Manufacturing Process Quality Research Articles

Effect of drilling process parameters on natural fiber reinforced basket epoxy composites using grey relational analysis

In industrial and technological development of recent years, constant increase in quality of machining is the key motto and natural composites are playing predominant role in this path. They have taken prominent place due to their prevailing properties (strength to weight ratio, wear resistant eco-friendly, corrosion resistant, recyclability, toughness), their availability, cheap manufacturing process and high quality that fetched composites great demand and made the researchers to use composites in many real world applications .Natural fibers are being machined by several machining process one among them is drilling. Natural composites are heterogeneous in nature so it is difficult to drill them, as drill bit need to pass alternatively through fiber and matrix and may cause the composites to delaminate, while performing Drilling process, there are many parameters that need to be studied such as spindle speed (rpm), feed rate (mm/min), drill bit diameter that are influencing highly on the drilled hole. In this work we optimize the thrust force, delamination, torque; using Taguchi and Grey regression techniques .The natural composites are subjected to drilling with different drill diameters of varied sizes. The worked results give us the clear picture that the quality of drilled hole can be improved by selecting optimal value for the input characteristics using grey relational technique

Process parameter optimisation of a minster draw‐redraw system

Non‐conformance in Unit Depth Value (UDV) of expansion rings for metal cans makes them prone to major defects, such as swells and distortions, during the Drawing/Redrawing industrial process. In this paper, with the aim of improving the manufacturing process quality, the process parameters for the Minster Draw/Redraw machine at a generic company are optimised so as to obtain the desired UDV. The Taguchi method is used to determine the most influential process parameters. Nine experiments have been performed with four selected parameters, namely, Upper Blank Holder Pressure, Lower Blank Holder Pressure, Profile Pad Pressure, and Inner Ram Value. A multiple‐linear regression analysis is also conducted to demonstrate the relationship between UDV and the considered parameters. The best settings for the respective process parameters are obtained via Analysis of Means (ANOM). Furthermore, Analysis of Variance (ANOVA) shows that the Inner Ram Value is the process parameter that most significantly affects the formation of expansion rings. The same methodology proposed in this study can easily be extended to find the optimum settings of process parameters for different can heights. Finally, an equation is formulated to predict the setting values of the most significant process parameters under user‐specified UDVs.

Fault detection using bispectral features and one-class classifiers

Smart manufacturing systems are equipped with various sensors to collect engineering variables for monitoring of manufacturing process quality, such as fault detection. Multiple fundamental difficulties, however, arise in the fault detection including high nonlinearity, high dimensionality, unequal batch length, unsynchronized batch trajectory and imbalance in the ratio of fault data and normal data in the sensor time series recordings. To capture the dynamics, nonlinearity and non-Gaussianity in the recordings, we design a feature extraction method using bispectral features and Principal Component Analysis (PCA); to address the class-imbalance issue, we use one-class classifiers. We evaluate the proposed method using recall, precision, G-means and Area Under ROC Curve (AUC) on a publicly available benchmark of a real semiconductor etching dataset for fault detection. It is demonstrated that bispectral features processed further by PCA can significantly improve the fault detection accuracy and lower the false alarm rate of one-class classifiers. The k-Nearest Neighbor (k-NN) can achieve the 100% fault detection rate and zero false alarm in the 10-fold cross validation test.

Reduction of defects in the lapping process of the silicon wafer manufacturing: the Six Sigma application

Abstract Aiming to reduce flatness (Total Thickness Variation, TTV) defects in the lapping process of the silicon wafer manufacturing, it is crucial to understand and eliminate the root cause(s). Financial losses resulting from TTV defects make the lapping process unsustainable. DMAIC (Define, Measure, Analyse, Improve and Control), which is a Six Sigma methodology, was implemented to improve the quality of the silicon wafer manufacturing process. The study design and the choice of procedures were contingent on customer requirements and customised to ensure maximum satisfaction; which is the underlying principle of the rigorous, statistical technique of Six Sigma. Previously unknown causes of high TTV reject rates were identified, and a massive reduction in the TTV reject rate was achieved (from 4.43% to 0.02%). Also, the lapping process capability (Ppk) increased to 3.87 (beyond the required standard of 1.67), suggesting sustainable long-term stability. Control procedures were also effectively implemented using the techniques of poka yoke and control charts. This paper explores the utility of Six Sigma, a quality management technique, to improve the quality of a process used in the semiconductor industry. The application of the Six Sigma methodology in the current project provides an example of the root cause investigation methodology that can be adopted for similar processes or industries. Some of the statistical tools and techniques were used for the first time in this project, thereby providing new analysis and quality improvement platform for the future. The article offers a deeper understanding of the factors that impact on the silicon wafer flatness in the lapping process. It also highlights the benefits of using a structured problem-solving methodology like Six Sigma.

Effect of neutralization of poly(acrylic acid) binder on the dispersion heterogeneity of Li-ion battery electrodes

A systematic study using a model negative electrode slurry with poly(acrylic acid) as a binder is conducted aiming at the control of both dispersion quality and rheology in the electrode manufacturing process. Rheological properties of the model slurries vary considerably with the neutralization degree α. Optical microscope and Turbiscan measurements show that the rheological change is accompanied by the change in particle aggregation tendency. Adsorption and zeta potential measurements reveal that α-dependent adsorption of poly(acrylic acid) is the key factor that controls the rheology of the slurries. The difference in α of the slurry results in a significant difference in the surface roughness and the carbon black particle distribution in the electrode. From this study, it is concluded that the adjustment of neutralization degree of poly(acrylic acid)-based binder critically affects the heterogeneity of the electrode, which can influence the performance as well as the rheological stability in the electrode manufacturing process.

Product reliability–oriented optimization design of time-between-events control chart system for high-quality manufacturing processes

In high-quality manufacturing processes, the yield is no more the only issue, and the quality control approach that also considers the product’s actual performance gradually becomes the focus. Reliability is a critical dimension of quality, and its degradation in usage is always determined by the manufacturing quality. To mitigate the degradation of reliability, this article presents a product reliability–oriented optimization design of the time-between-events control chart system, where the quantitative impact of process quality on the product reliability is analyzed. First, critical-to-reliability process parameters are identified, and a product reliability degradation model is proposed considering the effect of process variations and defects. Second, the observed event and the used statistics are determined to prearrange the time-between-events chart system. Third, all individual time-between-events charts are systematically optimized to minimize the expected batch product reliability degradation caused by potential process shifts, where the cost and statistical performance are considered as constraints. The result of the case study shows that the proposed design can reduce 27.02% of batch product reliability degradation due to manufacturing process quality, and this model can also save the operating cost on the basis of attaining minimum reliability degradation in certain situations.

CHEMICAL ALTERATIONS OF HARDWOOD VENEERS DUE TO THERMAL TREATMENT

Wood is the most popular building material in the world due to its universal versatility, although it has disadvantages - the difficulty to apply small diameter logs in construction, hygroscopicity and anisotropic swelling and shrinking. To solve these disadvantages, plywood from wood material is produced. Plywood is a material that can solve anisotropy, but it is still biodegradable by rot and stain fungi. Thermal treatment is a methodology that improves the durability of wood. In this paper aspen (Populus tremula L.), poplar (Populus x canadensis Moench) and birch (Betula pendula Roth) were treated by steam (WTT) and vacuum (TERMOVUOTO) devices under 160°C/50 min (birch and aspen), 204°C/2 h, 214°C/2 h, 217°C/3 h, 218°C/30 min (birch and poplar). Chemical changes in treated veneers were investigated by ATR-FTIR (Attenuated Total Reflection Fourier Transform Infrared Spectroscopy) in a range 2000 cm-1 – 800 cm-1. ATR-FTIR is a non-destructive methodology, which is important during manufacturing process quality control. Untreated poplar wood and aspen wood had similar ATR-FTIR spectra because both species belong to Populus genus. Untreated birch wood had higher absorption intensity peak at 1740 cm-1, which indicates the C=O bond stretching in the carboxyl group depicting more acetyl groups in birch wood than in aspen/poplar. According to spectral data, birch wood, treated in TERMOVUOTO process at 200°C for 2 hours is chemically almost identical to untreated one. WTT process causes the most significant changes in the chemistry of both in aspen and birch. Therefore, regime 160°C/50 min in water vapour is more aggressive than treatments at higher temperatures and under reduced pressure. It is expected that plywood produced from WTT treated veneers will have reduced strength in comparison with TERMOVUOTO process produced ones.

Electric corona discharge as a basis for managing the quality of solid cereals

The paper overviews directions for electrotechnology treatment of grain seeds before sowing. The hypothesis of the germination enhancement of seeds if treated with corona discharge is presented here. Besides, the description of electrotechnology installation and facilities for it including manufacturing process quality obtained as a result of scientific tests are also provided in the paper.

A deep autoencoder feature learning method for process pattern recognition

Recognition of various defect patterns exhibited in discrete manufacturing processes can significantly reduce the diagnostic processes, and increase manufacturing process stability and quality. Thus the effective recognizers are in great demand to improve the performance of process pattern recognition (PPR). Deep learning has been widely applied in image and visual analysis with great successes. However, the application of deep learning in feature learning for process control is still few. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for PPR in manufacturing processes. This paper will concentrate on developing an SDAE model to learn effective features from the process signals and then implementing an effective PPR through a deep network architecture. Feature visualization is also performed to explicitly present the feature representation of the proposed SDAE model. The effectiveness of the proposed PPR method is verified through a big simulation dataset and Tennessee Eastman process. The result shows that the proposed method obtains good feature learning and PPR performance.

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process.

On-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm

Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify a set of serum-free formulations that support cell expansions similar to the serum-containing conditions commonly used to culture these cells, by experimentally testing less than 1 × 10−5 % of the total search space. We also demonstrate how this iterative search process can provide insights into factor interactions that contribute to supporting cell expansion.

Reliability-oriented design of integrated model of preventive maintenance and quality control policy with time-between-events control chart

Superior manufacturing quality is prerequisite to continuously produce reliable products, and the proactive product reliability assurance is always a crucial routine task for manufacturing. Therefore, this paper presents a reliability-oriented optimization model for joint preventive maintenance (PM) and process quality control with time-between-events (TBE) control chart, where the impact of manufacturing process on product final reliability is considered to reduce the product reliability degradation originating from latent manufacturing defects. Firstly, as the foundation of reliability-oriented control, the product reliability degradation mechanism in manufacturing stage is analyzed, and the process variations of product critical-to-reliability characteristics and internal defects are considered to model the product actual reliability. On this basis, periodical PM and TBE chart are jointly applied to enhance machine performance and improve manufacturing process quality to reduce potential reliability degradation, and a three-scenario model is proposed. Then, to evaluate the performance of the integrated model, the occurrence probability, expected reliability degradation, average operational cost, and cycle time of each scenario are analyzed, and the joint policy is optimized to minimize the expected reliability degradation of batch products within a limited cost range. Finally, a case study is presented to verify the performance of the developed model by comparing it with a pure planned maintenance policy, and a sensitivity analysis is conducted to analyze the impacts of model parameters.

Reliability-based analysis of machine structures using second-order reliability method

In design and manufacture of machine structures and elements, the characteristics and sizes are randomly changing factors because of the change of loads, the mechanical properties of materials, and the quality of manufacturing process (size tolerances). Thus, the reliability of structures is extremely important to be considered carefully. This paper presents the application of Second-Order Reliability Method (SORM) to analyze and design of CNC milling machine structure. Many examples are utilized to validate the use of the proposed SORM approach. Then, the analytical findings are verified by comparing with First-Order Reliability Method (FORM) and Monte Carlo Method (MCS). The obtained findings highlighted that the SORM method is more reliable than the FORM method. Besides, the result deviations between the SORM method and the MCS method are negligible. Therefore, SORM method is a potentially effective approach in calculation and design with the aims of improving the reliability, increasing safety, and working efficiency of machine structures.

Development of an integrated design methodology model for quality and throughput of Additive Manufacturing processes

Additive manufacturing (AM) has risen to be a substantial part of modern manufacturing due to its unique capabilities and has already been fondly adopted in various fields especially in the aerospace. In order to fully harness the benefits of this revolutionary manufacturing technology, this article aims to develop a practical integrated design methodology that can be used to enhance quality and throughput of AM processes. In doing so, investigation were conducted to examine an AM aero-based component through design tools that allow designers to consider an integrated process chain, from component design to pre-processing, manufacturing (laser bed fusion building), post-processing and finished part. The developed design model integrated with decision tools will assist the design experts in developing new knowledge that looks beyond the familiar Design for Additive Manufacturing (DfAM) rather to Design towards product Certification (DoC). In addition, this will serve as an effective design guidance that can inform proper design planning and optimization in aerospace industry production.

Rapid and simultaneous analysis of five free anthraquinone contents in rhubarb during the stir-frying with rice wine process by near infrared reflectance spectroscopy

Background: The active ingredients of Traditional Chinese Medicines (TCMs) vary greatly with the degree of stir-frying; so, rapid analysis of the active content is very important for the processing of TCMs. Objective: In this study, near infrared reflectance (NIR) spectroscopy was used to develop a new method for the rapid online analysis of five free anthraquinones (aloe-emodin, rhein, emodin, chrysophanol, and physcion) during the stir-frying process for rhubarb. Materials and Methods: With partial least-squares (PLSs) and artificial neural networks (ANN) regression, calibration models were generated based on five free anthraquinone contents, as measured by high-performance liquid chromatography. Results: The results indicated that the 2 types of models were robust, accurate, and repeatable for five free anthraquinones. Moreover, PLS as the linear model was more suitable for developing the NIR models of the five free anthraquinones than ANN. The performance of the optimal models was achieved as follows: the coefficient of determination for prediction (R2pre) for aloe-emodin, rhein, emodin, chrysophanol, and physcion was 0.9161, 0.9699, 0.9655, 0.9611, and 0.9724, respectively; the root mean square error of prediction was 0.0251, 0.0445, 0.3333, 0.0862, and 0.0211, respectively. Conclusion: The established NIR models could apply to determine the content of five free anthraquinones in rhubarb. This work demonstrated that NIR may be an effective online analysis method to reflect the quality of TCM industrial manufacturing processes.

Sensors performance in laser-based manufacturing process quality assessment: a conceptual framework

Laser-based manufacturing processes are emerging in manufacturing. Thus, new requirements are created with respect to monitoring, control, and online quality assessment. This work describes a framework for both studying and designing the performance of an IR-vision sensor with respect to real-time on-axis laser-based process quality assessment. More specifically, utilizing physics model at machine level as well as experimental results, requirements are extracted for the efficiency metrics of the sensor. These requirements regard temporal and spatial characteristics of the sensor physical layer, as well as software (algorithmic) level specifications.

Structural Analysis of Monoclonal Antibodies by Ultrahigh Resolution MALDI In-Source Decay FT-ICR Mass Spectrometry.

The emergence of complex protein therapeutics in general and monoclonal antibodies (mAbs) in particular have stimulated analytical chemists to develop new methods and strategies for their structural characterization. Mass spectrometry plays a key role in providing information on the primary amino acid sequence, post-translational modifications, and other structure characteristics that must be monitored during the manufacturing process and subsequent quality control assessment. In this study, we present a novel method that allows structural characterization of mAbs based on MALDI in-source decay (ISD) fragmentation, coupled with Fourier transform ion cyclotron resonance (FT-ICR) MS. The method benefits from higher resolution of absorption mode FT mass spectra, compared to magnitude mode, which enables simultaneous identification of ISD fragments from both the heavy and light chains with a higher confidence in a wide mass range up to m/z 13 500. This method was applied to two standard mAbs, namely NIST mAb and trastuzumab, in preparation for method application in an interlaboratory study on mAbs structural analysis coordinated by the Consortium for Top-Down Proteomics. Extensive sequence coverage was obtained from the middle-down analysis (IdeS- and GingisKHAN-digested mAbs) that complemented the top-down analysis of intact mAbs. In addition, MALDI FT-ICR MS of IdeS-digested mAbs allowed isotopic-level profiling of proteoforms with regard to heavy chain N-glycosylation.

Innovative application of electrotechnological effect of electric corona discharge for dry agricultural products

The hypothesis of the germination enhancement of seeds if treated with corona discharge is presented here. Besides, the description of electrotechnology installation and facilities for it including manufacturing process quality obtained as a result of scientific tests are also provided in the paper. The paper overviews directions for electrotechnology treatment of grain seeds before sowing.

Application of Shewhart's control card to supervise the quality of manufacturing process in the automotive industry

The manuscript presents the use of Shewhart control charts to supervision the quality of production processes in the automotive industry of the gearbox automatic. At the request of the company, control charts were prepared for individual characteristic geometrical features. This paper presents the procedure for preparing the control card for monitoring the shape characteristics in an automated production socket.

Examination of voids and geometry of bio-based braided composite structures

Braided composites are formed by interlacing continuous fibers into a textile pre-form and then impregnating the pre-form within a matrix material. Braid mechanical properties are manipulated through the selection of matrix, fiber and braid geometry. Braided composites are produced with conventional materials like carbon, aramid and glass fibers; however, they can also be produced using natural fibers such as jute, hemp, flax or regenerated cellulose. The mechanical properties of conventional and natural braided composites are highly affected by voids within the braided structure. The effect of voids on braided structures must be investigated to improve braided composite performance. A high-resolution micro-computed tomography (μCT) measurement method was utilized to quantify the size and distribution of voids within natural fiber-bio resin braided composite structures. Image processing techniques were employed to quantify void, matrix and fiber content within 35° and 45° braid samples. Accurate quantification of fiber, matrix and void volumes are crucial for evaluating the quality and repeatability of the braided composite manufacturing process. Reduction of voids and pores will improve braided composite mechanical properties and performance. Measurement of the constituents within braided composites is also necessary for the development of accurate models for predicting braid mechanical properties. Measurement of the internal microstructure of braided composites will allow for the development of improved analytical and numerical braided composite models.