Product Qualification Research Articles

Mission reliability modeling based on extended RQR chain considering process scheme robustness for multistate manufacturing systems

Process scheme robustness is the core of the “Method” in 5M1E (Man, Machine, Material, Method, Measurement, and Environment), which is the foundation of manufacturing quality and reliability assurance. However, previous studies frequently overlooked the significant impact of the coupling between the process scheme and process parameter robustness on the formation and degradation of mission reliability. Therefore, a novel method for modeling mission reliability in a multistate manufacturing system is proposed. This method is based on an extended RQR chain (manufacturing system Reliability, operational process Quality, and the produced product Reliability) while considering process scheme robustness. First, the connotation of process scheme robustness and mission reliability in multistate manufacturing systems is described. Second, an extended RQR chain (ERQR chain) model is established, which considers the influence of process scheme robustness on the high-quality operation of the manufacturing system. Third, based on the established ERQR chain model, an integrated mission reliability model considering process scheme robustness for multistate manufacturing systems is established. Specifically, the process scheme robustness is evaluated from three perspectives: manufacturing cost, operation time, and product qualification rate. Finally, a hydraulic pump is used as an example to verify the effectiveness of the proposed approach.

Demonstrating the principal mechanism of action of medical devices intended for vaginal use on reconstructed human vaginal epithelium: the case of two hyaluronic acid-containing devices

Regulation (EU) 2017/745 on medical devices (MDR) has significantly modified the rules to be adopted for MD qualifications and classification. New requirements require robust evidence on mechanisms of action (MoAs) that cannot be produced by existing common EU or ISO standards. Therefore, on a “case-by-case basis,” a new evidence-based non-clinical approach to MD qualification must be defined. In this study, an in vitro experimental approach is described to assess the physicochemical and mechanical MoA of two hyaluronic acid (HA)-based medical devices: Mucogyne® Gel and Mucogyne® Ovule for vaginal use. They both act as moisturizers and lubricants as well as a healing adjuvant by promoting the continued moisture of the vulvovaginal area. The MoA of these two products has been demonstrated by using a 3D reconstructed human vaginal epithelium (HVE) model in a homeostatic physiological state and in stressed conditions. Film forming and persistency properties were assessed on intact HVE tissues by caffeine permeation assay and Lucifer Yellow (LY) localization on HVE vertical sections. Healing properties were assessed on injured HVE tissues by trans-electrical epithelial resistance (TEER) measurements associated with histo-morphological analysis (H&E), and moisturizing efficacy was evaluated on HVE tissues cultured in dry conditions by histomorphological analysis (H&E) and aquaporin 3 (AQP3) expression and localization by immunohistochemistry (IHC). Using the same “dry” HVE model, the non-pharmacological action of the two products was addressed by CD44 (hyaluronic acid receptor) expression and localization. The results suggest that in vitro evaluations can provide robust results on a human-relevant experimental model for the intended use of the products and supports clinical data with mechanistic information which may not be achieved with in vivo studies but are particularly important for product qualification. The results also underline the specific relative efficacy of the mechanisms investigated for Mucogyne® Gel and Mucogyne® Ovule in line with their different formulation types (respectively, hydrophilic and lipophilic) that influence the action of the active ingredient HA. The present in vitro non-clinical evaluation of HVE combined with clinical investigation data obtained in women explain why Mucogyne MDs provide significant benefits in various physiological or pathological situations, including vaginal dryness and healing.

Novel Workstation Module and Method for Automatic Blanking of Surgical Forceps

During the manufacturing of surgical forceps, the flashes of the blanks need to be removed. Manual production has problems such as high labor intensity, low efficiency, and high-risk factors. To solve this problem and realize fully automatic resection, a novel modular workstation was designed and a corresponding process method was proposed. The workstation adopts robots, non-standard automation equipment, and image recognition technology instead of manual loading and blanking, but the blank storage still needs to be performed manually. The critical components were selected according to the workstation design scheme and process method, and the control system design was completed. The reliability of the separation unit was studied through a test platform, and the failure problem caused by uneven force was solved using a blank locking device, which showed that the separation success rate was stabilized at 100%. The detection speed of the image recognition system can reach 100 ms/piece, and the product qualification rate can reach 95.7%. The advantages of the workstation in terms of output and productivity were further analyzed by comparing it to manual production, where the average daily output increased by 12.5% (4500 pieces). In addition, the results of long-term test experiments and power consumption comparison tests showed that the workstations are highly stable and consume little additional power.

Generating the Evidence Base for Convalescent Plasma Use for a New Infectious Disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) swept across the world in the waning months of 2019 and emerged as the cause of the coronavirus disease 19 (COVID-19) pandemic in early 2020. The use of convalescent plasma (CP) for prior respiratory pandemics provided a strong biological rationale for the rapid deployment of COVID-19 convalescent plasma (CCP) in early 2020 when no validated treatments or prior immunity existed. CCP is an antiviral agent, with its activity against SARS-CoV-2 stemming from specific antibodies elicited by the virus. Early efforts to investigate the efficacy of CCP in randomized clinical trials (RCTs) that targeted hospitalized patients with COVID-19 did not demonstrate the overall efficacy of CCP despite signals of benefit in certain subgroups, such as those treated earlier in disease. In contrast, studies adhering to the principles of antibody therapy in their study design, choice of patient population, and product qualification, i.e., those that administered high levels of specific antibody during the viral phase of disease in immunocompromised or very early in immunocompetent individuals, demonstrated benefits. In this chapter, we leverage the knowledge gained from clinical studies of CCP for COVID-19 to propose a framework for future studies of CP for a new infectious disease. This framework includes obtaining high-quality CP and designing clinical studies that adhere to the principles of antibody therapy to generate a robust evidence base for using CP.

Characteristic analysis of mold level fluctuation during continuous casting of Ti-bearing IF steel

Mold level fluctuation has always been one of the key factors in slab quality control. Abnormal fluctuations can easily cause slag entrainment and form surface defects of hot-rolled and cold-rolled steel sheets, thus reducing the product qualification rate. In this study, mold level fluctuation data was collected during Ti-bearing Interstitial Free (IF) steel in a stable state for a complete casting sequence in a certain factory. The mold level fluctuation is analyzed using the discrete wavelet transform (DWT) method, and its characteristic values are classified based on frequency. Combined with the corresponding process parameters, the causes were traced during abnormal fluctuation characteristic values. It was found that the formation of abnormal mold level fluctuation mainly comes from two aspects. On the one hand, there are random fluctuations in the low-frequency band, and on the other, there are clustered fluctuations in the relatively high-frequency band. There is always a rising trend in mold liquid levels when there are random abnormal fluctuations in the low-frequency band. There is a delay of approximately 0.1412 s in the feedback of the stopper rod to the mold level fluctuation. The major causes of the low-frequency random fluctuations include variations in the tundish weight, casting speed, and mold width, and the clogs peeling. The clogging of the side hole at the submerged entry nozzle (SEN) is the primary cause of abnormal clustered fluctuations in the high-frequency band. These are certain relationships with clogging, whether it is high-frequency clustered or low-frequency random abnormal fluctuations. Without a doubt, this is a crucial link that limits the slab quality.

Assessing thermal resistance as a degradation metric for solder bump arrays in discrete SiC MOSFET packages

The development of innovative power electronics solutions for automotive applications introduces new reliability requirements for electronic assemblies and interconnection technologies. Many of the reliability methods used in power electronics require extensive experimental data, resulting in long product design cycles. This work focuses on developing a simulation-driven approach to assess the solder joint reliability of a discrete silicon carbide MOSFET by monitoring its degradation under power cycling in the thermal and thermo-mechanical domains using the thermal resistance of the stack as a failure metric. Active power cycling tests are performed to determine the loading condition at which end-of-life is reached due to a 20% increase in thermal resistance. Numerical analysis using finite element simulations is conducted to gain a physical understanding of the failure criterion, allowing to monitor solder degradation based on the thermal resistance and to pinpoint failure at individual interconnects within a solder bump array. The proposed methodology aims to accelerate the quality assurance and product qualification processes of discrete power electronic devices.

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

A review of the current state-of-the-art on in situ monitoring in electron beam powder bed fusion

The industrial development of electron beam powder bed fusion (PBF-EB) is relatively younger and much more limited in terms of global widespread and revenues compared to laser powder bed fusion (PBF-L). Nevertheless, PBF-EB has been adopted in some of the most successful industrial case studies of metal AM, as it provides specific benefits and capabilities that make it a key enabling technology in a variety of industrial applications. Moreover, the recent years have seen a rapid evolution with new actors and new systems entering the market, together with a considerable increase of research and innovation programs. A field of major interest is the development and continuous improvement of in situ sensing and monitoring methods to anticipate the detection of defects, to predict the final quality of the part, and to rethink product qualification procedures. The technological features of the PBF-EB process have motivated the development of solutions that differ from the ones in PBF-L. Some of them have reached a good maturity level, being recently integrated into industrial machines, while others still deserve further research. This study explores the current state-of-the-art on in situ and in-line monitoring of the PBF-EB process, aiming to provide an up-to-date overview of the major differences with respect to PBF-L, currently available methods and their performances, as well as open issues, challenges to be tackled, and perspective for future research and industrial developments.

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

The advent of additive manufacturing (AM) has dramatically shifted the manufacturing sector conceptualization, design, and creation of products. AM can facilitate the production of complicated geometries and create functioning components with distinctive features for aerospace and automotive applications. However, defects such as pores, voids, interfaces, and inclusions can impair the quality and functionality of AM components. Vibration analysis (VA) has become a popular tool for the dynamic qualification and testing of products and nondestructive testing, but the literature lacks a comprehensive review of VA applied to AM. Hence, in this article, recent advances in the application of VA for identifying and characterizing flaws in metal alloys, including titanium, aluminum, and nickel‐based alloys produced by AM, are summarized. In this review, studies on defects such as porosity, cracks, and inclusions and their effect on VA are also included. Herein, this article concludes with a discussion of the limitations of VA for defect characterization and future research directions. Overall, VA is a promising nondestructive testing method for quality assurance in AM and offers insights on overcoming the difficulties for further development and application of this technology.

Positioning accuracy prediction of precision cycloid reducer based on a BDDTE model and use in design optimization

As users' expectations for precision cycloid reducer positioning accuracy rise, accurate positioning accuracy prediction of products before assembly and design solutions to improve product qualification rates are very important. Considering the actual manufacturing conditions of components, a new multi-source error (MSE) equivalence modeling assumption and pin gear's MSE measurement method are proposed, and the rationality of the assumption is verified through experiments. Based on the geometric characteristics of each component's MSEs, a new analytical model of bi-directional drive transmission error (BDDTE) is established, which organically unifies the prediction of transmission error (TE) and lost motion (LM). The influence of the output mechanism on the positioning accuracy of the entire reducer is found to be non-negligible through Monte Carlo simulation; meanwhile, the general tolerance allocation scheme makes it difficult to ensure that each prototype meets the performance requirements. For this reason, a new design solution for the positioning accuracy of the reducer with the pin gear and output mechanism as the optimization targets is proposed. Finally, the prototype test results show that the BDDTE model based on the new assumption has a higher accuracy in predicting positioning accuracy compared with the model based on the conventional assumptions. Meanwhile, the effectiveness of the proposed improvement design solution for positioning accuracy improvement is verified. The BDDTE model reveals the intrinsic connection between the TE and LM of the cycloid reducer, and the improvement solution has guiding effects on improving the product qualification rate.

CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal–open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-mean-square deviation from observations of less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (CT), and pH. The global average 1σ uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and CT, and 0.4 % for pH relative to their global mean values. Both model–observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account as a priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification. The data associated with this study are available at https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982 (Chau et al., 2023).

Enterprise intelligent manufacturing data analysis technology based on big data analysis

The rise of big data has deeply influenced various industries, especially the intelligent manufacturing of enterprises. However, traditional data analysis methods are difficult to adapt to the storage and analysis of sea volume data in intelligent production. To address this issue, a method relying on big data analysis and cluster analysis is proposed to design data analysis techniques for enterprise intelligent manufacturing. The proposed improved algorithm is subjected to performance testing. The accuracy of this algorithm is 97%, which exceeds the comparison algorithm. The error is 6% and the running time is 5 s, both of which are below the comparison algorithm. The effectiveness of the enterprise intelligent manufacturing data analysis technology is tested. The experimental group completes orders in 4.1 weeks, 5.2 weeks, 3 weeks, 3.4 weeks, and 4.9 weeks, respectively, shorter than the control group. The product qualification rates for the experimental group are 92%, 93%, 95%, 92%, and 92%, respectively, which exceed the control group. In summary, the proposed enterprise intelligent manufacturing data analysis technology relying on big data and cluster analysis can better utilize data resources and information technology, improving the production efficiency and competitiveness of enterprises. It is hope that this research result can provide useful guidance and reference for the application and development of intelligent manufacturing data analysis technology in enterprises.

Research on Precision Bending Forming Technology of Aluminum Alloy Outer Frame

In this paper, the 6063 aluminum alloy outer frame is prone to cracking during bending forming. We analyze the strength and plasticity of the material through tensile experiments (in situ stretching, high-temperature stretching), and establish constitutive models based on component material parameter equations. Using COMSOL Multiphysics software, numerical simulation was conducted on the bending forming of 6063 aluminum alloy profiles, analyzing the stress and deformation of the material during the extrusion process. By utilizing simulation results to design and improve the process and mold, the aluminum alloy outer frame will not produce cracks during the bending forming process, thereby improving the product qualification rate.

Creation a Blockchain-based Product Qualification System for E-Marketplace using Practical Byzantine Fault Tolerance Algorithm

An indicator of e-marketplace effectiveness is to facilitate transparent transactions between sellers and buyers by offering good products and adequate infrastructure to handle system requirements. A good product must be in accordance with the product qualification which consists of the classification of feasibility or not, and the benefits of the product. However, this often has problems including those caused by frequent problematic system conditions, non-transparent product qualifications, undistributed data, human error until data security. The researcher proposes the creation of a product qualification system for e-marketplace using blockchain technology. The product qualification system consists of data verification and validation carried out by stakeholders involved in the decentralized network. Transactions that have been executed and validated will be stored in the distributed ledger. The focus of this research is the implementation of blockchain that is allowed to use the PBFT algorithm on product qualification records in the e-marketplace. The purpose of this research is to create a blockchain-based product qualification system for e-marketplace that is transparent and immutable and the successful performance of the system in information storage and management security. The results show the creation of a web-based apps product qualification system that implements a blockchain using Hyperledger Fabric and the performance obtained through system testing and data validation testing.

Mask-less Laser Direct Imaging &amp; Adaptive Patterning for Fan-Out Heterogeneous Integration

Mask-less Laser Direct Imaging &amp; Adaptive Patterning for Fan-Out Heterogeneous Integration

Based on the integration of multimedia e-commerce environment in the enterprise management innovation technology applications

Abstract This paper first applies Multi-Agents’ distributed data management model to establish an enterprise economic data mining platform, combined with a clustering algorithm to extract valuable economic data from a multi-source heterogeneous enterprise database. The weights of indicators are solved by particle cluster algorithm, and the linkage vector distance and set-pair analysis of linkage degree are introduced to construct the innovative technology of enterprise economics and management under the integrated multimedia e-commerce environment. The study shows that the enterprise macro optimal economic index weight of this paper’s method is 0.812, and the production qualification rate of the enterprise is as high as 98.8%, which indicates that this paper’s method can meet the innovation of the enterprise development, and realize the enterprise business management and development strategy is of great significance.

Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines

The characteristics of flexible production lines, i.e., “multiple steps and few processes”, increase the complexity of the process and the difficulty of process quality control, but are not conducive for improving the quality and efficiency of a production line. In this study, we use a flexible production line processing spindle box as the research object. Using an extensive data analysis method, we study the key influencing factors of process quality and the prediction and optimization of process quality characteristics, aiming to accurately predict the machining accuracy of flexible production line processes and to achieve efficient quality control. A fuzzy hierarchical analysis-based impact factor model is developed to obtain a process quality impact factor model consistent with the spindle box of a production line. By verifying the prediction accuracy of 24 sets of quality spindle bore data, a prediction model with a relative error of less than 0.01 is obtained, which provides a prediction sample for analyzing potential problems of process quality in a production line. The SPC control principle is used to monitor process quality by using the standard control method, and the change trends between the actual and predicted values of quality characteristics are compared to achieve predictive control of the process quality. The product qualification rate of this control scheme under this monitoring method is 96%.

ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.

In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after packaging. However, the current traditional defect detection algorithm cannot meet the demands of high accuracy, fast speed, and real-time chip defect detection in industrial production. Therefore, this paper proposes a new multi-scale feature fusion module (ATSPPF) based on convolutional neural networks, which can more fully extract semantic information at different scales. In addition, based on this module, we design a deep learning model (ATNet) for detecting lead defects in chips. The experimental results show that at 8.2 giga floating point operations (GFLOPs) and 146 frames per second (FPS), mAP0.5 and mAP0.5-0.95 can achieve an average accuracy of 99.4% and 69.3%, respectively, while the detection speed is faster than the baseline yolov5s by nearly 50%.

Cooling Pipe Network System in the Quenching Green Equipment Design Method Based on the “Solid Contact-Fluid Heat Transfer” Mode

As a typical thin plate part, a circular saw blade matrix is prone to deformation and cracking due to uneven surface cooling rate during heat treatment. Simultaneously, the traditional quenching method is easy to produce a low product qualification rate and serious environmental severe when processing thin plate parts. The quenching device designed in this paper is to achieve the non-contact quenching between the processed parts and the quenching medium through the heat transfer of the fluid in the cooling pipe. The structure design of the internal cooling channel will directly affect the temperature field distribution of the quenching device, thus determining the microstructure and final performance of the product. Therefore, based on the microstructure transformation mechanism and pipe network flow control theory, combined with the heat transfer theory and other relevant theoretical bases, this paper established the mathematical model of internal cooling system parameters. Finally, MATLAB is applied to solve the fluid network topology model. It provides a reliable and general research method for cooling pipe networks in quenching devices based on the “Solid Contact-Fluid Heat Transfer” Mode.

CMOS Scaling Challenges for High Performance and Low Power applications facing Reliability Criteria towards the Decananometer range

The huge improvements in integrated circuits manufacturing has faced great challenges between process optimization, performance requirements and the trade-off between low power operation and reliability for long term use. Both the variability at time zero and the time variability due to external constraints and aging phenomena make mandatory the validation of the CMOS technology nodes from device to circuits and products under operation. While High-K Metal-Gate (HKMG) offered good compromises down to 28nm gate-length, the move to Fully Depleted Silicon on Insulator (FDSOI) allows to further scale the dimension down to 14nm effective gate-length with ultra-thin equivalent gate-oxide thickness (EOT) of 1.35 nm. This has been obtained guarantying a small subthreshold slope for switching, small drain-induced barrier lowering (DIBL) for limited short-channel effect (SCE) and excellent current drivability thanks to a proper gate-stack with HfO2/SiON optimization and adapted rapid thermal processing and annealing. We give new insights to determine first the performance with temperature, the process variability impact at time zero and the robustness of CMOS nodes submitted to interface traps, oxide charge and recoverable traps. Their role is analysed using accelerated DC and AC experiments in devices to SRAM cell and array, focusing on the balance between hot-carrier and bias temperature damage. This allows to guaranty the technology robustness, speed performance and limited power consumption for product qualification.