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The use of industrial robots allows the precise manipulation of all components necessary for setting up a large-scale particle image velocimetry (PIV) system. The known internal calibration matrix of the cameras in combination with the actual pose of the industrial robots and the calculated transform from the fiducial markers to camera coordinates allow the precise positioning of the individual PIV components according to the measurement demands. In addition, the complete calibration procedure for generating the external camera matrix and the mapping functions for e.g. dewarping the stereo images can be automatically determined without further user interaction and thus the degree of automation can be extended to nearly 100%. This increased degree of automation expands the applications range of PIV systems, in particular for measurement tasks with severe time constraints.
Additive Manufacturing (AM) is a topic that is becoming more relevant to many companies globally. With AM's progressive development and use for series production, integrating the technology into existing production structures is becoming an important criterion for businesses. This study qualitatively examines the actual state and different perspectives on the integration of AM in production structures. Seven semi-structured interviews were conducted and analyzed. The interview partners were high-level experts in Additive Manufacturing and production systems from industry and science. Four main themes were identified. Key findings are the far-reaching interrelationships and implications of AM within production structures. Specific AM-related aspects were identified. Those can be used to increase the knowledge and practical application of the technology in the industry and as a foundation for economic considerations.
The fourth industrial revolution is on its way to reshape manufacturing and value creation in a profound way. The underlying technologies like cyber-physical systems (CPS), big data, collaborative robotics, additive manufacturing or artificial intelligence offer huge potentials for the optimization and evolution of production systems. However, many manufacturing companies struggle to implement these technologies. This can only in part be attributed to the lack of skilled personal within these companies or a missing digitalization strategy. Rather, there is a fundamental incompatibility between the way current production systems and companies (Industry 3.0) are structured across multiple dimensions compared to what is necessary for industry 4.0. This is especially true in manufacturing systems and their transition towards flexible, decentralized and autonomous value creation networks. This paper shows across various dimensions these incompatibilities within manufacturing systems, explores their reasons and discusses a different approach to create a foundation for Industry 4.0 in manufacturing companies.
Establishing high-performance polymers in additive manufacturing opens up new industrial applications. Polyetheretherketone (PEEK) was initially used in aerospace but is now widely applied in automotive, electronics, and medical industries. This study focuses on developing applications using PEEK and Fused Filament Fabrication for cost-efficient vulcanization injection mold production. A proof of concept confirms PEEK’s suitability for AM mold making, withstanding vulcanization conditions. Printing PEEK above its glass transition temperature of 145 °C is preferable due to its narrow process window. A new process strategy at room temperature is discussed, with micrographs showing improved inter-layer bonding at 410°C nozzle temperature and 0.1 mm layer thickness. Minimizing the layer thickness from 0.15 mm to 0.1 mm improves tensile strength by 16%.
In the face of the current trend towards larger and more complex production tasks in the SLM process and the current limitations in terms of maximum build space, the welding of SLM components to each other or to conventionally manufactured parts is becoming increasingly relevant. The fusion welding of SLM components made of 316L has so far been rarely investigated and if so, then for highly specialised laser welding processes. When welding with industrial gas welding processes such as MIG/MAG or TIG welding, distortions occur which are associated with the resulting residual stresses in the components. This paper investigates process-side influencing factors to avoid resulting residual stresses in SLM components made of 316L. The aim is to develop a strategy to build up SLM components as stress-free as possible in order to join them as profitably as possible with a downstream welding process. For this purpose, influencing parameters such as laser power, scan speed, but also scan vector length and different scan patterns are investigated with regard to their influence on residual stresses.
Transgenic plants have the potential to produce recombinant proteins on an agricultural scale, with yields of several tons per year. The cost-effectiveness of transgenic plants increases if simple cultivation facilities such as greenhouses can be used for production. In such a setting, we expressed a novel affinity ligand based on the fluorescent protein DsRed, which we used as a carrier for the linear epitope ELDKWA from the HIV-neutralizing antibody 2F5. The DsRed-2F5-epitope (DFE) fusion protein was produced in 12 consecutive batches of transgenic tobacco (Nicotiana tabacum) plants over the course of 2 years and was purified using a combination of blanching and immobilized metal-ion affinity chromatography (IMAC). The average purity after IMAC was 57 ± 26% (n = 24) in terms of total soluble protein, but the average yield of pure DFE (12 mg kg−1) showed substantial variation (± 97 mg kg−1, n = 24) which correlated with seasonal changes. Specifically, we found that temperature peaks (>28°C) and intense illuminance (>45 klx h−1) were associated with lower DFE yields after purification, reflecting the loss of the epitope-containing C-terminus in up to 90% of the product. Whereas the weather factors were of limited use to predict product yields of individual harvests conducted for each batch (spaced by 1 week), the average batch yields were well approximated by simple linear regression models using two independent variables for prediction (illuminance and plant age). Interestingly, accumulation levels determined by fluorescence analysis were not affected by weather conditions but positively correlated with plant age, suggesting that the product was still expressed at high levels, but the extreme conditions affected its stability, albeit still preserving the fluorophore function. The efficient production of intact recombinant proteins in plants may therefore require adequate climate control and shading in greenhouses or even cultivation in fully controlled indoor farms.
Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.
Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative.
The thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series.
In times of short product life cycles, additive manufacturing and rapid tooling are important methods to make tool development and manufacturing more efficient. High-performance polymers are the key to mold production for prototypes and small series. However, the high temperatures during vulcanization injection molding cause thermal aging and can impair service life. The extent to which the thermal stress over the entire process chain stresses the material and whether it leads to irreversible material aging is evaluated. To this end, a mold made of PEEK is fabricated using fused filament fabrication and examined for its potential application. The mold is heated to 200 ◦C, filled with rubber, and cured. A differential scanning calorimetry analysis of each process step illustrates the crystallization behavior and first indicates the material resistance. It shows distinct cold crystallization regions at a build chamber temperature of 90 ◦C. At an ambient temperature above Tg, crystallization of 30% is achieved, and cold crystallization no longer occurs. Additional tensile tests show a decrease in tensile strength after ten days of thermal aging. The steady decrease in recrystallization temperature indicates degradation of the additives. However, the tensile tests reveal steady embrittlement of the material due to increasing crosslinking.
Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems’ fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning.
Traditional vulcanization mold manufacturing is complex, costly, and under pressure due to shorter product lifecycles and diverse variations. Additive manufacturing using Fused Filament Fabrication and high-performance polymers like PEEK offer a promising future in this industry. This study assesses the compressive strength of various infill structures (honeycomb, grid, triangle, cubic, and gyroid) when considering two distinct build directions (Z, XY) to enhance PEEK’s economic and resource efficiency in rapid tooling. A comparison with PETG samples shows the behavior of the infill strategies. Additionally, a proof of concept illustrates the application of a PEEK mold in vulcanization. A peak compressive strength of 135.6 MPa was attained in specimens that were 100% solid and subjected to thermal post-treatment. This corresponds to a 20% strength improvement in the Z direction. In terms of time and mechanical properties, the anisotropic grid and isotropic cubic infill have emerged for use in rapid tooling. Furthermore, the study highlights that reducing the layer thickness from 0.15 mm to 0.1 mm can result in a 15% strength increase. The study unveils the successful utilization of a room-temperature FFF-printed PEEK mold in vulcanization injection molding. The parameters and infill strategies identified in this research enable the resource-efficient FFF printing of PEEK without compromising its strength properties. Using PEEK in rapid tooling allows a cost reduction of up to 70% in tool production.
The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive (e.g. in the case of depot operations) or highly inefficient (e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for low-speed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.
Digital twins are seen as one of the key technologies of Industry 4.0. Although many research groups focus on digital twins and create meaningful outputs, the technology has not yet reached a broad application in the industry. The main reasons for this imbalance are the complexity of the topic, the lack of specialists, and the unawareness of the twin opportunities. The project "Digital Twin Academy" aims to overcome these barriers by focusing on three actions: Building a digital twin community for discussion and exchange, offering multi-stage training for various knowledge levels, and implementing realworld use cases for deeper insights and guidance. In this work, we focus on creating a flexible learning platform that allows the user to select a training path adjusted to personal knowledge and needs. Therefore, a mix of basic and advanced modules is created and expanded by individual feedback options. The usage of personas supports the selection of the appropriate modules.
The development of protype applications with sensors and actuators in the automation industry requires tools that are independent of manufacturer, and are flexible enough to be modified or extended for any specific requirements. Currently, developing prototypes with industrial sensors and actuators is not straightforward. First of all, the exchange of information depends on the industrial protocol that these devices have. Second, a specific configuration and installation is done based on the hardware that is used, such as automation controllers or industrial gateways. This means that the development for a specific industrial protocol, highly depends on the hardware and the software that vendors provide. In this work we propose a rapid-prototyping framework based on Arduino to solve this problem. For this project we have focused to work with the IO-Link protocol. The framework consists of an Arduino shield that acts as the physical layer, and a software that implements the IO-Link Master protocol. The main advantage of such framework is that an application with industrial devices can be rapid-prototyped with ease as its vendor independent, open-source and can be ported easily to other Arduino compatible boards. In comparison, a typical approach requires proprietary hardware, is not easy to port to another system and is closed-source.
There is a growing demand for more flexibility in manufacturing to counter the volatility and unpredictability of the markets and provide more individualization for customers. However, the design and implementation of flexibility within manufacturing systems are costly and only economically viable if applicable to actual demand fluctuations. To this end, companies are considering additive manufacturing (AM) to make production more flexible. This paper develops a conceptual model for the impact quantification of AM on volume and mix flexibility within production systems in the early stages of the factory-planning process. Together with the model, an application guideline is presented to help planners with the flexibility quantification and the factory design process. Following the development of the model and guideline, a case study is presented to indicate the potential impact additive technologies can have on manufacturing flexibility Within the case study, various scenarios with different production system configurations and production programs are analyzed, and the impact of the additive technologies on volume and mix flexibility is calculated. This work will allow factory planners to determine the potential impacts of AM on manufacturing flexibility in an early planning stage and design their production systems accordingly.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.