Manufacturing Process Quality Research Articles

An experimental and numerical study on optimization of number and position of the clamps in sheet metals fixture

The accuracy of the machining process is highly dependent on how well a workpiece is constrained in a fixture. A well-constrained workpiece has high stiffness and shows minimum deformation under the application of load. Workpiece can be constrained by the proper positioning of fixture elements. This research introduces a finite element-based numerical methodology to automatically select optimum number and position of clamps in the sheet metal, necessary for good quality of manufacturing process. The objective function is to minimize the total deformation normal to the plane of sheet metal while keeping maximum deformation of individual nodes upto 2 mm. The N-3-2-1 fixturing principle is used to place clamps on primary plane of the workpiece. The proposed method consists of two stages; in first stage, the initial number of clamps are determined. In second stage, initial number of clamps and their positions are optimized using genetic algorithm. The proposed method implemented on the case studies successfully reduced the total deformation with optimized number of clamps. The maximum deformation in individual nodes was kept upto 2 mm. An experimental setup was developed to verify the numerical results. The numerical and experimental results fully justify the proposed method.

Q-Marker: An Integrative Approach and Scientific Validation in the Indian System of Medicine

Abstract: The prevalence of plant-based medicine has risen worldwide, not only as part of conventional treatment but also for health care management. The authenticity and quality of herbal medicines impact these medications' safety and effectiveness in clinical treatment. So, it is necessary to identify the chemical composition and quality of pharmacologically active ingredients in the plants as there is no pertinent data regarding the exact dose of the Herbal based medicine. Similarly, the Indian system of Medicine (ISM) based products may vary in composition and properties, and increasing reports of adverse reactions have drawn the attention of many regulatory agencies for the quality evaluation of traditional formulations. Moreover, ISM's quality control has always been an important domain, and its current quality standards have a complex effect to assure clinical efficacy. The Quality marker (Q-marker) technique is a new approach in Chinese traditional medicines to enhance and empower Chinese herbal drugs by focusing on each step of the plant processing from its growing phases to the marketed product formulation. The present review highlights the application of Q-marker and its tools for the establishment of quality standards of herbal drugs. Besides, this work also highlighted complies few Q-marker-based traditional Chinese medicines case studies. We expect that it would benefit the manufacturing process control and quality management of the Indian system of medicine. This work could provide a new direction for Q marker in the Indian system of medicine and contribute in the modernization and globalization of the Indian system of Medicine.

In-situ observation of powder spreading in powder bed fusion metal additive manufacturing process using particle image velocimetry

Providing a dense powder bed is necessary to ensure the rationality of the final product prepared by powder bed fusion-based additive manufacturing under broad building parameters. In this study, the powder spreading mechanism was elucidated using particle image velocimetry and discrete element simulations. The particle flow regimes in the spreading process were identified based on the particle displacement: alignment, rotation, and deposition. The alignment regime was dominant in gas-atomized stainless steel 304 powder piles, whereas the rotation regime was dominant in plasma rotating electrode processed stainless steel 304 powder piles. A high-fidelity spreading simulation model was developed to clarify the critical factors resulting in the different flow regimes of different stainless steel 304 powders. The dominant rotational regime of the plasma rotating electrode processed powder was substituted for alignment by increasing the cohesive force. The particle supply in the spreading process was further suppressed by increasing the cohesive force, owing to the formation of a strong force arch and agglomeration. The high cohesive force in the gas-atomized powder was mainly attributed to the electrostatic force caused by the thick oxide film. Therefore, it was proven that the oxide film thickness is a key factor in determining the powder spreading mechanism and powder bed quality in the powder bed fusion additive manufacturing process.

Identification of Degradation Products of the New Anticancer Drug Substance ONC201 by Liquid Chromatography–High-Resolution Multistage Mass Spectrometry

ONC201 (dordaviprone) is a new drug substance used in a compassionate manner to treat patients with glioblastoma. Given the clinical context and the particularly promising preclinical results, we have been asked by the medical authorities to make a first treatment available throughout France as a hospital preparation to allow access to treatment and to conduct clinical trials. However, to control the quality and safety conditions inherent in this academic manufacturing process, while there is virtually no data available to date to understand the stability of ONC201, we had to determine the stability profile of ONC201, i.e., its sensitivity to different stressors and the types of impurities that could form during its degradation. We found that ONC201 was sensitive to oxidation in the presence of hydrogen peroxide or under light irradiation. Both conditions resulted in the formation of 20 degradation products detected and identified by liquid chromatography–high-resolution mass spectrometry. Their structural elucidation required an in-depth study of the fragmentation pattern of protonated ONC201, described for the first time. The product ions of the degradation products were compared to those of ONC201 protonated ion to assign the most plausible structures for all the detected degradation products. Of these degradation products, those that were rapidly produced, of high intensity and/or identified as potentially having a different toxicity profile to ONC201 by in silico studies, were selected to be monitored during batch release testing and stability studies.

Classifying and clustering noisy images using subset learning based on convolutional neural networks

AbstractA high yield rate is a key factor related to success in the competitive global semiconductor manufacturing business market. Wafer bin maps (WBMs) can be used as one measure of the output quality of a semiconductor manufacturing process. A WBM is the image results from a number of circuit probe (CP) tests on a wafer after the completion of a manufacturing process. The specific defect patterns on WBMs provide crucial information for engineers to trace the causes of defects in the complicated manufacturing process. This study is aimed toward investigating major practical challenges in current WBM analyses. Our approach involved utilizing a small, carefully labeled subset to reduce the labor requirements and human recognition bias related to identification of very noisy images. The first proposed procedure classified the noisy defect patterns by using convolutional neural networks (CNNs) trained with a small subset of labeled WBMs in the early batches. The second proposed procedure provided the proper clusters of noisy defect patterns using the features extracted from the trained CNNs. This procedure made it possible to generate various clusters of WMBs and integrate them in label space. The third procedure separated the new pattern from the existing defect patterns with the help of a supplemental dataset from a similar wafer product. The evaluation of the three proposed procedures was done with simulation data generated from real sets of WBMs, which were added with random noise to maintain confidentiality. The evaluation results demonstrated the practical validity of the proposed procedures.

Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.

Abnormal Event Detection of Workshop Logistics Production Management Based on RFID Technology

RFID, as one of the most important key technologies in the next generation of advanced manufacturing system, has increasingly significant advantages in the application of data collection in the manufacturing workshop, real-time tracking of the manufacturing process and product quality backtracking, etc., and is considered to be the most potential and play a huge role in the technological innovation of manufacturing informatization. In order to better study the abnormal event detection technology of workshop logistics production management based on RFID technology, this paper firstly studies the abnormal event detection method of workshop production based on RFID technology based on the analysis of RFID technology principle, data characteristics and workshop production process. Then, a formalized complex event representation method is constructed to meet the complex and changeable business logic requirements in the actual workshop production application. Then, the solution idea of extracting complex RFID events from massive RFID data using Rete algorithm based on MapReduce framework is discussed. The results show that the accuracy of detecting abnormal events increases by 18.21%, which proves the validity of the model.

FDD: a deep learning-based steel defect detectors.

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.

An index of capability for bivariate zero-inflated processes

Rapid technological advancement and implementation of automation and computerization in today's manufacturing set up resulted in many high quality processes, where defects are rarely observed. There are many high quality manufacturing processes where two or more types of defects may be generated from different types of equipment/process problems. The zeroinflated defects data containing two types of defects are commonly modeled by bivariate zero-inflated (BZI) Poisson distribution. Pal and Gauri (2022a) proposed a methodology for measuring capability of a BZI Poisson process. However, they ignored the count of zero defect (ZD) products produced in a BZI process. Because of that, Pal and Gauri (2022a) proposed approach fails to discriminate the BZI processes which produces different proportions of ZD units but having almost the same proportion of nonconforming items with respect to the USL of combined number of defects or USLs of individual defect types. In this paper, a new measure of process capability for BZI processes is proposed that can truly discriminate different BZI processes taking into account the USL of combined number of defects (or USLs of individual defect types) as well as the proportion of ZD units produced in these processes. The proposed methodology is illustrated using two case studies. The results of the case studies show that the proposed index well represents the true capability of BZI processes.

A 1 × 2 Two-Dimensional Slanted Grating Based on Double-Layer Cylindrical Structure

Diffraction gratings play an increasingly important role in various planar optical systems, such as near-eye display systems for virtual reality (VR) and augmented reality (AR). The slanted gratings have more advantages than other elements. A 1 × 2 transmission two-dimensional (2D) slanted grating based on a double-layer cylindrical structure was proposed in this paper. In the initial phase of this study, this kind of grating was proposed and designed. We used rigorous coupled-wave analysis (RCWA) and simulated annealing algorithm (SA) to optimize the grating parameters. The effects of the grating geometric parameters on the diffraction efficiency were investigated using rigorous coupled-wave analysis (RCWA). The simulated annealing algorithm (SA) optimization results show that the diffraction efficiency of the (0, −1) and (−1, 0) order exceed 35% under normal incidence in the range of 429–468 nm wavelength for TE and TM polarization. Meanwhile, the total diffraction efficiency can reach up to 78%. In the last section, we discuss the tolerances for the grating parameters to ensure high quality manufacturing processes. The total effective efficiency is greater than 75% when the MgF2 thickness is from 300 nm to 350 nm and the SiO2 thickness is from 525 nm to 550 nm. Moreover, the grating period has a 53 nm fabrication tolerance, and the slanted angle has a 8.8-degree fabrication tolerance. The relatively large tolerances ensure that it is easy to fabricate the two-dimensional slanted grating and to achieve the targeted objectives. The proposed 2D slanted grating can be applied to 2D exit pupil expansion, which is of great importance in AR/VR applications.

Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique of structural similarity index measure (SSIM). After extracting handcrafted features, a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction. Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns, the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

Simulation-guided feedforward-feedback control of melt pool temperature in directed energy deposition

High-performance closed-loop control systems are crucial to process stability and part quality in additive manufacturing processes. In this work, a simulation-guided feedforward-feedback control framework is developed for the directed energy deposition process to effectively control the melt pool temperature. The developed framework consists of two stages: offline iterative finite element simulation for feedforward control and online photodiode-based temperature monitoring for feedback control. The experimental results show that the feedforward-feedback control method can significantly improve the control performance when compared to the conventional feedback control method.

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Given the challenge of manufacturing data silos and information credibility issues in traditional aviation suppliers’ result-oriented management approach, this paper proposes a blockchain-based aviation supplier manufacturing process quality data-sharing platform. Firstly, the paper introduces the possibility of integrating manufacturing supply chain quality management with blockchain technology. Secondly, the quality and data sharing platform architecture of the production process of new aviation suppliers is presented based on quality state and island kinds of aviation suppliers. Then, a detailed method for the implementation of quality and data security sharing is proposed to support the sharing platform’s real-time and orderly operation. Build critical technologies such as manufacturing quality data block packaging models, data storage security sharing, and supplier assessment models on this foundation. Finally, depending on data collection of supplier product production processes to shared application practices based on a specific aircraft industrial park under the supervision of the platform architecture and technology. The application platform integrates the data supply and request components, providing practical and intelligent sharing solutions for airlines’ product quality data.

Manufacturing process quality tracking and early warning system for building material equipment group enterprise considering schedule

Manufacturing process quality tracking and early warning system for building material equipment group enterprise considering schedule

Manufacturing process quality tracking and early warning system for building material equipment group enterprise considering schedule

Manufacturing process quality tracking and early warning system for building material equipment group enterprise considering schedule

Cause analysis on the unqualified pull-out force between the bushing and PCB pad in DC-DC converters for new energy vehicles

For new energy vehicles, the DC-DC converter is a key component to realize the electricity conversion between different voltage networks. In the DC-DC converter described in this paper, the copper-substrate bushing was specially designed to fix the PCB onto the aluminum radiator, and thereby the heat generated by the components on the PCB could be successfully dissipated. However, during the ejection tests after reflow soldering, the pull-out force between the bushing and PCB pad was unqualified. To find out the failure causes, the comprehensive characterization analysis and failure analysis were carried out and the analysis objects included the material quality, solder paste printing, reflow soldering process, soldering void and morphology characteristics. Through systematical investigation, the causes of the unqualified pull-out force between the bushing and PCB pad after reflow soldering were finally determined, and some countermeasures were put forward as well. Hopefully, the achievements of the paper could not only provide useful references for avoiding the recurrence of similar problems, but also improve the product quality in the future manufacturing process.

Sensitivity Optimization of Surface Acoustic Wave Yarn Tension Sensor Based on Elastic Beam Theory

The measurement of yarn tension has a direct impact on the product quality and production efficiency in the textile manufacturing process, and the surface acoustic wave (SAW) yarn tension sensor is a good option for detecting the yarn tension. For SAW yarn tension sensors, sensitivity is an important indicator to assess their performance. In this paper, a new type of SAW yarn tension sensor based on a simply supported beam structure is studied to improve the sensitivity of the fixed beam SAW yarn tension sensor. The sensitivity analysis method based on elastic beam theory is proposed to illustrate the sensitivity optimization. According to the analysis results, the sensitivity of the SAW yarn tension sensor can be greatly improved by using a simply supported beam structure compared to the s fixed beam structure. Moreover, from the calibration experiment, the sensitivity of the simply supported beam SAW yarn tension sensor is 2.5 times higher than that of the fixed beam sensor.

Advances in laser powder bed fusion thanks to beam shaping

AbstractCailabs have designed a reconfigurable beam shaper dedicated to the laser powder bed fusion process that is able to produce an ellipse, ring or round top‐hat beam profile in the processing plane. This study highlights the potential of beam shaping to improve the quality and productivity of the additive manufacturing process. Using a machine which was not initially able to print Inconel 939, the round top‐hat shape made it possible with a large scan speed process window.

Digital thread-based modeling of digital twin framework for the aircraft assembly system

Digital twins are proposed to manage the industrial production sites by mapping physical space entities to virtual spaces. Existing digital twin modeling methods are mainly realized through deploying several modules, including a physical module, a virtual module, a service module, a data module, and a connection module. However, the assembly environment of the aircraft assembly process is complex and challenging to manage. These methods do not provide the data flow and the intrinsic interaction between modules. As a consequence, a digital thread-based modeling digital twin (DTDT) framework with five modules is established in this paper, which takes advantage of both the digital twin and digital thread. Benefited by better data management, the DTDT framework would be conducive to the controllability and traceability of the manufacturing process and product quality. The key characteristics of products, process parameters, and equipment operation conditions, are stored in the database module. And then, the digital thread module will link the entity module and virtual module together by building the directed graph, in which interfaces are provided to the system services module for the process monitoring and processing quality prediction. Finally, the implementation of the DTDT framework is shown through a case of the drilling and riveting process in aircraft assembly. The comparative analysis confirms that this model can improve the efficiency of the aircraft assembly process.

Determination of Heat Transfer Coefficient by Inverse Analyzing for Selective Laser Melting (SLM) of AlSi10Mg

Heat treatment can improve performance and control quality in the additive manufacturing process. In the numerical simulation of heat treatment, the accuracy of the heat transfer coefficient will have a significant impact on the accuracy of the simulated temperature field. At present, The inverse analysis method is the most common and effective method to determine the heat transfer coefficient. Taking the actual temperature curve as the input condition, the heat transfer coefficient values of the heating, quenching, and air cooling components in the heat treatment process are successfully obtained. Based on the obtained heat transfer coefficient, a mathematical model of the heat transfer coefficient change with temperature during heat treatment is established. The heat transfer coefficient obtained by the inverse analysis method is then applied to the simulation of heat treatment, and more accurate simulation results are obtained. It is proven in this work that the inverse analysis method can improve the accuracy of the simulation model in the heat treatment process of AlSi10Mg.