Fachbereich Maschinenbau und Mechatronik
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The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.
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.
Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.
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.
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.
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 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.