Increasing quality requirements combined with high cost pressure make efficient quality management indispensable, especially for manufacturing companies. As part of quality management, complaints management ensures that complaints are received and processed and that the causes of defects are identified and eliminated. The 8D method is a tool for the profitable documentation and processing of complaints. Parallel to the execution of the 8D method, all steps performed are recorded in an 8D report. There are no objective internal inspection or control bodies that ensure the integrity and correctness of an 8D report.
Therefore, this thesis aims to support the verification of 8D-reports by an automated evaluation system. Thereby, 8D-reports should be evaluated both formally and in terms of content.
8D-Report, computational linguistics, quality assessment, complaints
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
Automated industrial trucks master difficult driving situations worse than humans – for now. New approaches based on artificial intelligence (AI) are intended to replicate human driving behavior and give automated systems more flexibility.
artificial intelligence, intralogistics, industrial trucks
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
Globalization enables even small and medium-sized companies to sell their products worldwide. This is also accompanied by an increase in the number of direct competitors. As a result of the steadily increasing competition smaller companies in particular are expanding their direct sales and e-commerce activities. This requires resources for packaging, warehousing and order picking. The high competitive pressure to which The high competitive pressure to which companies are exposed can mean that attention to the requirements of the human resource is pushed into the background of entrepreneurial activity. If this resource is not used sustainably, these companies are often at a competitive disadvantage in the short and long term in that they have to find replacements for employees who are absent at short notice. for employees who are absent at short notice and loses important empirical knowledge through affected employees. This represents a competitive disadvantage for small and medium-sized enterprises in particular. The economic damage must also be damage must also be considered: Expenses for recovery and retraining must be incurred for employees who are ill. Furthermore, jobs are more difficult to fill due to an increasing awareness of health issues if the health of each employee is not taken into account. The research project entitled "Automated camera-based ergonomic evaluation of workplaces" (AkEvAp for short) addresses precisely this point in order to use people as a resource for picking in a sustainable manner.
picking, AkEvAp, ergonomics
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
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
Factory planning can increase the productivity of manufacturing significantly, though the process is expensive when it comes to cost and time. In this paper, we propose an Unmanned Aerial Vehicle (UAV) framework that accelerates this process and decreases the costs. The framework consists of a UAV that is equipped with an IMU, a camera and a LiDAR sensor in order to navigate and explore unknown indoor environments. Thus, it is independent of GNSS and solely uses on-board sensors. The acquired data should enable a DRL agent to perform autonomous decision making, applying a reinforcement learning approach. We propose a simulation of this framework including several training and testing environments, that should be used for developing a DRL agent.
drone, UAS, deep reinforcement learning
Additive manufacturing allows components to be manufactured flexibly. This manufacturing process is particularly suitable for products with a unique character. In the production of large components, which have previously been manufactured by casting, this offers the advantages of greater flexibility in design and the elimination of the need to build molds that are only used once for unique items. To manufacture large components additively, a consortium of five companies is developing a new 3D printer for XXL products. For quality assurance, IPH - Institut für Integrierte Produktion Hannover has implemented two monitoring systems. These capture the geometry using three laser line scanners and regulate the manufacturing process during printing using two different software systems.
XXL products, large components, additive manufacturing, 3D printing, quality control
Laser transmission welding (LTW) is a known technique to join conventionally produced high volume thermoplastic parts, e.g. injected molded parts for the automotive sector. For using LTW for additively manufactured parts (usually prototypes, small series, or one-off products), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. In comparison to the injection molding process, the additive manufacturing process leads to an inhomogeneous structure with trapped air inside the volume. Therefore, a change in the transmissivity results due to the additive manufacturing process.
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. The designed expert system supports the user setting up the additive manufacturing process. With the results of a preliminary work, a neural network is trained to predict the transmissivity values of the transparent samples. To validate the expert system, specimen of transparent polylactide are additively manufactured with various manufacturing parameters in order to change the transmissivity. The transmissivity of the parts are measured with a spectroscope. The parameters of the additive manufacturing process are used to predict the transmissivity with the neural network and are compared to the measurements. The transparent samples are welded to black polylactide samples with different laser power in overlap configuration and shear tensile tests are performed. With these experiments, the prediction of additive manufacturing parameters with the expert system in order to use the parts for a LTW process is demonstrated.
Additive manufacturing, laser transmission welding, neural networks, expert system
Product complexity and variant diversity increase the effort for the development of production processes at SMEs. As part of the IGF research project "Self-learning multi-stage quality monitoring processes for (laser) material processing" (AiF No.: 20419N), an expert system was therefore developed for manufacturing companies in the field of laser material processing. The expert system supports users in process control and quality prediction of new products and product variants .
laser material processing, expert system, machine learning
This article presents a method for superimposing vision constraints based on the principle of augmented reality. The method is based on an overlay of the actual operator's field of view with information from a reconstructed scene. The reconstructed scene is superimposed as a hologram only over the vision-restricting components. The presented method is divided into position determination, data transmission and visualization. These software components are presented in detail. In view of the later use of the system in an industrial truck, the real-time capability of the data transmission, the accuracy of the visualization and the robustness of the position determination are also investigated.
augmented reality, driver assistance system, forklift trucks, image processing, obstacle detection
Additive manufacturing has established itself in medical technology, where complex and patient-specific products are manufactured. Since additive manufacturing processes are sensitive to changes in process parameters and environmental conditions, quality assurance is a key factor for production. This paper presents the approach for in-situ process monitoring in additive material extrusion.
Additive Manufacturing, 3D printing, Fused Deposition Modeling, quality control, machine learning
What factors influence the running behavior of idlers? How do they behave in heat and cold; how resistant are they to water and dust? All this can be investigated with the new test rigs at IPH. Even motor-driven idlers are tested there.
idlers, rollers, test rig, belt conveyor system, bulk material handling, energy efficiency
In order to enable even complex processes such as the joining of additively manufactured components by laser in production in a quality-assured way, the existence of specialist knowledge in companies is absolutely essential. To bundle this knowledge for process control and monitoring independently of personnel, an expert system is being developed in the IGF research project of FQS - Forschungsgemeinschaft Qualität e.V. entitled "Quality assurance in laser beam welding of additively manufactured thermoplastic components (QualLa)". By integrating specialist knowledge into the expert system, this knowledge can be secured in companies in the long term and processes can continuously be carried out with high qualitative standards.
additive manufacturing, 3D printing, FDM, laser transmission welding, laser beam welding
In Germany, demand for commercial drones is forecast to increase by 200% by 2025. As the use of drones increases, so does the danger they pose. This article describes a research project that aims to develop an acoustic operational monitoring system to improve the safety of critical components.
UAS, drones, operational monitoring
Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.
Material extrusion, Fused deposition modeling, Process monitoring, Sensor technology, Research gaps
