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
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
In this paper, objective functions for the optimisation of modular conveyor systems will be introduced. Modular conveyor systems consist of conventional as well as modular conveyor hardware, which are arranged in form of matrix-like layouts. The aim of an ongoing research project is to provide small and medium-sized enterprises with a user-friendly decision support for the selection and planning of modular conveyor systems. For this purpose, the conveyor systems should be evaluated according to the objectives throughput and space requirement. Therefore, mathematical equations have been developed, which enable a fast and precise evaluation of layouts. The paper focuses mainly on the efficient calculation of the throughput. The result quality of the evaluation equations regarding the throughput was proven by a simulation of example systems.
modular conveyor, conveyor system evaluation, throughput analysis, layout optimisation, logistics
Tailored forming is used to produce hybrid components in which the materials used are locally adapted to the diferent types of physical, chemical and tribological requirements. In this paper, a Tailored Forming process chain for the production of a hybrid shaft with a bearing seat is investigated. The process chain consists of the manufacturing steps laser hot-wire cladding, cross-wedge rolling, turning and deep rolling. A cylindrical bar made of mild steel C22.8 is used as the base material, and a cladding of the martensitic valve steel X45CrSi9-3 is applied in the area of the bearing seat to achieve the strength and hardness required. It is investigated how the surface and subsurface properties of the hybrid component, such as hardness, microstructure and residual stress state, change within the process chain. The results are compared with a previous study in which the austenitic stainless steel X2CrNiMo19-12 was investigated as a cladding material. It is shown that the residual stress state after hot forming depends on the thermal expansion coefcients of the cladding material.
Tailored forming, Residual stress, Laser hot-wire cladding, Deep rolling, Hybrid Components
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
The Tailored Forming process chain is used to manufacture hybrid components and consists of a joining process or Additive
Manufacturing for various materials (e.g. deposition welding), subsequent hot forming, machining and heat treatment. In
this way, components can be produced with materials adapted to the load case. For this paper, hybrid shafts are produced by
deposition welding of a cladding made of X45CrSi9-3 onto a workpiece made from 20MnCr5. The hybrid shafts are then
formed by means of cross-wedge rolling. It is investigated, how the thickness of the cladding and the type of cooling after
hot forming (in air or in water) afect the properties of the cladding. The hybrid shafts are formed without layer separation.
However, slight core loosening occurres in the area of the bearing seat due to the Mannesmann efect. The microhardness
of the cladding is only slightly efected by the cooling strategy, while the microhardness of the base material is signifcantly
higher in water cooled shafts. The microstructure of the cladding after both cooling strategies consists mainly of martensite.
In the base material, air cooling results in a mainly ferritic microstructure with grains of ferrite-pearlite. Quenching in water
results in a microstructure containing mainly martensite.
laser hot-wire cladding, cross-wedge rolling, hybrid components, cladding
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
The manufacturing technology of thermoelectric materials is laborious and expensive often including complex and time-intensive preparation steps. In this work, a laser sintering process of the oxide-based thermoelectric material Ca3Co4O9 is investigated. Samples based on spray-coated Ca3Co4O9 were prepared and subsequently sintered under various laser parameters and investigated in terms of the microstructure and thermoelectric properties. Here, the combination of laser sintering and subsequent thermal sintering proved to be a promising concept for the preparation of thermoelectric films. Laser sintering can thus make a great contribution in improving the processing of thermoelectric materials, especially when films are applied that cannot be sintered under pressure.
thermoelectric, laser sintering
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
