Multiple Quality Characteristics Research Articles

A connecting rod assembly deformation cognition method based on quality characteristics probability network

As the core component of marine diesel engines, the connecting rod quality directly affects the reliability and life of diesel engines. However, the deformation problem of the connecting rod assembly process, which caused the low pass rate of one-time assembly, has not been well solved. The deformation of connecting rod assembly is difficult to analyze by mechanism because it is influenced by many quality characteristics with complex and cross-process correlations. To address this issue, this paper proposes a connecting rod assembly deformation cognition method based on the quality characteristics probability network. Firstly, a quality characteristics probabilistic network model for the connecting rod assembly process is established. Secondly, based on the quality characteristics probability network model and the complexity of cross-process correlation, the K-shell entropy weight model is used to identify the key quality characteristics and construct their correlation model. Then, to deal with the uncertainty of the influence degree between quality characteristics, a Bayesian network is used to establish an influence analysis model of key quality characteristics and analyze their influence relationship from qualitative and quantitative perspectives. Finally, the simulation and actual assembly data of marine diesel engine connecting rod assembly process are used to train and verify the cognition method. The results show that the method can effectively analyze the relationship between the connecting rod assembly process quality characteristics and predict the connecting rod deformation degree. The proposed method can comprehensively consider the influence of multiple processes and quality characteristics, improve the accuracy of analysis and prediction of connecting rod deformation, and can be used to guide the dynamic adjustment of the connecting rod assembly process to increase the pass rate of one-time. Moreover, the proposed method can also provide a reference for other multi-process and multi-factor problem analyses.

A Study of the Impacts of Measurement errors on Robust Parameter Design for Multi-response

Robust parameter design is a principle in quality improvement methodologies that is directed towards reducing the effects of errors which are either poised by the noise factors or the control factors. Response surface methodology is an effective approach to robust parameter design. Previous studies discussed Robust parameter design based on the response surface model by considering measurement errors in control variables for a single response variable. However, in process design, determining optimal levels of control variables is an important issue in some problems with different outputs. This study therefore investigates the impacts of measurement errors in the levels of control variables on processes with multiple quality characteristics (responses). Different variances of error were tested on the levels of control variables and the analysis of response surface modeling and optimization was performed. The result showed that as measurement errors in the levels of control variables increase, the coefficient of determinations for the multi-response and the expected quality loss deviates from what is obtainable in the initial state. It can be concluded based on the result however, that measurement errors in the levels of control variables exert impacts on robust parameter design for multi-response.

A multitask encoder–decoder model for quality prediction in injection moulding

It is desirable in injection moulding that every yielded product is of high quality and precision. Several studies are conducted on modelling the relationship between the process parameters e.g., holding pressure, mould temperature, etc. that are either time series or non-time series, and the output product quality characteristics e.g., weight, dimensions, and warpage. To build a more comprehensive/generalized quality prediction model with online learning, this study for the first time takes time series process parameters from 2 pairs of in-mould pressure and temperature sensors simultaneously with 18 non-time series parameters and introduces a single deep learning architecture to perform multitask learning i.e., to leverage information from the related sources to simultaneously predict multiple product quality characteristics on a real injection moulding machine. This along with the discussed modern containerization practices develops a fully automated software part of the quality control process. The non-time series process parameters are learned by fully connected neural network layers of the model while the time series process parameters are related through an attention-based encoder–decoder architecture. This model is ablated as well as compared with a benchmark model to validate the performance advantage in terms of the reduced error asymptote. The proposed model offers an order of magnitude lower average mean squared error than the benchmark model, and 1.2 to 3 times smaller mean squared errors than its ablated versions.

Optimization of multiple surface roughness characteristics of mild steel turned product using weighted principal component and Taguchi method

This paper presents a hybrid approach utilizing Principal Component Analysis (PCA) integrated with the Taguchi Method to address a multi-criteria optimization problem in the turning process of a cylindrical mild-steel workpiece using a High-Speed Steel tool. Suitable turning process parameters are identified to comply with multiple requirements and assessment of surface quality. The traditional Taguchi method has limitations in solving multi-criteria optimization problems since quality characteristics need to be uncorrelated or independent. To overcome this, PCA was employed to identify independent or uncorrelated quality indices named Principal Components. The study demonstrates the feasibility of PCA and the Taguchi method to forecast an optimal parameter setting for assessing surface quality, followed by confirmatory tests. This paper showcases the mix of PCA and Taguchi methods for multi-criteria optimization and off-line control of correlated multiple surface quality characteristics in turning processes.

Design and Development of a Symbiotic Agrivoltaic System for the Coexistence of Sustainable Solar Electricity Generation and Agriculture

The symbiotic photovoltaic (PV) electrofarming system introduced in this study is developed for the PV setup in an agriculture farming land. The study discusses the effect of different PV system design conditions influenced by annual sunhours on agricultural farm land. The aim is to increase the sunhours on the PV panel for optimized electricity generation. Therefore, this study combines the Taguchi method with Grey Relational Analysis (GRA) to optimize the two quality characteristics of the symbiotic electrofarming PV system with the best design parameter combination. The selected multiple quality characteristics are PV power generation and sunhours on farm land. The control factors include location, upright column height, module tilt angle, and PV panel width. First, the Taguchi method is used to populate a L9(34) orthogonal array with the settings of the experimental plan. After the experimental results are obtained, signal-to-noise ratios are calculated, factor response tables and response graphs are drawn up, and analysis of variance is performed to obtain those significant factors which have great impact on the quality characteristics. The experiments show that the parameters which effects power generation are: location, upright column height, module tilt angle, and PV panel width. The ranking of the degree of influence of the control factors on the quality characteristics is location > PV panel width > module tilt angle > upright column height. By controlling these factors, the quality characteristics of the system can be effectively estimated. The results for PV power generation and sunhours on farm land both fall within the 95% CI (confidence interval), which shows that they are reliable and reproducible. The optimal design parameter realized in this research obtains a power generation of 26,497 kWh and a sunshine time of 1963 h. The finding showed that it can help to build a sustainable PV system combined with agriculture cultivation.

An efficient adaptive r-MD multivariate single control chart for nonlinear multiple quality characteristics

An efficient multivariate single control chart to simultaneously monitor ‘location or mean’ and ‘scale or variabilities’, based on individual observations of multiple quality characteristics (MQC), is a continuous research endeavour. Compared to rational subgroups, time-ordered individual sample observations of MQC are common in manufacturing. However, few researchers proposed parametric and nonparametric multivariate control charts to monitor individual observations in this context. The proposed parametric approach has limited applicability due to the restrictive distribution assumption of MQC. In addition, most of the suggested parametric or nonparametric approaches recommend multiple charts to monitor process ‘location’ and ‘scale’ parameters simultaneously. Multiple charts can increase the control complexities and overall false-alarm-rate . MQC can also have significant nonlinear correlation structures and follow unknown or nonnormal distributions in real-life scenarios. Single parametric or nonparametric multivariate charts for individual observations can exhibit poor performance in such situations. In this study, an adaptive Mahalanobis depth and k-nearest neighbour (k-NN) rule-based r-AMD chart is proposed and verified to address the lacuna mentioned above. Monte-Carlo simulation studies are used to evaluate the performance of the r-AMD chart. Four real-life manufacturing scenarios are also considered to validate the suitability and superiority of r-AMD over the existing ‘r-MD’ and other competing charts.

Roll-to-roll reactive ion etching of large-area nanostructure arrays in Si: Process development, characterization, and optimization

Roll-to-roll (R2R) nanofabrication processes are recognized as key enabling-technologies for many next-generation applications in flexible electronics, displays, energy generation, storage, as well as healthcare. However, R2R processing techniques reported in the literature currently lack a scalable method of performing high-throughput nanoscale pattern transfer of geometry requiring a high degree of fidelity in terms of critical dimension resolution, etch uniformity, and aspect ratio. Reactive ion etching (RIE) addresses the need for sub-10 nm pattern transfer with large-area uniformity in wafer-scale semiconductor manufacturing, but adapting plasma etch systems for use in R2R nanopatterning has proven to be nontrivial. Moreover, robust models for simulating R2R RIE do not exist, which is an obstacle to the creation of computational approaches to design, control, and scale-up of nanoscale R2R equipment and processes. To address these challenges, we demonstrate a process flow for fabricating Si nanopillar arrays utilizing a combination of nanoimprint lithography and RIE with all pattern transfer steps performed using a R2R plasma reactor system. Specifically discussed are process development details for etching imprint resist and Si including etch rates, cross-web etch uniformity, etch directionality, and etch selectivity at varying gas chemistries, powers, and pressures. 2k full-factorial Design of Experiments (DoEs) and ordinary least-squares regression analysis are also employed to study influence of process parameters on multiple outgoing etch quality characteristics and generate stochastic models of the R2R RIE pattern transfer process into Si. Utilizing these DOE-based models and desired targets for etch quality characteristics, we describe a bounded multivariate inverse-optimization scheme for automated etch process parameter tuning. The culmination of these efforts, to the best of the authors' knowledge, is the first reported RIE-based pattern transfer of 100 nm-scale features performed in continuous R2R fashion with control of feature geometry over large area. The methodology employed herein may be applied similarly to additional materials and geometries for future applications.

PETA KENDALI MULTIVARIAT np

In general, a production process requires the control of multiple quality characteristics (variables). Multivariate control charts can be used to control many quality attributes and identify the root cause of an out-of-control condition or signal. It was used in the production of Hexagon Bolt M16x75mm. However, research regarding the theory of multivariate control charts remains limited. The np multivariate control chart is obtained by combining the processes of parameter estimate, control limit determination, and out-of-control signal detection. Keywords: Control Charts, Multivariate np, Out of Conrol Signal.

Multi-Objective Optimization of Nd: YAG Laser Drilling of Optical-Grade Acrylic Plate Using Taguchi-Based Grey Relational Analysis.

This study proposed an effective method for optimizing laser drilling processing (LDP) by using grey relational analysis (GRA) for multiple performance requirements. First, we developed a system using a Quantel Brilliant Neodymium-doped Yttrium Aluminum Garnet (Nd: YAG) laser with a pulse width of 5-6 ns and F-theta lenses to deliver a focused laser beam with a diameter of 0.2 mm. The developed system was first employed to drill holes in a 3-mm-thick optical-grade acrylic polymethyl methacrylate (PMMA) plate on a safe window with a high optical density and a grade of OD 7+ @ 950~1085 nm. To avoid errors in the experimental data due to unstable power, a laser power (energy) meter was used to measure the energy stability of the Quantel Brilliant Pulse Laser. Given the stability of 5.6%, this is an effective method for LDP. Four control factors were investigated, including laser pulse energy, repetition rate, focusing position offset, and drilling time. Then, nine experiments were performed using the Taguchi method with orthogonal arrays in L9 (34). The experimental results with multiple quality characteristics were measured and used to optimize the control factors by using GRA with equal weighting of the four qualities (roundness, Hillock ratio, taper, and HAZ). The results show that A1B3C1D1 is the optimal combination of the control factors, and the maximal variation of 0.406 is obtained from the control factor B (focusing position offset) which has the greatest contribution to the drilling time. We then performed confirmation experiment and obtained a better result from the combination of the control factors, A1B3C1D1. GRA helps us determine the best laser drilling parameters to meet the desired multiple drilling qualities.

Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.

Application of integrated CRITIC and GRA-based Taguchi method for multiple quality characteristics optimization in laser-welded blanks

There is a need to study the multi-objective optimization technique as the appropriate method to enhance welding qualities under optimal process conditions. Therefore, this study investigated the application of the integrated Criteria Importance Through Intercriteria Correlation (CRITIC), which is an objective weighting technique, and the Grey Relational Analysis (GRA)-based Taguchi method to solve multiple criteria optimization problem in the Nd:YAG laser welding process. The Taguchi-based L36 orthogonal array table was employed in this study to optimize six process parameters, including the beam diameter, laser power, flow rate, welding speed, laser offset, and pulse shape, with the aim to simultaneously achieve maximum weld strength and minimum weld width. The base metal JIS G3141 SPCC steel with 0.5 and 1.0 mm thicknesses was used in the present experiment. Following the welding process and optimization, the weld strength was measured using a Cometech QC-506M1 universal testing machine, while the weld width was determined under a Nikon SMZ25 stereomicroscope. Based on the results, the weight fractions of the weld strength and weld width from the applied CRITIC method were equal to 0.4157 and 0.5843, respectively. Meanwhile, the GRA revealed that the process parameters recorded an optimal setting for beam diameter of 0.8 mm, flow rate of 8 L/min, laser power of 0.6 kW, welding speed of 2.5 mm/s, laser offset of 0.2 mm, and pulse shape I. Furthermore, the weld strength and the weld width were enhanced from 236 to 328 MPa and from 1.13 to 1.04 mm, respectively. Additionally, the Analysis of Variance (ANOVA) indicated that the laser power and welding speed were the most influential parameters on the welding qualities. Most importantly, the findings of the confirmation experiment showed that the proposed approach was able to effectively identify the optimal laser welding parameters, which ultimately improved the multiple quality characteristics.

Quality-Based Supplier Selection Model for Products with Multiple Quality Characteristics

The concept of Industry 4.0 was first proposed by the German government in 2011. As the Internet of Things (IoT) becomes more prevalent and big data analysis technology becomes more mature, it is beneficial for the manufacturing industry to integrate and apply the related technologies to pursue the goal of smart manufacturing. Taiwan’s machine tool industry and downstream machine-tool purchasers, who are scattered around the world, have formed a machine-tool industry chain. To help the machine-tool industry and the suppliers of important components boost their process capabilities, ensure the final product quality of machine tools and improve the process capabilities of the entire industry chain, this study used radar charts to present the statistical testing information of the process capabilities of all quality characteristics, so that managers could have more complete information when evaluating and selecting appropriate suppliers. As noted in many studies, improving product quality and availability can reduce not only the rate of reworking and scrappage during production but also the frequency of maintenance or replacement of components after purchase. As a result, the loss of costs caused by reworking, scrappage, and maintenance can be diminished, carbon emissions can be lowered, and environmental pollution can be reduced as well, which will help to achieve sustainable operation in the entire machine tool industry chain.

Fuzzy process capability analysis for machined product with multiple characteristics of symmetric tolerance

Amidst fierce competition and diversifying customer needs in today’s markets, product development and design have shifted toward a more user-oriented model. This makes it common for products to have multiple quality characteristics. However, simply meeting customer needs is no longer sufficient; quality is also a key factor of a consumer’s willingness to buy a product. To this end, process capability indices have been used to measure the relationship between manufacturing specifications and processing performance, as well as serve as a bridge of communication between manufacturers and clients. In view of this, the loss-based capability index [Formula: see text] that fully reflects process loss and yield is employed in this study to analyze the process performance of each quality characteristic. However, [Formula: see text] must be estimated based on collected samples, in which the measured values of all sample data are expressed with precise values. Uncertainty and imprecision in collected data increase the risk of misjudgment. To reduce this risk, the [Formula: see text] confidence interval of [Formula: see text] is first derived to define fuzzy estimations of both the critical value and index, and develop a fuzzy process capability analysis model for a machined product with multiple quality characteristics of symmetric tolerance. Finally, an industrial example involving a five-way pipe product is presented to illustrate the applicability of the proposed approach. The results show that the proposed fuzzy analysis model makes determination of the process capability of each quality characteristic more reliable and rigorous to ensure that manufactured products meet requirements.

A scenario-based desirability function for correlated multi-response optimization problems considering modeling and implementation errors

Simultaneous improvement of multiple quality characteristics is one of the main concerns in today's complex industrial systems. Multiresponse optimization enables us to address this concern and find a compromise solution of design factors that leads to desirable quality outputs. This problem becomes complicated when some degree of uncertainty affects the result of the experiments. In this paper, two sources of uncertainty are included in multi-response optimization problems. The first is experimental errors related to unknown variables, also called environmental nuisances, creating response surface modeling errors. The second source is errors in implementation. In the experimentation, the controllable factors are mainly set at some predefined levels, but after the optimal design has been found, it oscillates around that value during the implementation phase. To handle this problem, a scenario-based two-stage stochastic programming is developed in which desirability functions are aggregated to form the final objective function. Scenarios are generated by sampling from probability distribution functions of the response surfaces' coefficients under normality assumptions. The numerical results show that joint consideration of the error sources leads to a smaller range of desirability values among the different scenarios and a higher value of the total desirability index.

The role of quality characteristics in pricing hard red winter wheat

This study empirically examines the role of conventional and end-use wheat quality characteristics as well as transportation costs on the pricing of U.S. hard red winter wheat (HRWW) utilizing a hedonic price analysis framework, detailed quality characteristic data as well as location matched price data. We find evidence that multiple conventional quality characteristics and multiple end-use quality characteristics (milling and baking) have a statistically significant and economically important effect on the price of U.S. HRWW. This evidence suggests that, although U.S. HRWW producers are not directly paid premiums or discounts for end-use quality characteristics, they can be paid indirectly through regional HRWW prices. We also evaluate the role of information about conventional and end-use quality characteristics on price as harvest progresses. Results suggest that as harvest progresses, prices are updated to reflect changes in wheat quality. Results suggest revisions to U.S. policy on standard grade factors to improve transparency between characteristics farmers are paid for on grade sheets and characteristics millers and bakers value.

Bearing Fault Diagnosis Method Based on Multidomain Heterogeneous Information Entropy Fusion and Model Self-Optimisation

Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.

Quick‐switch inspection scheme based on the overall process capability index for modern industrial web‐based processing environment

AbstractToday, in Industry 4.0 and sensor‐rich environments, web‐based processing, and cloud computing have played significant roles in the quality systems designed to ensure product quality and reliability. Among quality validation technologies, variables acceptance sampling plans are essential tools in supply chain channels, where the variables quick‐switch sampling (VQSS) system is among the efficient schemes. The VQSS system comprises normal and tightened inspections that can provide a dynamic adjusting mechanism for lot disposition. Recently, the VQSS system regulated on process capability indices (PCIs) has been developed to integrate consumers' requirements into manufacturing processes. Unfortunately, the existing PCI‐based VQSS systems only consider a single, critical quality characteristic of the product. With advanced sensor technologies, cost‐efficiently measuring multiple quality characteristics (MQCs) has become a reality in today's manufacturing environments. Therefore, in this article, we emphasize developing the VQSS system with MQCs according to the overall PCI for lot disposition. By comparing the existing sampling plans, the proposed overall PCI‐based VQSS system can reduce the required sample size significantly. The results also demonstrate that the proposed system can accommodate both normal and tightened inspections' strengths to obtain a superior lot‐discrimination power. Furthermore, we have developed web‐based architecture and tools for practitioners to execute our proposed system efficiently in the cloud. They can be used to construct automation and semi‐automation applications for the lot disposition, which is appropriate in Industry 4.0 environments. Finally, a case of ultra‐mini chip resistors is illustrated to demonstrate the applicability of our designed VQSS system.

Aggregation and Size Attributes Analysis of Unadsorbed and Adjuvant-adsorbed Antigens using a Multispectral Imaging Flow Cytometer Platform

Various vaccine quality attributes should be monitored to ensure consistency, potency, purity, and safety of vaccine products prior to lot release. Vaccine particle size and protein antigen aggregation are two important considerations for particle-adsorbed vaccines. In this study, we evaluated the use of imaging flow cytometry as a potential all-in-one platform to measure adjuvant particle size and to detect protein aggregates through a combination of brightfield microscopy, side scatter detection, and fluorescence microscopy. An aluminum phosphate adjuvant was analyzed for size using the brightfield function, and the size measurement was compared against laser diffraction. Heat-induced protein aggregates of either unadsorbed antigens or aluminum phosphate adjuvant-adsorbed antigens were stained with the fluorescent ProteoStat aggregation dye, followed by detection and analysis using a combination of the brightfield and fluorescence microscopy functions. The change in aggregation of unadsorbed antigens was confirmed using dynamic light scattering. These results demonstrate the versatility of the imaging flow cytometry platform for the evaluation of multiple vaccine quality characteristics.

Optimization of multiple quality characteristics for end milling under dry cutting environment using desirability function

Milling AA6082T6 materials is a difficult venture because of their heterogeneity and a slew of problems, inclusive of surface roughness, that get up for the duration of the machining method and are connected to the material’s homes and slicing settings. The optimization of machining parameters is a crucial section inside the manufacturing method. This research introduces a unique approach for improving machining settings whilst milling aluminum alloy. A technique notorious as desirability function analysis (DFA) turned into worn to optimize machining parameters. DFA is a effective tool for optimizing multi-reaction problems. Milling research for aluminum alloy were completed using tungsten carbide end milling inserts in dry situations, based totally on Taguchi’s L9 orthogonal array. Multi-response issues, along with machining pressure and surface roughness, are used to optimize machining parameters including feed charge, spindle speed, and depth of reduce. person desirability values from the desirability characteristic analysis are used to create a composite desirability cost for the multi-responses. The most effective ranges of parameters had been discovered based at the composite desirability fee and substantial contribution of parameters has been determined the usage of analysis of variance.

Analysis of tribological behaviour of aluminum‐titanium carbide‐basalt metal matrix composite

AbstractIn the recent years, particulate reinforced aluminum based matrix composites are playing an important role in automobile and aerospace applications due to their enhanced properties. In this work, an attempt has been made to optimize the tribological behaviour of aluminum 7075 matrix reinforced with titanium carbide (3 percent weight) and basalt particles (2 percent weight) using Taguchi based grey relational analysis. Composites are fabricated according to american society for testing materials standard using stir casting method dry sliding wear tests were carried out using pin on disc apparatus as per Taguchi's L9 orthogonal array. Grey relational analysis was used to obtain the optimum process parameters for multiple quality characteristics such as wear rate and coefficient of friction. Then significant contribution of wear parameters was determined by analysis of variance. A confirmatory test was carried out to validate the test result. Finally, the micro structural investigation on the worn surfaces was performed by scanning electron microscope.