It was investigated how a grain refinement can be introduced into a cylindrical rolled piece by means of cross rolling in a flat-jaw design. For this purpose, a non-round shape was rolled in and out again. A theoretical preliminary investigation was used to define a suitable parameter field for experimental evaluation. Metallographic investigations showed that cross-rolling had a positive effect on the microstructure, and IPH will inform the client immediately if it becomes apparent that the estimated costs will be exceeded. The execution of work that leads to the estimated expenditure being exceeded shall only take place after consultation with the client.
Cross-Wedge Rolling, fine grain
A major advantage of flashless precision forging is the saving of resources by avoiding excess material. Especially materials with good flow properties, such as aluminum, sometimes lead to the formation of thin flash. Seals have been developed and experimentally tested to prevent thin flash, i.e. the flow of material into tool gaps. The study presented shows the effectiveness of different sealing materials and the ideal sealing geometry.
thin flash, Sealing concept, precision forging, forming technology, process optimization
This article examines the use of point clouds as a geometric data basis for factory planning and compares different mapping techniques for generating these point clouds. Data and information acquisition is a crucial step in factory planning and thus in developing efficient production processes. In this context, different mapping techniques are analysed: photogrammetry (using drones and action cameras) and LiDAR scans (performed both from drones and from the ground).
The methodology and results of this investigation are discussed in detail, highlighting the advantages and disadvantages of each mapping technique. The focus is on comparing the generated point clouds in terms of completeness, recognisability and geometric tolerance. This comparison provides valuable insights into which technique is best suited for the data acquisition of factory planning. The outlook of this paper includes the further development of recording techniques, particularly with regard to autonomously flying drones. In the future, these could enable more efficient and precise data acquisition for factory planning and thus further strengthen the basis for optimising production processes.
Drone, Photogrammetry, LiDAR, Point cloud, Factory planning, Data acquisition
Around half of the currently 30,000 active wind turbines in Germany will reach the end of their service life by 2030, which is generally defined by the manufacturer as 20 years of operation. The most common strategy for the subsequent use of a wind farm is repowering, provided this is (legally) possible at the respective location. One option for dealing with old turbines is to resell them. At the time of repowering, in Germany after an average of around 17 years, the wind turbines usually still have a remaining operating time of several years before critical parts such as generators fail.
This article presents a machine learning model for predicting the resale value of used wind turbines. This model can be used to approximately predict the resale value of comparable wind turbines based on certain input parameters such as the power output or the age of the wind turbines. The model was trained using an adjusted data set from an online trading platform for wind turbines. The necessary pre-processing steps such as the removal of extreme outlier values and the addition or replacement of missing or incorrect wind turbinespecific data from a second data source using a self-developed matching algorithm are presented. Finally, the prediction accuracy of different ML algorithms is tested using test data to find the best method for predicting the resale value of wind turbines.
Renewable energies, wind turbines, resale values, machine learning, data science
The article addresses a method for generating project schedules for factory relocations based on a planned layout concept in reorganisation projects. The layout is modelled in a cell grid with cells of uniform edge length. The factory objects are arranged in this grid for both the actual and the target layout state. Further input parameters relate to the required time and personnel resources according to several operations. A two-stage optimisation algorithm is presented, which first checks a given solution for layout feasibility and then generates and solves a project scheduling problem on this basis. Accordingly, elements of the facility layout problem are integrated into a resource constrained project scheduling problem. The process steps of generating and evaluating of the project schedule are further embedded in a genetic algorithm, which successively improves the solution. The project schedules for relocation are evaluated through a combination of the resulting project duration and the downtimes of all factory objects. The aim of the optimisation is to minimise both objective values considering a weighting factor. The article concludes by validating the method using a practical example.
factory planning, relocation planning, reorganisation, project scheduling, project planning
Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.
Process monitoring, Wear, Hot forming, Predictive maintenance, Quality management
Wear due to thermal and mechanical stresses is one of the major causes of forging die failure. The assessment of die condition and the associated die life is usually based on experience. This paper presents a method to objectively predict the remaining life of a forging die. With this method a prediction based on optical measurements can be calculated. Practical tests show the possible applications. In addition, force measurements are performed and analyzed to determine how wear affects the force distribution in the die. The assessment based on optical measurements allows objective statements about the remaining tool life of forging dies. The analysis of the force measurements shows potential for predicting tool life but needs further investigation.
You can view the article here.
Process monitoring, wear, optical measurements, force measurement
A continuously growing number of product variants increases the demands on the flexibility of intralogistics transportation. One way to achieve greater flexibility is the use of cellular automated guided vehicles, which can be variably interconnected depending on the size of the product to be transported. This article explains the characteristics of cellular automated guided vehicles and the relationships between influencing variables of the cellular transport system and economic and logistical target variables.
Intralogistics, automated guided vehicles, cellular transport units
The resilience of a supplier is a fundamental factor in the design of supply chains. In supplier selection, resilience is not yet fully taken into account in the evaluation process. As part of a research project, the aim is to create a basis for this and thus integrate resilience as an evaluation criterion into the supplier selection process. The article aims to create an understanding of the various success factors of resilient supply chains.
Resilience, sustainability, supply chain management, risk management
Automated industrial trucks master difficult driving situations worse than humans – for now. New approaches based on artificial intelligence (AI) are intended to replicate human driving behavior and give automated systems more flexibility.
artificial intelligence, intralogistics, industrial trucks
Twelve interviews were conducted with IT system users and providers in order to investigate the adaptability of IT systems in manufacturing companies. A methodical evaluation of the interviews made it possible to identify factors that influence the service life of IT systems. The evaluation shows that, in addition to the technical characteristics, human and organizational aspects in particular are decisive for the long-term use of IT systems.
Information management, people and technology, software
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
Bees are an important part of local ecosystems. Many companies have beehives set up on their company premises and looked after by beekeepers as an ecological measure. Remote digital monitoring can be a useful way of reducing the workload while ensuring the health and continued existence of the bee colony. The developed prototype and AI-based object recognition offers beekeepers the opportunity to monitor activity levels in the hive, detect intruders such as wasps or recognize a drone brood at an early stage, which occurs when the queen bee dies.
AI, image-recognition, Bees
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
In an increasingly digitalized world, IT systems such as ERP or MES are the backbone of efficient processes. But many companies find that their IT systems neither meet changing requirements nor effectively support new processes. This leads to either the development of shadow IT, provisional adjustments to the IT system, or the selection of a new IT system. The VIPER research project was launched to counteract such developments and to improve the selection process for a new IT system. FIR at RWTH Aachen University and the IPH – Institut für Integrierte Produktion Hannover gGmbH have set themselves the goal of helping companies to extend the service life of their IT systems and make better decisions when selecting new IT systems by analysing the entire socio-technical information system.
IT systems, ability to change
Development of a prototype for automated data quality control at Trivium Packaging GmbH, a manufacturer of metal packaging. The project aimed to ensure the reliability of production data for KPI calculations by implementing plausibility checks for machine and line status to identify faults at an early stage. The use of AI and algorithms to monitor and evaluate data quality in real time led to improved efficiency and performance of the production lines. A guide to ensuring data quality for reliable KPI calculations was also created to help small and medium-sized enterprises increase their competitiveness.
Data quality, production, manufacturing, KPI, metrics
In the research project “AutoPress”, the IPH – Institut für Integrierte Produktion Hannover gGmbH and Jobotec GmbH are jointly striving to develop an automated process control of screw presses. By retrofitting and applying an optimization algorithm, the energy demand is to be reduced and the component quality increased.
digitalization, forming technology, production technology
Automated guided vehicles (AGVs) are floor-bound systems,
consisting of several components that can organise the logistical transport of materials in an automated and driverless manner.
The advantages associated with the introduction of AGVs can be particularly interesting for small and medium-sized enterprises (SMEs).
can be particularly interesting. By specifically increasing the degree of automation in the organisation of internal processes with the help of AGVs, an increased volume of material transport can be handled reliably and with high availability.
can be handled reliably and with high availability without
without having to tie up more personnel at the same time. This means that personnel resources can be utilised more efficiently and relieved by using AGVs.
Digital route network planning, digital factory planning, AGV introduction
IPH is developing an autonomously flying indoor drone in the AIMS 5.0 research project as one of 20 application examples for the industry of the future. The project is funded by the EU and involves 53 research and industry partners from twelve countries.
Drone, copter, data acquisition, digital twin, industry 5.0
An ageing society and the emergence of new diseases are generating rapid growth in the healthcare industry in many industrialized countries. The use of AI can lead to an increase in performance while at the same time reducing costs. This is why the use of AI in the medical technology is continuously increasing, driven by the numerous benefits it brings, including
- Diagnosis can be significantly optimized by incorporating extensive experiential knowledge into and retrieving it from AI systems. In this respect, AI enables us to analyze images, laboratory results and patient files can be analyzed and evaluated.
- Individualized treatment plans can be developed through the use of AI can be developed. These plans holistically take into account all aspects of the patient and thus help to significantly increase the effectiveness significantly increase the effectiveness of the therapy.
- Predictive analyses are made possible through the use of AI. AI can identify risk factors and predict complications.
AI, Unsupervised Learning, Diagnostics
