Scrap Rate Research Articles

Insights into the digitalization-performance relationship: the role of flexibility and quality enhancing organizational practices

Although still in its early stages for the average manufacturing firm, the adoption of digital technologies has arrived in production systems. In response to these changes, firms are transforming their production systems and processes to accommodate this trend towards digitalization, constituting Industry 4.0. To make change effective, manufacturing firms are implementing advance production technologies and complement these with the necessary organizational changes in the production processes. Building on the premises of diffusion of innovation theory, the resource-based view and socio-technical systems theory, the present study examines whether the relation between the implementation of digital technologies and flexibility- and quality-enhancing organizational practices have a direct and moderating effect on the operational efficiency of manufacturing firms. The data come from a questionnaire-survey among 502 Spanish and Dutch organizations that responded to the European Manufacturing Survey in 2015 and 2018. Using a concatenated and multi-country dataset, the results reveal that both digitalization and organizational practices affect operational efficiency positively, but the impacts are mostly on different efficiencies, i.e. digitalization results in more efficiency regarding quality aspects (scrap rate, quality complaints), while organizational practices impact lead time and not-on-time-delivery efficiencies, and quality complaints. Both technologies and flexibility- and quality-enhancing organizational practices are seemingly needed, and sometimes even complement each other, but they do not provide synergy effects in combination. Last, early adoption of the digital technologies can help to further improve operational efficiencies.

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining.

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

Developing a concept for the implementation of predictive quality in battery production

The supply of batteries plays a crucial role in the electrification of the automotive industry and thereby in reducing CO2 emissions. With strict regulations forcing the ban of internal combustion engines and the reduction of emissions, the demand for batteries has risen drastically. In current battery productions, planned production volumes are in most cases not reached due to high scrap rates. Reasons for this lay in the lack of predictability of product quality due to process complexity and interdependencies both on the process level and on the parameter level. The reduction of scrap through data-driven methods in battery production offers the potential to increase production volumes addressing the global demand and to preserve energy and materials. Tackling the challenge of high scrap rates, existing and conventional methods in quality management lack the applicability in complex processes and the predictability of product quality. Predictive quality describes an industry 4.0 application and the approach to use production data to predict process and product related quality characteristics. In this paper, a concept for the technical implementation of predictive quality in battery production is developed. For this, an overarching concept is outlined and based on this the technical infrastructure described. The concept highlights the required data collection as well as data processing and addresses the development of prediction models. By applying the developed concept, the deployment of quality prediction models and the detection and compensation of scrap at an early stage is enabled.

DEPLOYING BUILT-IN QUALITY TO REDUCE SCRAP IN AN AUTOMOTIVE COMPONENT MANUFACTURER

Automotive component manufacturers face global competitive challenges, and the paradigm has shifted from product price as the determining factor of competitiveness to the quality of the product. An automotive component manufacturer was struggling to manage the outflow of defects, and adding inspectors as quality gates to the rear-step bumper production line had severe cost implications. This study aimed to reduce the number of defects by deploying a strategic path of implementing built-in quality. Quality tools were used, and the study’s results included significant manpower reduction, improved quality capability, and reduced scrap rates and reworks.

Energy-efficient scheduling for a flexible job shop problem considering rework processes and new job arrival

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

Electrochemical Impact of Inhomogeneous Binder Distribution in Lithium-Ion Battery Electrodes

In recent years, the application possibilities of the lithium-ion battery (LIB) have increased significantly. Moreover, the demand for LIBs will rise in the next few years, especially in the automotive sector. This leads to an increasing importance of careful management of the limited lithium resources. Battery production offers high savings potential, as the current scrap rate is around 5-30% [1,2]. The scrap arises in the various process steps such as electrode production, battery conditioning or even from impure raw materials. Especially during the electrode production including mixing, coating, drying and calendaring, various defects can be implemented into the electrodes. Defects such as compositional inhomogeneities (e.g., inhomogeneous binder distribution), microstructural inhomogeneities (e.g., thickness variations), line defects, point defects or even foreign particle contamination can lead to increased scrap rates [3].For the detection of defects, there are already a few established methods such as computed thermography, camera-based optical systems, or post-mortem analyses. However, the detection of the defects might not be sufficient to evaluate the electrode usability since the influence might still be tolerable. Therefore, the impact of the defect type, size, and concentration and resulting determination of the tolerance limits is decisive for the reduction of production waste.In this work we focus on the influence of compositional inhomogeneities on the battery performance. Compositional inhomogeneities like inhomogeneous binder distribution can modify the local electrode characteristics e. g. porosity/tortuosity. This can lead to local higher or lower c-rate followed by faster or slower local degradation. In worst case these inhomogeneities can cause lithium plating or short circuits and shorten the lifetime of LIBs significantly.To determine the criticality and establish tolerance limits, we produced and analyzed defective and defect-free electrodes. For the determination of the impact of the inhomogeneities we developed a special experimental setup that allows us the analysis of the internal dynamics of the interaction between defect-containing (selective modification of the binder content) and defect-free (target binder content) electrodes. Therein, compensating currents continuously redistribute the inhomogeneous charges in electrodes with different binder content. This balancing effect was studied in terms of the caused accelerated aging.This experimental approach provides a foundation for further investigation and for deep understanding of the effect of electrode defects. This knowledge represents a basis for further developing of effective quality assurance strategies and reducing scrap rates accompanying by careful management of the limited lithium resources.[1] Gaines, L.; Dai, Q.; Vaughey, J. T.; Gillard, S.: Direct Recycling R&D at the ReCell Center. Recycling. 2021, 6 (2), 31. DOI: 10.3390/recycling6020031. [2] Brückner, L.; Frank, J.; Elwert, T.: Industrial Recycling of Lithium-Ion Batteries—A Critical Review of Metallurgical Process Routes. Metals 2020, 10 (8), 1107. DOI: 10.3390/met10081107. [3] du Baret de Limé, A.; Lein, T., Maletti, S.; Schmal, K.; Reuber, S.; Heubner, C. and Michaelis, A.: Impact of Electrode Defects on Battery Cell Performance: A Mini Review. Batteries & Supercaps 2022, 5, e202200239, doi.org/10.1002/batt.202200239.

Direct recycling of lithium-ion battery production scrap – Solvent-based recovery and reuse of anode and cathode coating materials

The rapid growth in the use of lithium-ion batteries is leading to an increase in the number of battery cell factories around the world associated with significant production scrap rates. Direct recycling of this scrap material has both environmental and economic benefits, such as reducing the carbon footprint of cell manufacturing, as well as reducing production costs and raw material requirements. In this work, a solvent-based direct recycling route for anode and cathode coating materials is presented that allows direct reuse of the recovered coating materials. A high yield of recovery is achieved by mechanical stress in a suitable solvent (96 % for anodes, 85 % for cathodes). The resulting suspension is used for electrode production to study the influence of direct recycled material on the resulting electrode and cell performance. It can be seen that the electrodes and cells with up to 10 % recyclate content have similar high performance in terms of electrical conductivities, initial discharge capacity (approx. 165 mAh g−1 for graphite/NMC622 coin cells) and C-rate stability. Overall, the work demonstrates that direct solvent-based recycling is an efficient method for recycling electrode scrap without compromising the performance of the cells produced when fresh material is added to the recycled suspension.

Towards a Circular Economy of Lithium-Ion Batteries – Is a Fast Ramp-up of Recycling and Circularity Sustainable?

With the rapidly growing demand for lithium-ion batteries (LIBs) in electric vehicles (EV) major global challenges for policymakers and companies arise. Global production and supply chains of battery materials increasingly account for environmental issues accompanied by social problems of raw material mining (e.g., for cobalt in DR Congo) and recent highly fluctuating raw materials prices. To face these problems, policymakers and companies are pushing for circular economy strategies for battery supply chains to guarantee a sustainable supply of critical raw materials such as lithium, cobalt, and nickel, and to integrate used batteries back into the value chain by recycling or reuse. But reaching a full circular economy early does not translate into improved sustainability at the same time. A circular economy of batteries in EVs is fully developed once the demand for raw materials in EV batteries is completely covered by recycled materials, the break-even points (BEPs) of a circular economy. At this stage, regional independence from primary raw materials with critical production and supply chains is achieved.In this study, a critical perspective on the pushing for strategies to reach circular economy of EV batteries early is taken. Within three steps it is shown, (1) when the circular economy is realized on a regional level by reaching BEPs, (2) how to accelerate reaching these individual BEPs for battery materials through different scenarios, and (3) what this acceleration scenarios implies in terms of a sustainable global production of battery materials via mining and transportation compared to reuse and recycling.In the first step, this numerical analysis focuses on identifying the BEPs of lithium, cobalt, and nickel for EV batteries in key regions, namely Europe, the US and China, by using a dynamic material flow analysis (MFA). For this purpose, the life cycle of a battery is considered and analyzed in a circular economy approach via production, EV life, secondary application (re-use) and recycling. The results show that China is the first region to achieve BEPs for Co and Ni in the 2040s while focusing on LFP as the main battery technology in the upcoming decades, followed by Europe and the U.S. Compared to China, EV battery technologies in Europe and the USA are dominated by NMC and NCA leading to later BEPs for cobalt and nickel. Generally, cobalt will be the first metal to achieve BEPs, while the secondary coverage of lithium poses a major future global challenge.Second, based on a sensitivity analysis of the model parameters with the highest impact on the BEPs, different scenarios of parameter adaptions are presented which lead to potential earlier achievement of the BEPs in Europe, the USA and China (shift scenarios). These scenarios include early full electrification of car sales, no secondary application of EV batteries, reduction of EV battery sizes in time, shorter lifespan of batteries in EVs and high production scrap rates. The results show that each of the five shift scenarios has a different impact on accelerating reaching the BEPs in each region for the individual raw materials.Third, in each shift scenario, the necessary mining and production amount of primary EV battery materials until reaching the BEPs are analyzed. Although achieving BEPs earlier by applying the shift scenarios seems beneficial, the results indicate that more primary raw materials must be produced annually until reaching the BEP under certain conditions, suggesting that more social and environmental problems linked to raw material mining and transportation would have to be tolerated. Therefore, a sustainable slow ramp-up of mines and recycling must be weighted individually against a fast ramp-up and, thus, against reaching a full circular economy early.This analysis and the model in general can help policymakers and companies to strategically decide whether and how to accelerate achieving certain BEPs for an early independence from primary raw materials. However, by taking a critical perspective here, it is shown that accelerating the transition towards circular economy by reuse and recycling can be accompanied by intensifying temporary sustainable issues of raw material mining and transportation.

Service and inventory model for maintenance workshop in the short cycle operation region: Agent-based simulation approach

Tools used in the North Sea region has a high cycle and come very often to maintenance workshop to be checked, repaired if needed and prepared for the next operations. The availability of spare parts at the workshop plays a significant role to keep the flow time as short as possible and meet such high-cycle operations. Supplying spare parts from the best-cost countries to the North Sea region with about one-year lead time makes the situation more critical and the economic order quantity and reorder point need to be found. However, having inventory at the workshop and ordering a batch of spare parts increases operational expenditures. Moreover, frequent supplies increase the environmental impacts of shipping CO2 and spare scrap rates, whereas repairing the used spare parts and reusing them again can offer a more sustainable solution. Therefore, the purpose of this paper is to develop a simulation model that can quantify the cost and benefits of reusing repaired spares compared to supplying newly built spares from the best-cost countries. To achieve this purpose, a case study has been implemented on a specific maintenance workshop within the North Sea region and the entire tool repair and spare part supply operations are conceptualized and modelled with the help of the simulation modelling approach. Two scenarios have been simulated: (1) the maintenance workshop fully depends on supplying newly built spares from the best-cost countries with no inventory stock or spare reuse, and (2) the Maintenance workshop is primarily dependent on repaired spares with an optimal level of spares stock. The simulation results, from the studied case, support the second scenario where a repair path cycle is introduced to the maintenance workshop, as a 78% reduction in lead times, a 116% improvement in worker utilization, a 73% reduction in crowding levels, a 52% reduction in scrap rate, and a potential profit increase of roughly three million NOK (20%). Therefore, it can be concluded that a local repair service is required to keep maintenance workshops in high-cycle regions at high-performance levels.

Historical and prospective lithium-ion battery cost trajectories from a bottom-up production modeling perspective

Lithium-ion batteries (LiBs) are pivotal in the shift towards electric mobility, having seen an 85 % reduction in production costs over the past decade. However, achieving even more significant cost reductions is vital to making battery electric vehicles (BEVs) widespread and competitive with internal combustion engine vehicles (ICEVs). Recent trends indicate a slowdown, including a slight cost increase in LiBs in 2022. This study employs a high-resolution bottom-up cost model, incorporating factors such as manufacturing innovations, material price fluctuations, and cell performance improvements to analyze historical and projected LiB cost trajectories. Our research predicts potential cost reductions of 43.5 % to 52.5 % by the end of this decade compared to 2020. Furthermore, reaching cost parity between BEVs and ICEVs is expected in the latter half of this decade, contingent on a total installed capacity of 3500 to 4100 GWh.year−1 across giga-factories. The determinants influencing these cost reductions evolve over time, with historical significance placed on savings in cathode materials and anticipated future importance on minimizing scrap rates. This research offers valuable insights for policymakers and investors in the LiB industry, aiding informed decision-making.

Recycling channel design and coordination in a reverse supply chain with customer green preference

This study investigates the selection of recycling mode and the coordination mechanism in a reverse supply chain (RSC) that consists of one remanufacturer, one traditional recycler, and one smart recycler. We characterize the collection cost differences between traditional recycling and smart recycling, and the scrap rate of used products by smart recycling. We consider three recycling modes with recycling pricing competition, conditional on whether the remanufacturer collaborates with the traditional recycler, with the smart recycler, or with both recyclers. We show that the optimal recycling channel strategy for the remanufacturer is to enroll both the traditional recycler and the smart recycler, and the competition between these two recyclers exerts some non-trial implications on the firms’ equilibrium pricing strategy and profitability. By comparing the equilibrium outcomes with a centralized system, we further develop two coordination contracts: a two-part tariff contract and a transfer price combined with a profit-sharing contract, both of which can effectively mitigate double marginalization and improve the supply chain’s overall performance. Our findings provide useful guidance for the design of recycling channels, and for the coordination of RSC members to achieve Pareto improvement.

Induction heating as a pre-treatment for the recycling of Li-ion battery cathodes – Technical feasibility

Production capacities for lithium-ion batteries are being expanded globally to meet the rising demand for energy storage applications. However, high scrap rates represent a key challenge for the production of lithium-ion batteries, especially for electrode manufacturing. The recycling of cathode production scraps that occur as intermediate products in electrode manufacturing offers a good possibility to reuse valuable battery active materials and close the material cycle within the production line. In this work we develop a novel method based on electromagnetic induction to separate cathode coating and aluminum foil. A cathode coil rejected from production was cut into sheets which were inductively heated to 300, 400 and 550 °C. The thermally pre-treated sheets were stressed in a cutting mill. With this method the coating could be separated from the foil up to 99.12 %. The aluminum impurities in the recycled fine powder fraction could be kept at a low level of 0.27 wt.-% at maximum. Agglomerates in the powder fraction could be significantly reduced through the thermal pre-treatment. This work shows the feasibility of induction heating as an energy-efficient and flexible thermal pre-treatment for the recycling of roll-to-roll cathode materials.

Automated defect detection on inductive thermography images using supervised and semi-supervised Deep Learning methods

Inductive infrared thermography has been proven as an interesting solution for the inspection of surface defects. To automate the inspection, defect detection methods based on convolutional neural network proved their efficiency for complex detection tasks compared to traditional methods. Both supervised and semi-supervised learning approaches have been proposed for the inspection task. While the supervised approach remains the most common one, it requires images of both defective and non-defective parts during the training phase. Unfortunately, in many industries where the scrap rate is low, acquiring images of defective parts is difficult and requires time which can delay the deployment of such solutions. This paper compares these two learning approaches by illustrating the advantages and disadvantages of each approach from an industrial point of view. In conclusion, we describe an inspection deployment strategy, which combines the two approaches to ensure robust inspection with rapid deployment.

(Invited) Accelerating Battery Manufacturing Optimization by Combining Process Modeling with Machine Learning

The manufacturing process of lithium ion battery (LIB) electrodes impact their architecture and practical properties, such as their energy and power densities, their durability and safety. Therefore, it is very important to optimize this manufacturing process in a proper manner. Such an optimization is highly complex because of the very significant amount of parameters and numerous interdependencies between the process steps, such as the slurry mixing, the casting, the drying, the calendering, the assembly, the electrolyte filling and the solid electrolyte interphase formation. As a consequence, traditional trial and error approaches can lead to high scrap rates in LIB cell manufacturing (as high as 30 % in prototyping activities).1 In this lecture I present a computational infrastructure able to optimize the LIB manufacturing process. Such infrastructure, called ARTISTIC,2 is supported on multiscale physics-based modeling and machine learning. Mixing, casting and drying stages are simulated by using a 3D-resolved Coarse Grain Molecular Dynamics approach, while the calendering is simulated by using a 3D-resolved Discrete Element Method. The electrolyte filling is simulated by using a 3D-resolved Lattice Boltzmann Method and the formation stage is simulated by using a 3D-resolved Finite Element Method (FEM)-based model. These physics-based models are sequentially linked in a way that the outputs of one simulated process step goes as inputs for the following one. In such a way, the interdependencies between process steps can be captured. At the end of this workflow, the 3D-FEM-based model is used to simulate the electrochemical and transport processes in the electrodes and capture the influence of manufacturing parameters on the spatiotemporal heterogeneities of lithiation/delithiation. Machine learning models, particle swarm optimization and Bayesian optimization are used to speed up the parameterization of these different models. Machine learning is also used to derive surrogate models implemented in a Bayesian optimizer predicting which manufacturing parameters to adopt in order to maximize/minimize different electrode properties.All this modeling work is supported in a 1-to-1 comparison with experimental data acquired in our battery manufacturing pilot line. The optimization capabilities of our computational infrastructure is demonstrated for LIBs with electrodes with different active material chemistries (NMC111, NMC622, LFP, graphite, silicon-graphite blends), for Sodium Ion and Solid State Batteries. Illustrative examples are provided during my lecture.3 I also discuss the ARTISTIC Online Calculator,4 a free service offered by the project to perform battery manufacturing simulations from an internet browser. Finally, I demonstrate our Virtual Reality-based digital twin of our battery manufacturing pilot line5 and how it is being used by us to train the next generation researchers and pilot line operators.

Thermomechanical properties of 3D-printed sand moulds using inorganic binder

Additive Manufacturing of 3D-printed sand cores using the binder jetting process is well-established in prototype manufacturing. Due to the rising focus on sustainability and the fact that printed cores are shifting to serial production, a transition from organic to inorganic binder systems is taking place. To ensure a stable casting process and reduce the scrap rate accurate simulation tools are required. However, a study about the thermomechanical properties and the anisotropy of 3D-printed sand cores has not yet been conducted. In this work the thermomechanical properties of 3D-printed sand cores in three different printing orientations using inorganic binder are given. In contrast to homogeneous materials like metals, the simulation of sand cores requires new material models due to the dependency to hydrostatic pressure. The Drucker-Prager soil plasticity model is used, and the parameters needed for the Drucker-Prager-Cap model until 500°C are analysed using the three-point-bending test, the indirect tensile test and the uniaxial compression test. In addition to these specific parameters, also general parameters required for mechanical simulation like the Young's modulus, the Poisson's ratio, the density and the thermal expansion coefficient are given. In comparison to the reference binder system for shot cores using inorganic binder, the 3D-printed cores showed a higher mechanical strength. In the tensile region due to the higher binder content and in the compressive region due to the higher AFS number. Furthermore, the binder system for printed cores showed a lower thermostability.

How can automation increase production efficiency and reduce scrap rates?

In the modern understanding, automation of business processes is the use of computers, networks, high-tech manipulators, equipment and systems based on artificial intelligence and machine vision in an enterprise. First of all, automation of business processes is the simplification of the work of employees of the enterprise, the replacement of manual or human labor with machine labor, in order to improve and improve the quality indicators of manufactured products. In the age of digitalization, there are still quite a lot of processes in companies and industries that remain non-automated. Quite a lot of processes depend on a person, there are no objective means of continuous accounting, measurement and quality control of products. How to automate industries in which manual quality control of manufactured products was until today the most common way of working, gradually improving processes and releasing even greater production potential, and what economic effect can be obtained, is presented in this material.

SecFL – Secure Federated Learning Framework for predicting defects in sheet metal forming under variability

With the ongoing digitization of the manufacturing industry and the ability to bring together data from specific manufacturing processes, there is enormous potential to use machine learning (ML) techniques to improve such processes. In this context, the competitive automotive industry can take advantage of the ML power by predicting defects before they occur, aiming to reduce the scrap rate and increase the robustness and reliability of the production processes. In a real world scenario, small and medium size companies do not have the amount of data the big companies have, which can prevent the usage of ML models in this vital niche for the industry. A collaboration in terms of data usage to develop powerful and general industry solutions is hindered by data privacy concerns despite similar problems. This paper addresses these concerns by providing a framework based on the Federated Learning (FL) method combined with Digital Envelopes (DE) to allow the ML models training while keeping the data of the partners and the models parameters private and protected against external cyber-attacks, which is one of the weaknesses of FL as of now. A case study was carried out to demonstrate the effectiveness of the proposed framework on handling data poisoning attacks to the training data and also the models’ weights.

Framework for enhancing the operational resilience of cyber-manufacturing systems against cyber-attacks

Cyber-manufacturing systems (CMS) are interconnected production environments composed of complex and networked cyber-physical systems (CPS) that can be instantiated across one or many locations. While it offers enhanced productivity and efficiency than traditional manufacturing, its inherent properties open the door for new cybersecurity vulnerabilities. Defense mechanisms need to be implemented to prevent, detect, and recover from cyber-attacks. This research aims to contribute to the design of more resilient cyber-manufacturing systems. Operational resilience is a system’s ability to withstand cyber-attacks, faults, and failures and continue to operate in a degraded state to carry out its mission. Thus, an operational resilient CMS is capable of withstanding disruptions arising from cyber-attacks while maintaining availability, utilization efficiency, and a quality ratio above degradation thresholds until recovery. This work proposes a novel framework to enhance the operational resilience of CMS against cyber-attacks. The framework consists of four steps: (1) Identify: map CMS production goals, vulnerabilities, and resilience-enhancing mechanisms; (2) Establish: set targets of performance in production output, scrap rate, and downtime at different states; (3) Select: determine which mechanisms are needed and their triggering strategy, and (4) Deploy: integrate into the operation of the CMS the selected mechanisms, threat severity evaluation, and activation strategy.

A Test Stand for Quantifying the Core Gas Release and the Gas Permeability of Inorganically-Bound Foundry Cores

Environmental and work safety aspects necessitate a radical change in the foundry industry. Organic binder systems for foundry sand cores create toxic combustion products and are, therefore, more and more often substituted by inorganic binder systems. While providing an environmental advantage by mainly releasing water vapor, inorganic binder systems impose new challenges for the casting process. The gas release of inorganically-bound foundry cores can lead to increased gas porosity in the cast parts and thus to high scrap rates. The present work aims to gain more understanding of the gas generation and transport in inorganic sand binder systems. We developed a test stand to measure the temperature-dependent core gas release in inorganically-bound foundry cores and their gas permeability. Samples were prepared in a core blowing process and analyzed using the test stand. The measurement results are in good agreement with validation experiments and existing literature.

Metabolic Process Modeling of Metal Resources Based on System Dynamics—A Case Study for Steel in Mainland China

Rapid urbanization has promoted the development in human production and living standards, and the metabolic rhythm of metal resources has accelerated. Grasping the metabolic processes of metal resources and predicting their future development trends can help the country refine the formulation and adaptability of metal resource production and the recycling strategies for sustainable development. In this study, from the perspective of the entire life cycle of steel resources as an example, a system dynamics-based metal resource metabolism prediction model was established to predict the steel resources in the three stages, including production, in-use and end-of-life recycling. The trend in changes in steel resources production, in-use stock, end-of-life and recycling in mainland China from 1990 to 2020 were also analyzed. The results show that the volume of all stages of steel resource metabolism in mainland China from 1990 to 2020 has shown an increasing trend, and will reach a peak around 2040 and then remain stable. The steel resources in all the metabolism stages in mainland China were predominantly distributed in buildings. In mainland China, steel resource production efficiency reached above 0.9 and the steel resource outflow rate was 0.079, within which the domestic scrap rate reached 0.022 and the recycling rate reached above 0.8.