Scrap Rate Research Articles

A Hybrid Modelling Approach Coupling Physics-based Simulation and Deep Learning for Battery Electrode Manufacturing Simulations

Lithium-ion battery (LIB) performance is significantly influenced by its manufacturing process. Manufacturing of an optimized electrode can incur high production costs such as high energy consumption, high scrap rates and emissions. This is due to the process that consists of a series of manufacturing steps presenting a complex interrelationship, thus limiting the understanding of performance as a function of manufacturing parameters. While several empirical and computational methods are employed for optimization, they are demanding in terms of resources such as materials or computational effort. By leveraging Deep Learning (DL), we can enhance our understanding of the complex manufacturing processes and accelerate its optimization. We propose a data-driven supervised DL methodology to complement physics-based LIB cathode manufacturing simulations. The trained DL-based predictive model integrates well into the manufacturing simulation framework to forecast cathode slurry microstructures. The DL model demonstrates robust predictive performance for LIB NMC-111 and LiFePO4–based slurries and slurries for a solid-state battery NMC-622/argyrodite composite electrode preparation. While the current work is focused on the cathode slurry process, the proposed methodology has potential for application to drying and calendering steps. This approach will be helpful in streamlining lab-scale electrode manufacturing, and reducing errors, waste and resource consumption.

Novel approach for in-line process monitoring during ultrasonic metal welding of dissimilar wire/terminal joints based on the thermoelectric effect

AbstractUltrasonic metal welding (USMW) is a manufacturing technique widely employed in the automotive and aerospace industries due to its efficiency in joining similar as well as dissimilar metals. Despite its prevalence, the lack of effective in-line process monitoring methods has resulted in high scrap rates, product recalls due to unrecognized scrap or financial losses due to pseudo-scrap, limiting its application in more sensitive industries. This paper presents a novel thermoelectric effect-based method for in-line process monitoring of USMW processes. This approach utilizes the thermoelectric properties, that manifest at the junctions of dissimilar metals during welding to accurately measure the temperature of the weld zone without the need of additional thermocouples, pyrometers or infrared cameras. An experimental setup was developed to validate the thermoelectric-based temperature measurement methodology. Key to this approach is the detection of thermoelectric voltage developed due to thermo diffusion when dissimilar materials are joined. The experiments showed a strong correlation between the thermoelectric voltage and the mechanical strength of the welds, suggesting that this parameter can effectively predict the quality of the weld. In the trials, a series of welded samples was created under controlled conditions to measure the generated thermoelectric voltage and correlate it with ultimate tensile strength tests. The data were analyzed using Spearman’s correlation coefficients to determine the correlation of the thermoelectric signals and joint strength. Results indicate that the thermoelectric voltage measurements correlate highly with the joint strength, with a Spearman’s correlation coefficient of over 0.94, thereby providing a promising predictive metric for assessing weld quality.

Implementation of a performance management system for environmental sustainability in an industrial organization

Abstract The paper explores the implementation of process performance management systems in the automotive industry, specifically focusing on improving the assembly line of an organization within this sector thanks to the introduction of cobots in the line of manufacturing electronic modules used in car seat control systems. With an emphasis on environmental sustainability and circular economy (CE) concepts, the study investigates the positive effects of optimizing mechanical and electromechanical systems within the assembly line. Leveraging concepts such as KAIZEN and Gemba Kaizen, along with the guiding principles of Just-In-Time (JIT), Continuous Improvement and Automation with a Human Touch (JIDOKA), the research aims to elucidate the potential benefits of such enhancements. Key performance indicators (KPIs) including Overall Equipment Effectiveness (OEE), First Pass Yield (FPY), and scrap rates were considered. Rejects, primarily stemming from human error in part handling, emerged as the major challenge facing this assembly line. To address this issue, collaborative robots were introduced to automate part handling, replacing manual processes with precise and reliable automation. Before the implementation, the cycle time stood at 13.6 units, which was reduced to 12.5 units post-implementation, resulting in a significant increase in productivity equating to 3000 more parts produced per week. OEE increasing from 80% to 87.74%, FPY rising from 96.42% to 98.48%, and the scrap rate decreasing from 0.06% to 0.02%. By addressing inefficiencies at the assembly line level, organizations can achieve significant improvements in performance metrics while concurrently contributing to a greener and more sustainable future.

Comparative study of the strain and strain rate hardening mechanisms and its workability during hot deformation of TC4 titanium alloys

Localized thinning is one of the main defects in hot forming processes, which is determined by the hardening behaviour during deformation. This study focused on building a hot-workability map to prevent localized thinning in wide ranges (650–950 °C & 0.001–1 s−1) for industrial significance. Insights into the associated strain and strain rate hardening mechanisms were provided by visualizing the microstructure evolutions using both in-situ High-Temperature Laser Scanning Confocal Microscope (HTLSCM) and Electron Backscattered Diffraction (EBSD). Results show that the highest uniform strain, up to 0.250, is achieved in the viscoplastic region due to the cooperative strain and strain rate hardening, where dislocation slip, α/β transformation and grain sliding concurrently occur. Significant grain refinement is also achieved. The optimum forming condition was identified in the viscoplastic region and is further verified by forming an ultra-thin large bend component, reducing thickness unevenness from 18.6 % to 13.1 %. This research finding could aid in understanding the effects of strain and strain rate hardening on forming uniformity and further provide scientific guidance for industrial processing parameters selection, reducing localized thinning and hence scrap rates.

An efficient detector for detecting surface defects on cold-rolled steel strips

Surface-defect inspection is vital in cold-rolled steel-strip manufacturing, given the complexities of production environments and the high speeds involved. Further, the defects on cold-rolled steel strips are often characterized by their small size, diversity of types, and similarities among different types, posing significant challenges in balancing detection accuracy and efficiency. To address the challenges, we designed a detector based on You Only Look Once version 5 (YOLOv5) to achieve precise detection of surface defects on cold-rolled steel strips. First, a dataset containing seven types of defects was curated, named the Cold-Rolled Steel Defect Dataset (CR7-DET). Next, a feature-extraction network based on residual-like connections within a single residual block (Res2net) was developed to enhance the model’s feature-extraction capability, alongside introducing a multi-head attention module to focus on key information features. To reduce the information loss during feature fusion, we established an adaptive feature-fusion Path Aggregation Network (aff-PAN), which was optimized by designing a lightweight adaptive down-sampling module (LAD) to increase the sensory-field implementation of feature fusion. The ghost convolution effectively reduced the number of parameters and increased the speed without affecting the model’s performance. Finally, experiments were conducted on our CR7-DET and a public dataset (GC10-DET). With a reduced parameter count of 6.85 million, our model achieved a mean average precision(mAP) of 87.6% on CR7-DET and 79.7% on GC10-DET. The experimental results demonstrated that our model achieved a balance between detection accuracy and inference efficiency. The model has the potential to reduce scrap rates caused by defects and improve the overall surface quality of cold-rolled steel strips.

MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Production Optimization of Premium Food Can with Distortion Printing under Waving Requirement

This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate.

Live fitting of process data within digital twins of manufacturing to use simulation and optimisation

ABSTRACT In production scenarios, uncertainty in production times, and scrap rates is common. Uncertainty can be described by stochastic models that need continuous updates due to changing conditions. This paper models probability distributions and estimates their parameters from real-world data on processing times and scrap rates. It uses live fitting to identify timely changes in the data sets, comparing different distributions. The fitting and live fitting approaches are applied to data from a real production system to compare the goodness of fit for different distributions and to demonstrate the reliability of the reaction to changes in the input data. Data and probability estimations are categorised, and a concept combining simulation and optimisation models is developed to optimise scheduling. The vision of the research presented in this paper is an online approach that determines quasi-optimal schedules for production systems based on current data from the system and its environment.

Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard

AbstractIncreasing 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.

Assessing Process Control in the Foundry

Relying on scrap rates alone to assess process control in the foundry can be deceiving. Depending on the inspection method used, it is possible to sort grossly non-conforming parts, while still allowing a wide window of acceptance, masking a process that is out of control. Modern methods of acoustic resonance testing (ART) allow for detailed data logging of both part weight and resonant frequency peak data. Monitoring these two factors at the lot level can give foundry managers insight into how consistent their process is both within a single date code and across multiple date codes. The information collected during quality testing can and should be combined with statistical information gathered at the front of the foundry (design, tooling, molding, inoculation, pouring, cooling, and shakeout) and used to adjust the process to achieve smaller data spreads and more consistent and repeatable process control. Once a process is deemed to be under control, consideration should be given to monitoring the defined specification limits and subsequent control limits using statistical trend analysis as a means for ensuring a process is not approaching a condition in which the opportunity for quality issues can arise.

Research on Precision Control and Optimization of Automatic Feeding Device of Stamping Die

This paper discusses the precision control and optimization method of automatic feeding device of stamping die. By analyzing the accuracy of the existing feeding device, the main influencing factors are identified. To solve these problems, a comprehensive scheme of structural design optimization, control system improvement and precision test method optimization is proposed. The specific research contents include the improvement of the mechanical structure of the feeding device, the application of advanced algorithms in the control system, and the improvement of precision testing equipment and methods. Through experimental verification, the optimized feeding device has been significantly improved in precision and efficiency, effectively reducing the scrap rate in production, improving the overall production efficiency and product quality, and providing important technical support for precision manufacturing in industrial production.

Electrical Smoothing of the Powder Bed Surface in Laser-Based Powder Bed Fusion of Metals

Achieving a homogeneous and uniform powder bed surface as well as a defined, uniform layer thickness is crucial for achieving reproducible component properties that meet requirements when powder bed fusion of metals with a laser beam. The existing recoating processes cause wear of the recoater blade due to protruded, melted obstacles, which affects the powder bed surface quality locally. Impairments to the powder bed surface quality have a negative effect on the resulting component properties such as surface quality and relative density. This can lead either to scrapped components or to additional work steps such as surface reworking. In this work, an electric smoother is presented with which a wear-free and contactless smoothing of the powder bed can be realized. The achievable powder bed surface quality was analyzed using optical profilometry. It was found that the electric smoother can compensate for impairments in the powder bed surface and achieve a reproducible surface quality of the powder bed regardless of the initial extent of the impairments. Consequently, the electric smoother offers a promising opportunity to reduce the scrap rate in PBF-LB/M and to increase component quality.

Hot Corrosion Damage Modeling in Aeroengines Based on Performance and Flight Mission

Hot corrosion is a form of chemical damage that causes surface degradation, sound material loss, and reduced component life. A lifing analysis in aeroengines without considering hot corrosion can lead to unexpected damage findings and increased scrap rates due to blade thickness loss beyond repair. This paper presents a novel methodology to predict hot corrosion damage based on aeroengine performance and flight mission analysis while taking into account environmental exposure, fuel quality, and material factors. The participating mechanisms, from salt and sulfur ingestion to deposition and hot corrosion attack, are discussed to explain the phenomenon in aeroengine components. In the investigated engine type, the first stage of the low-pressure turbine is the most affected. The application of the new methodology provides insights into the damage progression during the flight, the most affected components and the importance of capturing variations in the fuel quality, environmental exposure at the flight region, and the thrust derate policy. For a representative 1500 n mile mission, the variations in environmental exposure, fuel quality, and derate policy within typical limits can result in up to +350% damage. The outputs of the new framework can inform the decision making for maintenance, repair, and overhaul contract costing and scheduling.

A quantitative framework to support the decision between traditional, selective, and hybrid assembly

High-technological products often consist of several components, each with varying feature values that are critical to the assembly’s quality. As traditional manufacturing involves random selection of components, strict tolerance ranges are typically set to limit their feature variability, increasing scrap rates and lowering material efficiency. By smartly combining components based on their measured features, selective and hybrid assembly allow for more relaxed tolerances and more efficient use of raw materials without affecting the final product’s quality. However, the increased logistical complexity involves additional operational costs, complicating the choice for the best strategy.This study presents a decision-making model for selecting from traditional, selective, and hybrid assembly. The model is formulated by first defining the cost functions for the corresponding expected assembly cost, and then analytically determining the optimal choice boundaries between the three methods based on the involved process parameters. Estimation methods for the instance-specific process parameters are then discussed before validating the model with a fully elaborated case study.Results confirm the need for a quantitative framework to support an informed choice as this strongly depends on the involved parameter values. This study aims to pave the way towards more sustainable manufacturing by supporting practitioners to identify the conditions whereby intelligent assembly methods, in addition to allowing for scrap reduction and increased material efficiency, also represent the most cost-effective option.

Application study of surgical instruments information management system in sports medicine specialty

The management of surgical instruments is related to the safety and efficiency of surgical operations, and a surgical instruments information management system (SIIMS) has been developed. The aim of the current study is to explore the application value of the SIIMS in sports medicine specialty. A set of self-developed SIIMS for sports medicine surgeries was applied to the study. The application value of the SIIMS was verified by comparing the safety and efficiency of instrument manipulation before and after its application, with instrument accidents, instrument repair rate, instrument scrap rate and instrument use efficiency as indicators. Through the application of the SIIMS, the incidence of surgical instrument accidents decreased from 3.7 times to 1.8 times (P = 0.02), the number of instrument repair decreased from 7.7 times to 2.9 times (P = 0.00), and the number of scrapped instruments decreased from 5.1 to 2.3 (P = 0.03), when referred to per thousand operations. Before and after the application of the SIIMS, the average instrument use efficiency was 74.0% ± 3.3% and 88.2% ± 4.4%, respectively, with statistically significant difference (P = 0.00). The application of the SIIMS in sports medicine specialty is helpful to the fine management of surgical instruments, improve surgical safety and instrument use efficiency.

Fuzzy Radar Evaluation Chart for Improving Machining Quality of Components

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer’s process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company’s past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Implementation of Frugal Innovation Approach in Moroccan Industry through the Development of a New Frugal System

To keep pace with economic growth in an environment marked by globalization, competition, and the significant magnitude of market requirements, companies must be dynamic to adapt quickly to new demands. This dynamism must be achieved through frugal projects aimed at achieving better performance with fewer resources. In line with this vision, we have proposed a new frugal innovation approach to develop a new concept of machines to meet the requirements of Smart Automation, which is the main element of the transformation plan defined by our company as a strategy for the coming years. This strategy aligns with the direction set by Morocco for the second generation of the Industrial Acceleration Plan 2021–2025. This work serves to introduce the objectives of the second-generation Industrial Acceleration Plan in Morocco, describe a company transformation strategy with an emphasis on the Smart Automation component and its context, explain frugal innovation applications within our organization and their outcomes, and outline the project and frugal innovation approach perspectives in Morocco. The results demonstrate the effectiveness of the newly proposed and implemented concept of frugal innovation, illustrated in a cost reduction of 59.7% compared with the manual process, of less than 56.3% compared with the existing semi-automatic process, and of less than 36.8% compared with the fully automatic process and in the improvement of quality by a reduction in customer complaints of 72.7% and in the scrap rate percentage of 0.2%. This has been achieved by providing cost-effective solutions in our competitive automotive environment. These results reinforce the importance of the approach in its global application.

Freckle prediction model incorporating geometrical effects for Ni-based single-crystal superalloy components

A freckle is a grain defect frequently found in Ni-based single-crystal (SC) superalloy castings resulting in a high scrap rate. A precise prediction of the defect before production is of great importance, especially for the complex-shaped blades and vanes. Without reckoning geometrical effects, the state-of-the-art Rayleigh number (Ra) model fails to accurately forecast the freckle formation positions. In this study, a novel Ra-based mathematical model was proposed. It not only shortened the calculation period and eliminated the physically meaningless at the singularity of the existing model, but also quantified the geometrical effects. Using the model, the Ra at every position of SC castings was calculated, which can be used to evaluate the freckling tendency of the whole casting and locate the specific positions of the freckling risk zone. A freckle-free approximate critical Rayleigh number about 1.5 was determined. A highly consistent and accurate prediction of this model was validated by comparing it with the experiments using complex-shaped SC castings, which suggests its great prospects for industrial application.

Analysis of longitudinal wrinkle formation during calendering of NMC811 cathodes under variation of different process parameters

The requirements for the lithium-ion battery (LIB) are constantly increasing. In the nearer future, batteries need to be even more powerful, safer and cheaper. One lever is the optimization of the production in order to minimize production scrap rates. In electrode manufacturing, calendering is an essential process step to adjust the volumetric energy density. However, this process leads to undesirable defects that result in production scrap. In this paper, the formation of longitudinal wrinkles is analyzed using a statistical experiment design. Electrode density, web tension and temperature are varied in two levels during calendering and are examined for their significance with regard to the geometry of the longitudinal wrinkles. Furthermore, the strain and the deformation of the electrode are analyzed. A complex interaction of material and process is revealed with respect to the formation of longitudinal wrinkles. A better understanding of these interactions contributes to optimize the calendering process and to find a solution to avoid longitudinal wrinkles.