We propose a method to evaluate the accuracy and latency of AR systems using HMD. For measuring accuracy, we compare the deviation between real and virtual markers. For latency measurement, we count the frame difference between real and virtual events. We present the influence of different system parameters and dynamics on latency and overlay accuracy.
augmented reality, image processing, driver assistance system, forklift trucks
Additive manufacturing enables the economical production of complex components with a high degree of customization. Therefore, the medical industry is using the advantages of additive manufacturing to produce individualized medical devices. Medical devices are subject to special quality control requirements that additive manufacturing processes do not meet yet. This article deals with the introduction of an in situ process monitoring concept using the example of fused deposition modeling. The process monitoring is carried out by a quality model, which accesses the data of a self-developed sensor concept integrated in the printer. This data is analyzed using a machine learning pipeline to predict process and product quality. Thereby, the machine learning pipeline consist of several sequential steps, ranging from data extraction and preprocessing to model training and deployment. The procedure presented for ensuring print quality forms a basis for the production of safety-relevant components in batch size one and extends conventional quality assurance methods in additive manufacturing.
additive manufacturing, quality monitoring, fused deposition modeling, artificial intelligence
Laser transmission welding (LTW) is a known technique to join conventionally produced thermoplastic parts, e.g. injected molded parts. When using LTW for additively manufactured parts (usually prototypes, small series), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself.
In this paper, a method is presented to enhance the weld seam quality of laser welded additively manufactured parts assisted by a neural network-based expert system. To validate the expert system, specimens are additively manufactured from polylactide. The parameters of the additive manufacturing process, the transmissivity, and the LTW process parameters are used to predict the shear tensile force with the neural network. The transparent samples are welded to black absorbent samples in overlap configuration and shear tensile tests are performed. In this work, the prediction of the shear tensile force with an accuracy of 88.1 % of the neuronal network based expert system is demonstrated.
Additive manufacturing, laser transmission welding, neural networks, expert system
