Abstract: Abstract In the landscape of the emerging Industry 5.0, human–robot collaboration (HRC) represents a solution to increase the flexibility and reconfigurability of production processes. Unlike classical industrial automation, in HRC it is possible to have direct interaction between humans and robots. Consequently, in order to effectively implement HRC it is necessary to consider not only technical aspects related to the robot but also human aspects. The focus of this paper is to expand on previous results investigating how the learning process (i.e., the experience gained through the interaction) affects the user experience in the HRC in conjunction with different configuration factors (i.e., robot speed, task execution control, and proximity to robot workspace). Participants performed an assembly task in 12 different configurations and provided feedback on their experience. In addition to perceived interaction quality, self-reported affective state and stress-related physiological indicators (i.e., average skin conductance response and heart rate variability) were collected. A deep quantitative analysis of the response variables revealed a significant influence of the learning process in the user experience. In addition, the perception of some configuration factors changed during the experiment. Finally, a significant influence of participant characteristics also emerged, auguring the necessity of promoting a human-centered HRC.

Abstract: Abstract The main driver of industry 4.0 is data and information for realizing resilient manufacturing systems through data-based decision-making. This requires the data flow from different machinery and its processing. Therefore, a methodical survey on the logical and technical requirements for enabling an efficient data flow can be beneficial since it fastens the process of establishing the data flow and gives production and software engineers a common understanding of the data’s meaning. Hence, data, related metadata like the unit or the frequency of recordings, and technical information, e.g., the machine’s interface or the communication protocol, are necessary. Based on the classical value stream mapping (VSM) as a lean management tool, the first approach for a methodical gathering of data, value stream mapping 4.0, was established in 2016. Since VSM 4.0 provides technical properties for specifying the data flow, some shortcomings were identified, particularly related to technical information. This contribution introduces the VSM 4.0 plus that solved the identified shortcomings by adding new properties. Compared to VSM 4.0, VSM 4.0 plus offers detailed specifications of data points, includes technical information, e.g., used communication protocols, adds information regarding data processing, and enhances the storage and usage dimension by providing more details. Through the added properties, VSM 4.0 plus allows domain and software engineers more efficient collaboration since all required information for establishing the data flow and its processing are united in one methodology. To verify the practicability, the VSM 4.0 plus was applied within several manufacturing-related use cases.

Abstract: Abstract Forgings are produced in several process steps, the so-called forging sequence. The design of efficient forging sequences is a very complex and iterative development process. In order to automate this process and to reduce the development time, a method is presented here, which automatically generates multi-stage forging sequences for different forging geometries on the basis of the component geometry (STL file). The method was developed for closed die forging. The individual modules of this forging sequence design method (FSD method) as well as the functioning of the algorithm for the generation of the intermediate forms are presented. The method is applied to different forgings with different geometrical characteristics. The generated forging sequences are checked with FE simulations for the quality criteria form filling and freedom from folds. The simulation results show that the developed FSD method provides good approximate solutions for an initial design of forging sequences for closed die forging in a short time.

Abstract: Abstract The electric drives used in traction applications employ conventionally pure Cu bars bent to the required form, inserted in the stator and welded by a laser at the extremities. These extremities, which are referred to as Cu hairpins, should be stripped off from the electrically isolating polymeric enamel. Laser stripping is industrially used to remove the enamel from the Cu surface. Pulsed wave lasers are employed for the purpose with a large variety of solutions industrially available to the end users. The peculiar process may give way to material removal by surface heating for instance using infrared radiation (IR) or ultraviolet (UV) lasers or an indirect material expulsion via near-infrared (NIR) sources. Accordingly all major laser sources, namely CO 2 , active fiber, active disk, and Nd:YAG at different wavelengths, may be used for the purpose. Such laser sources possess very different characteristics regarding the pulse durations, power levels, and beam diameters. As newer laser system solutions are made available, the need for methods and experimental procedures to compare the process performance also increases. This work compares 7 different hairpin stripping solutions based on contemporary pulsed laser sources along with a detailed comparative analysis method. Initially, the 7 laser sources are used for hairpin stripping. The process quality is analyzed through surface morphology, chemistry, and the mechanical strength upon laser welding. Productivity and efficiency indicators are collected. Using the collected data, the work proposes system configurations for three different scenarios prioritizing quality, productivity, and cost.

Abstract: Abstract The unit process life cycle inventory is a modeling approach to estimate the energy demand and resource requirements of a unit process. Thus, a model of the unit process life cycle inventory for a specific manufacturing process can be used for quantifying the environmental impacts of specific products manufactured by that process. Within the approach, reusable models are developed for specific manufacturing processes. In this paper, the development and validation of a unit process life cycle inventory model for high-speed laser directed energy deposition is presented. This additive manufacturing process offers great potential for the industry due to its fast process speed. However, high-speed laser directed energy deposition has a high energy consumption and resource demand. Assessing the energy demand for individual manufactured products is a time-intensive process that requires expert knowledge. Thus, the development of an adaptable unit process life cycle inventory model enables more convenient assessment and improvement of its energy and resource efficiencies for producing different products. For the development of the model, the subsystems of a high-speed laser directed energy deposition machine are analyzed separately, e. g. the laser generator, the trajectory system, the powder feeder, and the suction system. Afterward, the energy and material demand of these subsystems are described in mathematical models. Finally, the model is validated by comparing the energy demands of three demonstration parts, measured by experiments, and predicted by the model.

Abstract: Existing literature on optimizing inventory levels in pharmaceutical supply chains has focused on a limited set of drivers. However, the global supply chain disruptions produced by the Covid-19 pandemic demonstrated the need for a more nuanced picture of the inventory management drivers in this sector to identify profitable inventory configurations while fulfilling demands and safety margins. To address this gap in the literature, this paper identifies key drivers impacting inventory levels and develops a framework for assessing inventory configurations in pharmaceutical supply chains. The framework is tested using a single case study approach. The case study showed that while external and downstream supply chain factors were recognized as being critical to pursuing inventory reduction initiatives, internal factors prevailed when making inventory management decisions. The framework developed in this paper may assist practitioners in identifying the most important factors impacting inventory levels within a specific pharmaceutical supply chain configuration and is in use in the industry today.

Abstract: Abstract Process forces are an integral characteristic for the evaluation of machining operations, which can be calculated using, for instance, empirical models. An adequate prediction is essential, especially as it provides input data for subsequent models, e.g., for stability analysis of milling processes. However, the calibration of force model coefficients is not unambiguous and may have multiple local optima, which can significantly affect the accuracy of the approximation of the cutting forces. In this context, the selection of experiments used for calibration is crucial to obtain adequate results. In this paper, a systematic analysis of the selection of the required calibration experiments and the influence of the resulting parametrization of the coefficients on the predicted milling forces and the stability limit was conducted.Based on the results, designs of calibration experiments could be identified, with which the influence of varying undeformed chip thicknesses could be represented adequately. By applying these force model parameterizations for stability analysis, an improvement in the prediction of stability limits was achieved.

Abstract: Abstract In this paper a cost and time efficient approach to setup a compliance model for industrial robots is presented. The compliance model is distinctly determined by the gear’s stiffness parameters which are tuned by an optimal design of experiments approach. The experimental setup consists of different poses of the robot’s axes together with the applied force at the tool center point (TCP). These robot poses represent together with defined forces the experimental setup where the deviation of the robot under defined force is measured. Based on measurements of the displacement of the TCP the stiffness parameters for the compliance model are estimated and afterwards validated in new experiments. The efficiency of this approach lies in the reduced amount of experiments that are needed to identify the stiffness parameters that are parameters inherent to the compliance and the less complex experimental setup.

Abstract: Abstract Metal cutting is characterized by high temperatures at the tool-workpiece interface. Although valuable information could be provided by the temperature values, their direct measurement still presents a challenge due to the high contact pressure and the inaccessibility of the process kinematic. In this research work, the current state of thin-film sensors for measuring temperatures on the chip-tool interface has been analyzed with a focus on the measuring phenomena: thermoelectricity and thermoresistivity. Thin-film sensors placed on the cutting tools in or close to the tool-chip contact area are expected to obtain accurate temperature information at the expense of a short lifetime. New insights into thin-film sensors manufacturing, design and calibration are presented, and a new concept of a three-point thermoresistive thin-film sensor is proposed. During orthogonal cutting tests the workpiece deformations were measured through high-speed imaging and the process temperatures were measured with thin-film sensors. In order to validate the temperatures and to obtain the temperature distribution on the cutting edge, Finite Element simulations were carried out. Finally, the potential of using cutting tools with integrated thin-film sensors for in-situ characterization is investigated and a statement for its limitations and potential applications is given.

Abstract: Abstract There is a continuing trend in the aircraft industry to automate production. In order to be able to react to shortages of skilled workers, high order fluctuations and machine breakdowns, cost-effective, mobile and flexible systems are required to support the workers. This paper focuses on the integration of existing skill-based engineering concepts into production using standard OPC Unified Architecture interface, where production systems can be built quickly by simply interconnecting modules. The interconnected modules together form higher level subsystems enabling reusability of the individual modules as well as the assembled subsystems across several use cases. The approach is evaluated on a production related mobile robot system, whose task is to drive to the workstation, reference the component and drill holes in a vertical tail plane section of an aircraft. All devices from different suppliers contain skill-based modules based on standards defined by OPC Foundation and communicate via OPC UA-based Client/Server communication.

Abstract: Abstract Automated robot-assisted disassembly is essential for the flexible disassembly of Li-ion battery modules for economic and safety reasons. In such a case, a CAD model for the planning process is of immense benefit. The geometric uncertainties due to the breathing of the Li-ion cells as well as the presence of component tolerances underline the importance of a sensor-based detection approach to determine the actual state of the battery module, which is crucial to ensure an automated and reliable disassembly process. In this paper, we present a method for 3D camera-based localization of points on deformed battery modules, aiding in identifying support points for milling operations in robot-assisted disassembly cells. This separation operation planning employs a CAD model, and our introduced computer vision “data processing pipeline”—a systematic series of processing steps—bridges the gap between the CAD model and the actual battery module. This involves capturing the module using a 3D camera and subsequently registering its points with the CAD model’s points. Central to this process are two algorithms: The Bayesian Coherent Point Drift (BCPD) algorithm ensures accurate non-rigid registration, while TEASER++ aids in reducing computational time. We demonstrate the effectiveness of these combined algorithms in our pipeline through rigorous testing and metrics, evidencing that a balance between accuracy and computational speed can be attained by adjusting point density.

Abstract: Abstract Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).

Abstract: Abstract Thermomechanical tangential profiled ring rolling enables the manufacture of ring shaped parts with specified geometry and hardness within a single step. Some influence over the cooling rate of the ring is achieved using supplementary cooling with compressed air. Earlier work has shown that some form of control is necessary in order to obtain an acceptable reproducibility. In this study, the possibility to control the temperature of the ring using closed loop control is shown. A model of the controlled system is implemented in order to determine PID parameters. Finally, the improvement of the process repeatability is demonstrated.

Abstract: Abstract The aviation industry is characterised by high manufacturing requirements of products with difficult-to-machine materials to ensure quality and safety. Standardised and secured processes and transparency in resource and material flows within production are important requirements for meeting these safety and quality standards while staying competitive on the market. Those requirements also apply to a companies’ tool management and are to be met with an optimised tool change strategy considering economic aspects at the same time. The article presents a use case of a company belonging to the aviation industry striving to achieve goals concerning costs, quality, and time in their tool management. To realise potential improvements a retrofitting traceability solution is illustrated enabling data-based maintenance strategies in the use case. The traceability solution aims to provide transparency about tool inventory, the location of tools on the shop floor and functions as data acquisition system to realise the individual tracking of used tools. Using the individual tracking data of tools and matching them with relevant machining data enables the application of data-based maintenance strategies pointing out possibilities to indicate the tools’ wear state. This approach offers benefits such as reducing the scrap rate or machining down times with a direct impact on quality, costs, or lead times of customer orders.

Abstract: Abstract Clocked manufacturing processes such as sheet metal forming and cutting processes pose a challenge for process monitoring approaches due to inaccessibility of tool components and high production rates which make direct measurement of the physical process conditions unfeasible. Auxiliary data such as force signals are acquired and assessed, often still relying on control and run charts or even visual control in order to monitor the process. The data of these signals are high-dimensional and contain a large amount of redundant information. Therefore, the processing of such signals focuses on compressing information into as few variables as possible that still represent the important information for the manufacturing process. Due to repeatability in clocked sheet metal processing, the data generated consist of a series of time series of the same operation with varying physical conditions due to wear and variations in lubrication or material properties. In this paper two major research objectives are identified: (i) the theoretical evaluation of representation learning methods in context of clocked sheet metal processing, and the connection with (ii) the practical evaluation of the learned representations with a given use case to track the wear progression in series of strokes. The contribution of this paper is the comparison of varying time series representation learning techniques and their performance evaluation in a theoretical and practical scenario.

Abstract: Abstract Several studies show a demand for further transfer of Industry 4.0 concepts and technologies into practice. This paper describes how companies can be supported methodologically to identify individual opportunities for optimization within their future smart factories. Existing methods are screened, recombined, and enhanced to create a sufficient demand-oriented approach, based on the lean value stream mapping and design. The final approach covers a mapping phase of the current state and a design phase of a future state. It follows the general principle of process optimization prior to digitalization. Being a highly visual method, it can be used to explain the needs and the motivation for the identified improvement initiatives to all involved parties, including non-IT-specialists. This paper also presents an exemplary result and several learnings from tests with three companies with discrete manufacturing processes.

Abstract: Abstract Frictional losses occur in tapered roller bearings, particularly at low rolling speeds, which pose a risk of wear. The increased friction losses are a result of insufficient lubricant film thicknesses in the rolling and rib contact. Micro-lubrication dimples can be used to induce additional lubricant into the contact zone and minimize friction. The aim of this paper is therefore to implement a suitable machining strategy for the production of defined microstructures for tribologically optimized applications and to identify relationships between geometry formation and process parameters. For this purpose, the microstructure milling process was first modelled with a material removal simulation, including tool displacement. Additionally the kinematic limits of the machine tool were determined. The tool displacement was determined experimentally for this purpose. Subsequently, the findings from the simulation were used to induce microstructures in a defined manner on tapered roller bearings made of hardened 100 Cr6 steel. The investigations showed that the defined generation of lubrication dimples is possible with the developed machining strategy. Due to the inclination of the inboard bearing, there is a deviating depth of engagement when the tool penetrates, which also increases the tool displacement. As a result of the microstructure milling process, burr formation occurs, which shows a dependence on cutting speed and structure alignment. Increased burr formation and tool wear at structure orientations of 45° and 70° were found.

Abstract: Abstract Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not available in sufficient size and quality, and is class imbalanced due to the predominance of good parts. Data-driven manufacturing analytics requires data of sufficient quantity and quality. In order to predict quality characteristics, production data is collected across processes in the industrial use case at Bosch Rexroth AG for the purpose of inferring results in hydraulic final inspection using machine learning methods. Since high quality data generation is costly, synthetic data generation methodologies offer a promising alternative to improve prediction models and thus generate safer, more accurate predictions for manufacturing companies. Among the synthetic data generation methodologies used, variational autoencoders compared to generative adversarial networks and synthetic minority oversampling technique methods are best suited to synthesize the feature with highest feature importance from a small sample data set compared to the production data and improve the prediction for the target variable.

Abstract: Abstract To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.

Abstract: Abstract 7xxx aluminum alloys have very high specific strengths, making them interesting for manufacturing car bodies. However, the limited formability of the precipitation hardenable alloys in the artificially aged T6 temper is challenging for forming and joining processes. The pre-conditioning of aluminum sheets by a short-term retrogression is a promising approach to enhance the process limits of joining by forming technologies. In addition, localizing the heat treatment enables the control of the material flow. However, for a targeted material flow, a deeper understanding of the relation between the heat treatment layout and its influence on joining processes is needed, which can be achieved with the aid of numerical analyses. Therefore, within the scope of this work, an approach for the numerical simulation of locally limited heat treatment layouts for the control of the material flow of high-strength aluminum sheets in joining by forming process is shown. The investigations are conducted for the simulation software LS-DYNA and the innovative shear-clinching process. Process models for the local retrogression heat treatment of punch-sided AA7075 T6 and the subsequent shear-clinching as well as an approach to couple the individual models are presented. By performing a localized retrogression from the T6 temper, the radial material displacement of the upper sheet in the shear-clinching process can be reduced in comparison to joining AA7075 in the W temper. The control of the material flow in the joining process thereby causes the improvement of geometric features, which are critical for the joint strength.

Abstract: Abstract The cutting of difficult to machine materials such as titanium alloys is challenging for the machining industry. In case of the titanium alloy Ti6Al4V, the properties of the material cause high temperatures, mechanical loads as well as high frequency vibrations at the cutting edge, leading to premature tool failure. The use of uncoated carbide tools is very common for machining of Ti based alloys. However, temperature active, self-lubricating physical vapor deposition (PVD) coatings like CrAlMoN showed promising results to reduce friction and wear during turning of Ti6Al4V. In the present study, self-lubricating (Cr 34 Al 41 Mo 25 )N, (Cr 29 Al 36 Mo 35 )N and (Cr 25 Al 31 Mo 44 )N coatings were investigated on cemented carbide tools. These were deposited by a hybrid process combining direct current Magnetron Sputtering and High Power Pulsed Magnetron Sputtering. Coating morphology, thickness, chemical composition, indentation hardness and modulus at ϑ = 20 °C, ϑ = 200 °C, ϑ = 400 °C and ϑ = 600 °C as well as the oxidation behavior were analyzed. Moreover, wear development after cutting tests using a CNC-lathe was investigated. Independent of Mo-content, all coating variants possessed a dense morphology and a smooth surface topography, as well as a coating adhesion class of HF1 to the cemented carbide substrate in Rockwell indentation tests according to DIN 4856. With an increasing amount of Mo, heat treatment temperature and time, more self-lubricating molybdenum oxides such as MoO 3 and Mo 4 O 11 were detected by Raman spectroscopy. Therefore, the coating with the highest amount of Mo possessed the highest amount of molybdenum oxides. After cutting tests, molybdenum oxides were also found on the tool flank face by Raman spectroscopy. The level of flank wear land width decreased with increasing amount of Mo.

Abstract: Abstract Against the background of the climate crisis, fast innovation space in emerging technologies and the global competitive environment for manufacturing companies, a sound understanding of the life cycle behavior of factory systems becomes more and more important. The decision context of the factory life cycle conveys a high level of complexity, e.g. by the heterogeneous nature of factory element life cycles, manifold interactions between them as well as external change drivers. A model-based understanding as well as methods and tools are required that support factory planners and operators in this regard. This paper presents an approach for the modeling and quantitative evaluation of life cycle dynamics in factory systems while respecting the dynamic behavior of factory operation, as well. The purpose of the modeling is to deepen the knowledge of the prevailing life cycle mechanisms and their implications for factory planning and operation. The application of the approach is demonstrated in an exemplary case study.