The transition towards a circular economy (CE) has become inevitable to mitigate challenges of resource scarcity and resource price volatility, as well as minimize the climate and environmental impact. By 2030 for the EU, CE could result in a reduction of primary material consumption by 32% [1]  and greenhouse gas emissions by 48 % compared with the 2012 levels[2]. The European Environment Agency[3] estimates that the net benefits for businesses by implementing CE measures range from EUR 245 billion to EUR 604 billion. Although these figures are promising the reality is rather bitter. Remanufacturing[4] which is one of the most important strategies in implementing CE principles in the manufacturing industry, has an intensity (ratio of remanufacturing to new manufacturing) of only 1.9%, while the intensity in the automotive sector is 1.1% in the EU[5]. This means that the remanufacturing intensity in all sectors needs to be increased significantly to exploit the untapped potential of CE. 

At Scania and many other Original Equipment Manufacturers (OEMs) and Suppliers (OESs), the flow of new components for the production of new vehicles is handled independently from the flow of remanufactured components that are intended for the aftermarket. The gearbox is one such component. For any company to intensify its circularity to the level that society needs without cannibalising the aftermarket business, integrating remanufactured components in the production of new products is essential[6]. However, OEMs have thus far not attempted to systematically integrate remanufactured components in the new products.

The iReGear project presents the first successful demonstration of integrating a remanufactured gearbox into the production line of new trucks and serves as an objective demonstration that the remanufactured gearbox performs as good as a new one. The project also investigated and confirmed that there are no legal obstacles to using remanufactured components in new trucks, provided that customers are informed about it. Additionally, it has been established that there are no existing examples of remanufactured components being used in new vehicles.

Two major Scania customers have indicated their willingness to accept remanufactured components in new trucks, as long as the performance and competitiveness of the trucks are not compromised. They also expressed a readiness to pay more for such a solution, given that it reduces overall emissions, and their customers are willing to pay for these added environmental benefits.

An attempt has also been made to formulate two basic equations and a procedure to estimate the economic and environmental potential of scaling up the use of remanufactured gearboxes in new trucks. Moreover, it is also estimated that the current remanufacturing intensity of the gearbox is only 0.4% relative to the number of new gearboxes produced by Scania each year indicating a significant potential for increasing the volume of remanufactured gearboxes.

This research makes a significant contribution to the ongoing discussion and provides the first evidence to support the argument that it is feasible to envision future manufacturing organizations seamlessly integrating manufacturing and remanufacturing operations to develop Circular Manufacturing Systems that consume fewer resources, produce fewer emissions, and cost less without compromising quality and performance. Future research should advance with a vision toward a Circular Manufacturing System, where the integration of remanufactured components becomes the status quo. Along the way, efforts should also focus on enhancing the efficiency of remanufacturing by addressing the shortcomings of conventional approaches.

Circular Economy (CE) promotes trading functions of a product as aservice instead of selling the product in conventional ways. For a product like ababy stroller, the function means ensuring mobility with infants without needingto own a stroller. This approach of acquiring functions only when needed opensup the possibility to share the same products with multiple users. For a manufacturer that has built its business on a conventional sales model over the decades,this shift may be too radical. Therefore, for the manufacturers, it is important tounderstand consumer perceptions of the service-oriented business model beforeentering this unknown territory. To develop a thorough understanding of consumerperceptions of leasing a stroller instead of buying one, a survey among 200 parentsin Stockholm is conducted. The survey brings out quantitative results such as 39%of respondents are open to leasing and identifies key influencing factors such asconvenience and environmental image that play a key role for the remaining 61%of respondents to choose leasing. This research concludes that a large numberof consumers are open to leasing if a high level of service and environmentallysustainable strollers are offered at a competitive price.

This chapter presents a guiding framework for circular economy implementation in supply chains. Closing the loop for resource efficiency is a well-known practice in the industry. To concretize the circular economy implementation strategies, closed-loop thinking requires innovation and adaptation. Circular supply chains (CSCs) are one of the key enablers in closing the loop by design or intention for value recovery and profit maximization. CSC is an emerging area, and the view of CSC where forward and reverse supply chain is seamlessly integrated with the overall aim to achieve system-wide circularity is missing in the academic debate. By offering a cross-functional and systemic perspective of circular supply chains, we present a guiding framework to structure and understand the underlying complexities and highlight the crucial elements of circular supply chain implementation. The framework categorizes the circular supply chain into four building blocks: systemic approach, main drivers, levels of decision making, and mechanisms to manage the full loop closure and minimize the inherent uncertainties of a complex system. We conclude the chapter by illustrating the applicability of the circular supply chain framework using two industrial cases that are transitioning toward the circular economy.

The transition from conventional standalone metrology to integrated metrology has been accelerating in advanced manufacturing over the past decade. This keynote paper defines the concept of integrated metrology, which extends beyond parts inspection and encompasses processes and manufacturing equipment to enhance efficiency and productivity. The paper presents the characteristics, benefits, constraints, and future possibilities of integrated metrology for parts, processes, and equipment. It also includes a classification of the physical quantities of measurands, the corresponding measuring instruments, data and communication methods, uncertainty, and traceability. The paper also discusses future challenges and emerging trends.

In this review, we propose a comprehensive overview of additive manufacturing (AM) technologies and design possibilities in manufacturing metamaterials for various applications in the biomedical field, of which many are inspired by nature itself. It describes how new AM technologies (e.g. continuous liquid interface production and multiphoton polymerization, etc) and recent developments in more mature AM technologies (e.g. powder bed fusion, stereolithography, and extrusion-based bioprinting (EBB), etc) lead to more precise, efficient, and personalized biomedical components. EBB is a revolutionary topic creating intricate models with remarkable mechanical compatibility of metamaterials, for instance, stress elimination for tissue engineering and regenerative medicine, negative or zero Poisson's ratio. By exploiting the designs of porous structures (e.g. truss, triply periodic minimal surface, plant/animal-inspired, and functionally graded lattices, etc), AM-made bioactive bone implants, artificial tissues, and organs are made for tissue replacement. The material palette of the AM metamaterials has high diversity nowadays, ranging from alloys and metals (e.g. cobalt-chromium alloys and titanium, etc) to polymers (e.g. biodegradable polycaprolactone and polymethyl methacrylate, etc), which could be even integrated within bioactive ceramics. These advancements are driving the progress of the biomedical field, improving human health and quality of life.

The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility.

As manufacturing technology evolves and competition for customers between companies increases, the importance of producing high-quality products efficiently while minimising waste also increases. To achieve this, current production processes need a deeper investigation into how the efficiency can be improved. This project investigates the implementation of a more efficient sorting technique to enhance both space utilisation inside the furnace and the overall utilisation of the furnace. This is accomplished through a discrete event simulation (DES) of the tempering process at SSAB. The software used for this DES was MATLAB Simulink, chosen for its extensive block library and ability to simulate complex processes. Data previously collected from SSAB's own database were used as inputs for the model. Several visits to the tempering production site were conducted to learn and understand the process, identify the different steps needed for simulation, and observe the limitations to be included in the model. The simulation model was then used to simulate five different test scenarios to evaluate the most efficient sorting parameters: no sorting, the current situation, temperature, thickness, and length. The results from simulating these five scenarios indicated that temperature and length are the best sorting parameters, with an insignificant difference in produced plates between the two. Based on the insights gained from these simulations, three concepts were developed. The first concept uses the current available space with a new sorting method based on temperature and length. The second concept employs the same sorting method but with newly created space to sort on achieved by removing old inventories. The third concept involves adding two furnaces and is divided into two sub-concepts: one applied to the current situation and the other to the first concept, thus testing the new furnaces in both the existing space and the newly sorting method. The research identified that sorting by length and temperature significantly enhances production rates and furnace utilisation compared to the current method. Parameter studies revealed that temperature criteria are the most limiting factor in creating optimal plate combinations. Recommendations include optimising selection and sorting criteria and improve the developed model to better represent real-world operations.

As a result of digitalization, data is collected at every level of production as an enhancer for decision-making. However, including more sensors to collect additional information does not directly contribute to increasing the system reliability but instead raises challenges for optimal data utilization. This work presents an evaluation approach based on FMSA (Failure mode and symptoms analysis) combined with FMECA (Failure mode, effects and criticality analysis) prioritization methods. The different methods are applied to a feed-drive system to evaluate the suitability of the currently implemented detection and monitoring techniques. The recommendations derived from the evaluation can be utilized to maximize confidence in the monitoring and to minimize the sensors utilization and data collection. Since the FMEA family of assessment tools present shortcomings such as bias and uncertainty associated with their results, this work also aims at mitigating these effects in obtaining the monitoring priority numbers and their respective categorization and prioritization.

Industrial manipulators are desired to be commonly used for material removal applications due to their high flexibility, low cost, and large working space. However, their lower stiffness (compared to a machine tool) leads to a reduction in path accuracy. This reduction directly affects the dimensional accuracy of the machined part. Additionally, the low stiffness in the presence of dynamic process forces creates vibrations influencing the surface quality, tool life, and service life of the manipulator. Static stiffness models, optimization, and compensation techniques exist to minimize force-induced deflections. Multi-body dynamics analytical models still lack the required accuracy in predicting the position-dependent dynamics of the manipulator. Dynamics data-driven models are rising to tackle the uncertainties in modeling the robot properties. This study presents the position-dependent variation of the dynamic characteristics, namely frequency and damping, of a mid-size articulated industrial manipulator, which were determined through experimental modal analysis. The position-dependent dynamics is investigated and quantified in a low-frequency range and is discretely measured and presented in two perpendicular planes (horizontal and vertical) of the robot working space. The study concludes with a discussion on the potential to apply the dynamic information obtained experimentally for the process planning and working space optimization in contact applications. 

This article presents the measurement and analysis of the influence of velocity on the quasi-static deflections of industrial manipulators of three different manufacturers. Quasi-static deflection refers to the deflection of the end effector position of articulated robots during movement at low velocity along a predefined trajectory. Based on earlier reported observations by the authors, there exists a difference in the static and quasi-static deflections considering the same points along a trajectory. This work investigates this difference to assess the applicability of robotic compliance calibration at low velocities. For this assessment, the deflections of three industrial articulated robots were measured at different speeds and loads. Considering the similarity among the robot models used in this investigation, this work also elaborates on the potential influence of the measurement procedure on the measured deflections and its implications for the compliance calibration of articulated robots. For all industrial articulated robots in this investigation, the quasi-static deflections are significantly larger than the static ones but similar in trend. Additionally, the magnitude of the quasi-static deflections presents a proportional relationship to the Cartesian velocity.

Industrial articulated robots appeal to high force processes such as material removal applications mainly due to their high flexibility and large working space. However, due to the articulated robot's lower stiffness, significant deformations arise in the presence of process forces which reduces the robot's positioning accuracy. To improve the positioning accuracy in tasks performed under load, offline or online compliance compensation methods are implemented. This study presents an experimental comparison of the implementation and performance of offline and online compliance compensation strategies in a high-force application, i.e., loaded circular trajectory, characterized by the presence of quasi-static forces. The performance of the two compensation strategies was evaluated by calculating the mean deformation (comparison between unloaded and loaded trajectories). The results indicate that the performance of the online compensation strategy exceeded the offline compensation strategy performance for the case study analyzed. The limitations and potentialities of the different compensation strategies are discussed in terms of implementation and applicability for contact applications.

Additive manufacturing (AM) using powder bed fusion is becoming a mature technology that offers great possibilities and design freedom for manufacturing of near net shape components. However, for many gas turbine and aerospace applications, machining is still required, which motivates further research on the machinability and work piece integrity of additive-manufactured superalloys. In this work, turning tests have been performed on components made with both Powder Bed Fusion for Laser Beam (PBF-LB) and Electron Beam (PBF-EB) in as-built and heat-treated conditions. The two AM processes and the respective heat-treatments have generated different microstructural features that have a great impact on both the tool wear and the work piece surface integrity. The results show that the PBF-EB components have relatively lower geometrical accuracy, a rough surface topography, a coarse microstructure with hard precipitates and low residual stresses after printing. Turning of the PBF-EB material results in high cutting tool wear, which induces moderate tensile surface stresses that are balanced by deep compressive stresses and a superficial deformed surface that is greater for the heat-treated material. In comparison, the PBF-LB components have a higher geometrical accuracy, a relatively smooth topography and a fine microstructure, but with high tensile stresses after printing. Machining of PBF-LB material resulted in higher tool wear for the heat-treated material, increase of 49%, and significantly higher tensile surface stresses followed by shallower compressive stresses below the surface compared to the PBF-EB materials, but with no superficially deformed surface. It is further observed an 87% higher tool wear for PBF-EB in as-built condition and 43% in the heat-treated condition compared to the PBF-LB material. These results show that the selection of cutting tools and cutting settings are critical, which requires the development of suitable machining parameters that are designed for the microstructure of the material.

Reducing plastic interactions between abrasive grains and the material being processed improves grinding efficiency and lowers energy consumption. Widening the cutting zone with abrasive grains enhances chip formation and reduces lateral material displacement. This can be achieved by using abrasive microaggregates. The paper presents an experimental analysis of grinding with modified wheels containing abrasive microaggregates. It examines how these microaggregates impact the grinding wheel's surface microgeometry and material removal efficiency. The study measured changes in the number, surface area, volume, and spacing of active contact areas on the grinding wheel active surface. A comparative analysis using the Shos indicator showed that abrasive microaggregates promote the formation of active areas with wide cutting edges perpendicular to the cutting direction. Finite element method simulations confirmed that abrasive microaggregates enhance material removal by widening the micro-cutting zone and increasing lateral resistance, which reduces the formation of flashes along the cutting path. The study also assessed how these surface features impact the roughness of the ground surface. A comparative analysis of roughness parameters showed a statistically significant reduction in surface, volume, hybrid, and functional parameters when using grinding wheels with abrasive microaggregates. This analysis was conducted using bootstrap statistical hypothesis tests.

This research paper outlines the methodology and application of geometric and static accuracy assessment of articulated industrial robots using the Extended-Double-Ball-Bar as well as the Loaded-Double-Ball-Bar. In a first experiment, the EDBB is used to assess the geometric accuracy of a Comau NJ-130 robot. Advanced measuring trajectories are investigated that regard poses, which maximize the error influences of individual robot components. The developed error-sensitive trajectories are validated in experimental studies and compared to the circular trajectories according to ISO-230-4. Next, the Loaded-Double-Ball-Bar is used to assess an ABB IRB6700 manipulator under quasi-static loads of up to 600 Newton using circular testing according to ISO-230-4 and stiffness is identified. Then, the stiffness is used to perform a reverse calculation to identify the kinematic errors on the path deviations. The concept is validated in a case study of quasi-static loaded circular testing using the Loaded-Double-Ball-Bar compared to a Leica-AT960 laser tracker.

The transition to circular manufacturing systems (CMS) is crucial for achieving sustainable growth, addressing the environmental concerns and resource scarcity challenges. Shifting towards CMS requires a systemic approach that integrates value proposition models, product design, and supply chains (SCs). Circular supply chains (CSCs) emerge as a core pillar of CMS, incorporating value delivery, use, recovery, and reuse. CSCs are inherently more complex and dynamic than linear SCs requiring a holistic analysis approach to capture their complex and dynamic attributes. This research proposes an integrated analysis framework combining qualitative and quantitative approaches to explore the complexities and dynamics of CSCs and assess their economic, environmental, and technical performance. Through the lens of two different CMS implementation case studies, one in automotive parts remanufacturing and one in white goods manufacturing, this research illustrates the framework's applicability. In the automotive case, centralizing core management activities was found to improve economic performance by 50-54 %. However, the introduction of regional logistics hubs, while economically efficient, led to a 20 % increase in CO2-equivalent emissions. On the other hand, the white goods case study highlighted the trade-offs in centralizing end-of-life recovery facilities, where financial savings of up to 60 % were offset by increased transportation costs and increased CO2 emissions. The analysis of CSCs in these two distinct manufacturing sectors underscores the relevance and flexibility of the proposed framework, providing decision-makers with a tool to examine how different CSCs configurations and strategies impact overall performance. This guidance is crucial for developing optimal CSCs design and implementation strategies.

This work investigates the influence of a single tensile preload, applied prior to fatigue testing, on the fatigue strength of 316L stainless steel parts manufactured using laser-based powder bed fusion (PBF-LB) with a porosity of up to 4 %. The specimens were produced in both the horizontal and vertical build directions and were optionally preloaded to 85 % and 110 % of the yield strength before conducting the fatigue tests. The results indicate a clear tendency of improved fatigue life and fatigue limit with increasing overload in both cases. The fatigue limits increased by 25.8 % and 24.6 % for the horizontally and vertically built specimens, respectively. Extensive modelling and experiments confirmed that there was no significant alteration in the shape and size of the porosity before and after preloading. Therefore, the observed enhancement in fatigue performance was primarily attributed to the imposed local compressive residual stresses around the defects.

The transport sector is growing and so is the awareness of the environmental impact from fossil fuels. This calls for changes in how road transport is powered, driven by both rules and regulations and from customer and societal expectations. There are several technical solutions to reduce and finally replace the use of fossil fuels currently discussed both in academia and industry and those solutions are at different maturity levels. The aim of this research is to investigate how the introduction of new technologies effects the evolvement of the components in the powertrain. This knowledge will be valuable for the truck industry OEMs to support the transition of the production system to match future needs. This is done in two parts. First, a semi-structured interview with experts from the automotive industry was conducted, then a literature study. The research shows that several powertrain technologies will exist, optimized for different markets and applications. On a component level, effort will be made to reduce the losses in the powertrain and the strive for efficiency will lead to higher requirements on geometrical quality, tighter tolerances, and surface requirements.

Kinematic calibration improves the positioning accuracy of articulated industrial manipulators. Industrial manipulators with kinematic calibration are more likely to be successfully offline programmed. Almost all manufacturers offer this calibration service. The calibration provided by the robot manufacturer is a working space calibration, which means that it guarantees a positioning accuracy for all possible configurations. This approach seems reasonable for an unknown target operation and if only a single set of model parameters can be stored in the robot controller. However, nowadays robot controllers can store more than one set of model parameters to reflect the significantly different orientations in which industrial manipulators can be mounted, e.g., floor-, wall, or ceiling-mounted. Thus, this work investigates the variability of the mean and maximum positioning accuracy of industrial manipulators in four different operating space calibrations and compares them with the working space calibration provided by the robot manufacturer. The article concludes with a discussion about potential improvements of existing kinematic calibration procedures.

Electron beam melting (EBM) is a powder bed fusion (PBF) additive manufacturing (AM) process for metal powder printing with wide applications in key industrial sectors, including automotive, healthcare, aerospace, etc. The high-temperature processing of this technique extensively sinters the powders on the surfaces and creates a poor and coarse surface finish. Differences between the surfaces from EBM in comparison with other AM processes make it difficult to answer which measurement method, with what measurement settings, and which evaluation parameters should be used for surface characterization. In this work, the performance of various optical methods for the measurement of areal topography of rough EBM-made metal surfaces was investigated. A specially prepared artefact allowing for the generation of different angles was designed and produced from a nickel-based alloy using EBM without any supporting structure for down-facing surfaces. The as-built up-facing and down-facing surfaces from the artefacts were measured in orthogonal to the build direction. Measurement system capability for as-EBM surfaces is presented along with areal surface texture analysis.

This paper develops a hybrid experimental/simulation method for the first time to assess the thermal stresses generated during electron beam melting (EBM) at high temperatures. The bending and rupture of trusses supporting Inconel 625 alloy panels at similar to 1050 degrees C are experimentally measured for various scanning strategies. The generated thermal stresses and strains are thereafter simulated using the Finite-Element Method (FEM). It is shown that the thermal stresses on the trusses may reach the material UTS without causing failure. Failure is only reached after the part experiences a certain magnitude of plastic strain (similar to 0.33 +/- 0.01 here). As the most influential factor, the plastic strain increases with the scanning length. In addition, it is shown that continuous scanning is necessary since the interrupted chessboard strategy induces cracking at the overlapping regions. Therefore, the associated thermal deformation is to be minimized using a proper layer rotation according to the part length. Although this is similar to the literature reported for selective laser melting (SLM), the effect of scanning pattern is found to differ, as no significant difference in thermal stresses/strains is observed between bidirectional and unidirectional patterns from EBM.

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

In the turning process, the surface roughness of the machined part is considered a critical indicator of quality control. Provided the conventional offline quality measurement and control is time-consuming, with slow feedback and an intensive workforce, this paper presents an online monitoring and prediction system for the effective and precise prediction of surface roughness of the machined parts during the machining process. In this system, the audible sound signal captured through the microphone is employed to extract the features related to surface roughness prediction. However, owing to the nonlinear phenomena and complex mechanism causing surface quality in the whole process, the selection of statistical features of the sound signal in both the time and frequency domains varies from one case to another. This variation may lead to false prediction results as sufficient domain knowledge is required. Therefore, the versatile and knowledge-independent features extraction method is proposed, which exploits deep transfer learning to automatically extract sound signal features in the time-frequency domain through pre-trained convolution neural networks (pre-trained CNN). The performance of prediction models based on two feature extraction methods – statistical feature extraction and automatic feature extraction was further tested and validated in the case study. The results demonstrate that the performances of the prediction model built on the automatically extracted features outperformed that developed with the statistical feature method concerning the accuracy and generalization of the prediction model. In addition, this study also provides solid theoretical and experimental support for developing a more precise and robust online surface quality monitoring system.

The fine blanking process is often used in plastic working to produce both simple details and those with complex shapes. This process consists in separating the material with the use of a tool that causes pressure on the shaped material, creating an appropriate state of deformation in the cross-section of the sheet and the cutting zone. The main challenge in the production lines is to obtain high-quality products with the optimal shape of the cut edge. Electrical steels are usually supplied as very thin sheets ready to be cut and blanked for use in the construction of stators and rotors for the cores of electric motors or as laminates for shielding and winding transformers. The presence of high silicon content in electrical steels together with their low thickness makes them susceptible to the formation of serious cut edge defects, such as: concentration of excessive deformations and stresses in the vicinity of the cut edge, excessive burrs, edge cracks. This condition results in a reduction of the magnetic efficiency in the final products.  A new approach to the design of the fine blanking process was proposed by optimizing the geometry of the punch, allowing for the reduction of contact pressures in the cutting zone and cut edge defects of electrical steel. By using the optimal blanking clearance, it is also possible to reduce the deformation zone concentrated near the cut edge. The proposed approach allows for extending the work cycles of the punches. FEM combined with the updated Lagrange description was used to describe the phenomena at a typical incremental step. The states of strain and strain rate are described by non-linear relationships without linearization. The description of the nonlinearity of the material was made with an incremental model taking into account the influence of the history of strain and the strain rate. The condition of the material after the previous treatments was also taken into account by introducing the initial states: displacements, stresses, strains and their velocities. An optimization process was carried out to determine the process conditions that would ensure the highest quality of the cut edge and the greatest durability of the cutting tools. The research results may be useful for the design of modern blanking tools used in the processing of electrical materials.