In today's highly competitive industrial environment, continuous improvement of efficiency and optimization of processes is crucial. This paper presents an approach to the optimization of conveyor systems that uses the concept of a digital shadow. A digital shadow, as an exact digital replica of a physical conveyor system, enables detailed simulation and analysis of real operational data, providing a basis for in-depth analysis and identification of areas for improvement. The aim of this approach is not only to improve the understanding of the dynamics and performance of existing conveyor systems, but also to increase the overall efficiency through predictive simulations and optimization algorithms. In this work, we demonstrate how the integration of a digital shadow into the simulation process can contribute to a better reaction to changes in the production environment, to the reduction of downtime and to the optimization of production flows. Our methodology combines data collection / analysis, and enables the creation of accurate and flexible models of conveyor systems. These models are then used in simulations that help identify optimal settings for different production scenarios and predict potential problems before they occur. The results of applying our approach on a test laboratory line show a significant improvement in efficiency and a reduction in operating costs. This study provides important insights and practical guidelines for engineers and production managers focused on the use of digital shadow to increase the efficiency of conveyor systems. It also contributes to the development of intelligent production technologies in the era of Industry 4.0.

In recent years, 3D printing has gained increased significance in the production of material goods. Several studies have underscored the imperative for research aimed at expediting 3D printing processes. A promising field is the 3D printing of supercooled liquids, including sodium acetate trihydrate, which has the potential to innovate this field of production. The research presented in this paper investigates the extent to which printing speed can be enhanced and identifies crucial material parameters, such as electrical conductivity and maximum compressive stress for sodium acetate trihydrates, constituting the primary novelty. The paper highlights the substantial increase in printing speed achievable in the 3D printing of supercooled liquids, surpassing existing 3D printing processes by orders of magnitude.

In assembly of small-volume products, tasks are still frequently executed manually. However, the lead times foreseen for these tasks, often do not take into account the actual capabilities of the employees, which in turn leads to increased workload and the associated stress among the employees. This paper investigates how a commercially available wearable low-cost sensor and two machine learning algorithms can be applied to measure and evaluate heart rate, heart rate variability and respiration rate to establish a relationship with workload. The investigated algorithms, namely Random Forest and K-Nearest-Neighbours are able to distinguish between tasks phases and rest phases as well as between easy and difficult tasks executed by the employee, which is the main novelty of this paper.

For many decades, solid rivets made of high-strength aluminum alloys have been used for mechanical joining of aircraft structures. For numerical modeling of riveting processes the detailed description of the deformation behavior of the rivets is of utmost importance; however, only very little reliable material data are available. Therefore, this study reviews and investigates the deformation behavior of commercial MS20426AD3-5 countersunk rivets made of aluminum alloy AA-2117-T4 as typically used in aerospace applications. The selfconsistent procedure for determining the material-specific flow curve includes (i) the exact preparation of cylindrical samples, (ii) compression testing of the samples at testing speeds of 0.05 mm/s, 0.5 mm/s and 5 mm/s, and (iii) inverse numerical modeling of the testing procedure. In general, the compliance of the testing setup must be considered for obtaining reliable flow curves, especially when testing small samples. The determined flow curve of aluminum alloy AA-2117-T4 showed higher yield stress and more distinct initial strain hardening than most of the flow curves published in literature. Although the flow curve did not show any significant strain rate dependency, notable softening due to deformation-induced adiabatic heating of the compressed sample was observed at the highest testing speed. However, the fracture strain seems to be strain rate-dependent, because samples deformed at the low testing speed did not show any signs of macroscopic fracture, whereas local fracture occurred in samples deformed at the medium and high testing speeds.

The number of persons with disabilities in Europe for ages sixteen and above is estimated to be 25% or over 100 million people. Due to an aging society, it is estimated that this amount will increase in the next years. Moreover, persons with disabilities have an above-average rate of unemployment. As a result, the large potential of the labor force for societies remains unused. In the area of production, there are opportunities to include these groups of people due to emerging technologies within Industry 5.0 and the age of digitalization. However, there is practically no holistic methodology to estimate the effort and benefits of the implementation of such technologies for the manufacturer. In this paper, we develop a methodology that allows for such an estimation to be made. The novelty of this paper lies in the fact that it presents a universal methodology that is not limited only to the manufacturer, but everywhere where persons with disabilities execute tasks. The practicability and application of this methodology are shown based on a real use case in an Austrian manufacturer.

3D printing is nowadays an integral part of modern production. New materials for 3D printing therefore play a crucial role in further establishing this technology. Hence, this paper investigates the printability of salt hydrates. To print these type of materials, a novel deposition method based on supercooled liquids is first developed mathematically in theory and verified experimentally in practice. It is shown that this new deposition method has significant advantages compared to the state of the art 3D printing, especially with regard to the printing speed.

To reduce CO2 emissions, besides the automotive industry, manufacturing industries are increasingly under pressure to optimize processes and procedures for energy efficiency. These optimizations mainly involve the production processes, where most energy demand occurs. Alternatively, when most of this energy demand can be determined during the product design phase, the product designer can make energy-efficient decisions in the design phase. Most product designers are unaware of their decisions' significant impact on a product's energy demand. Therefore, this paper presents a workflow and novel method for predicting the energy demand of parts of their machining operation during the design phase. For this purpose, 29 energy consumption models for machining processes are examined, and the data published in the literature are summarized. Four resulting comprehensive process maps are derived, which enable the prediction of a part's energy consumption due to machining, specifically, the milling operation, based on the geometric features of the part. This was further verified on three machined parts. The workflow and methods presented in this paper are some of the first steps to facilitate conscious decisions already in the product design phase. The benefits of the method were demonstrated in a final survey: Both product designers and machine operators showed in this survey that their estimation of the energy consumption of the parts investigated differed by orders of magnitude.

In recent years, research in the field of fuel cells is gaining momentum. Leading automotive industries are investing in e-mobility. In fuel cell research, topics such as fuel cell durability, degradation phenomenon, catalyst performance enhancement, etc., are mainly researched upon. However, one fails to recognize the need for innovations in the area of assembly systems for the stacking process. In this paper, a modular approach referencing the gripping of bipolar plates and the membrane electrode assembly layers for the stacking process is postulated. A vacuum end-effector (VEE) gripper is innovated, designed, and manufactured using 3D printing methods. It is then tested in real-time at the maximum acceleration of the Cobot. The cycle time of assembly per unit cell achieved was 1.25s; for comparison, manual stacking per unit cell is averaged to approx. 8s. Therefore, huge potential savings in assembly time was denoted. Additionally, the benefit of using a vacuum end-effector gripper mitigates the particulate matter induced via manual handling. This prevents the early degradation of the fuel cell. The assembly technology and the demonstrator postulated in this paper integrates a complete loop of design, manufacturing, and assembly of the VEE gripping mechanism.

The automotive industry is characterized by a high degree of uncertainty. Companies are facing the challenge of producing different systems simultaneously. Additionally, the global quantity of electric vehicles is also expected to increase significantly. This results in the following capability to remain competitive: Effective and efficient adaptions of production systems to model variations and volume increases. While flexible production is identified as the most promising concept, defining the actual flexibility level of included production resources is essential for its proper realization. A literature review on existing flexibility assessment approaches revealed their emphasis on high-level enablers and limited practical applicability in the automotive industry. In contrast, focusing the assessment on single workstations supports the selection of appropriate production resources. Therefore, a simple and structured standard procedure for a production resource flexibility assessment was developed. This theoretical construct was subsequently complemented with practical insights through its application on two real-life case studies within one automotive engineering company. Summarizing and discussing the findings in combination with a conclusion completed this paper.

Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity.

The high-frequency (HF) machine data is retrieved from the Spinner U5-630 milling machine via an Edge Device. Unlike cloud computing, an Edge Device refers to distributed data processing of devices in proximity that generate data, which can thereby be used for analysis [1,2]. This data has a sampling rate of 2ms and hence, a frequency of 500Hz. The HF machine data is from various experiments performed. There are 2 experiments performed (parts 1 and 2). The experimented part 1 has 12 .json data files and part 2 has 11 .json files. In total, there are 23 files of HF machine data from 23 experiments. The HF machine data has vast potential for analysis as it contains all the information from the machine during the machining process. One part of the information was used in our case to calculate the energy consumption of the machine. Similarly, the data can be used for retrieving information of torque, commanded and actual speed, NC code, current, etc.

The reduction of CO2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high frequency data (500Hz) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining.

Nowadays, the reduction of CO2 emissions by moving from fossil to renewable energy sources is on the policy of many governments. At the same time, these governments are forcing the reduction of energy consumption. Since large industries have been in the focus for the last decade, today also small and medium enterprises with production lot size one are increasingly being obliged to reduce their energy requirements in production. Energy-efficient CNC machine tools contribute to this goal. In machining processes, the machining strategy also has a significant influence on energy demand. For manufacturing of lot size one, the prediction of the energy demand of a machining strategy, before a part is manufactured plays a decisive role. In numerous previous studies, analytical models between the energy demand and the machining strategy have been developed. However, their accuracy depends largely on the parameterization of these models by dedicated experiments. In this paper, different machine learning algorithms, especially variations of the decision tree ('DecisionTree', 'RandomForest', boosted 'RandomForest') are investigated for their ability to predict the energy demand of CNC machining operations based on real production data, without the need for dedicated experiments. As shown in this paper, the most accurate energy demand predictions can be achieved with the 'RandomForest' algorithm. (C) 2021 The Authors.

Due to mass customization, the product variances have considerably increased. Hence, achieving high quality, high variety, and small batch size production can be expensive. A thorough literature review and research findings indicate the new technologies that can be considered for mitigating these disparities. This paper then presents a case study for achieving high quality reliable e-axle bearings through a developed data-driven Bearing Test Rig (BTR). The second case study indicates the feasibility of high variety assembly of products in an e-axle through a cobot that is utilized for bearing assembly, sealing application, and bolting operations. Both depict the human-machine interaction as a core element in future e-axle assembly. The results are evaluated with the help of FMEA analysis, LoA matrix, and a simulation model.

Over the years, e-mobility has scaled exponentially, specifically automobiles, and is seen as the future. In this paper, implementation of specific adaptive technologies into the assembly line has been performed and is presented in the form of case study. The architecture and model of these technologies utilised are presented and their benefits on assembly line. Thus, indication is proven towards building a highly flexible, adaptive and cost-effective assembly line. Evaluation techniques such as FMEA, tecnomatix simulation and level of automation (LoA) study are performed to prove the improvements in qualitative, adaptive and an increase in automation levels when compared to previous manual assembly. Furthermore, the variances in e-axles are depicted with reference to e-axles from eminent electric automobile manufacturers. This is followed with a generalised assembly procedure and a cost model for the e-axle assembly. The results depict the adaptability achieved along with cost benefits when compared to an automated assembly line.

The automotive powertrain is undergoing a transformation due to the increased electrification. This transformation brings a number of technical challenges relating to the design and integration of electrified elements into the powertrain with it. Powertrain-related targets can be achieved in an increasing number of ways by varying and balancing the powertrain elements. This chapter discusses the challenges that engineers are facing in the development and production of internal combustion engines, e-drives, transmissions, batteries, and fuel cells, and how a systems engineering approach is the key to understand and take advantage of all the possibilities in such complex electrified powertrains.

The proliferation of the industrial digitisation through paradigms such as Industry 4.0 or Industrial Internet of Things have created a more complex work environment for human workers. Even though robots and machines have been created to make workers’ jobs easier and less stressful, work-related stress is still present in industrial environments. In addition to the traditional stressors such as workload or strict deadlines, the constant demand for interaction with robots and machines and the poorly designed and unclear interfaces lead to additional cognitive stress for workers. In this poster, we outline a system based on wearable sensors that can collect and store data relevant to the assessment of workers’ stress. The sensors measure physiological indicators such as heart rate or respiratory rate, as well as geospatial data that includes GPS coordinates and workers’ movement, speed and acceleration. The data generated by the sensors is transmitted to the lightweight on-board unit on the back of the fabrics (T-shirt or bra) and then streamed to the processing unit (Raspberry Pi) via BLE. The collected data will be processed in order to describe the relationships between the stressors and the workers’ health status and behavior.

The extraction and use of machine tool data has been described theoretically in the literature for decades. Nevertheless, the use for an increased productivity is still constrictive. However, current Industry 4.0 Communication methods are defined as OPC-UA, MQTT, or REST. These communication standards can integrate machine tool and process data in IoT-Clouds or external devices. As part of a comprehensive machining data acquisition, specific variables and parameters are not recognized in most cases. Transferring user-individual data via Open-Source applications from machine tools to end devices is only possible with high effort. This paper describes the workflow for the acquisition of user-defined data of a machine tool via the Open-Source-Stack open62541 for OPC-UA.