Product Quality Features Research Articles

Multi-Response Optimization of Single Point Incremental Forming of Al 6061 Sheet Through Grey-Based Response Surface Methodology

The single point incremental forming process (SPIF) is materialized to form the desired shapes in low-cost sheet metal processing and well suited for low batch components and customized designs. Many modifications have been attempted in recent years to maximize the formability, geometric accuracy and quality of the SPIF formed parts. In this work, an attempt has been made to analyse the influence of process parameters namely ball tool diameter, step size, spindle speed and sheet thickness on final wall thickness, forming force and surface roughness. The optimized results exhibit the desirable formability when the roller ball tool diameter of the tool is 12 mm. The results also project that formability of the sheet metal is minimized when the spindle speed is increased and the ball diameter maximizes the accuracy and surface roughness. Also minimizing the step size increases the product quality features. Upon conducting this multi-response optimization by grey based response surface methodology (RSM) technique It is identified that 12 mm ball diameter of the tool, 0.25 mm step size, 2445 rpm spindle speed, and 2 mm sheet thickness are the obtained optimized SPIF process parameters as confirmed through confirmation experiments.

Two robust multivariate exponentially weighted moving average charts to facilitate distinctive product quality features assessment

This paper offers new multivariate statistical process monitoring schemes to study the process shift supported by a distinctive product quality features assessment. The proposed procedures help determine whether one or more features have undergone a shift or whether the shift is in the dependence structure but not in the study variables. We use the marginal distributions and pseudo copula observations to effectively apply feature-wise rank-based Lepage and Cucconi tests. The rank-based statistics induce nonparametric properties to the proposed chart. Therefore, its in-control performance is highly robust and nearly distribution-free. It is shown that the new chart gives better results for detecting scale shifts in one or more quality variables than some representative existing nonparametric charts. Some Monte-Carlo simulation studies have been performed to establish the effectiveness of the new charting scheme. A real application involving the production quality of cork stoppers is considered to illustrate the use of the proposed schemes in manufacturing and production. Some encouraging product feature assessment properties are observed.

Commercial attractiveness of Industry 4.0 technology in the opinion of customers of the young generation

Purpose: The main objective of the article is to present the results of a study on the attitude of young generation customers to Industry 4.0 technologies used in manufacturing companies and the resulting conclusions. Design/Methodology/Approach: The CAWI methodology was used in the study. The respondents were mainly students of the University of Gdańsk and the Pomeranian University in Słupsk (320) aged 18-35, as full-fledged clients of the young generation. As part of the survey, respondents explained what Industry 4.0 technology is all about. Conclusions: As the basic results of the study turned out to be consistent with the hypotheses (younger generation customers are mostly ambivalent about the technologies used in manufacturing companies), an additional study was conducted on what is important to them. This part of the study yielded very interesting results from a marketing point of view. Research limitations/implications: The main limitation is that the study was conducted on students majoring in management. Currently, the research is being continued, consisting in polling the opinions of students of humanities and engineering. Practical implications: The results of the study can be used in the practice of marketing management of enterprises. Societal implications: The results of the study will have no impact on society at large. Originality/value: The publication is addressed to academics at management and marketing faculties and practitioners of the management of manufacturing enterprises. The main value of the article is the innovative (so far unprecedented) context of deliberations on technologies included in the Industry 4.0 concept. Keywords: customers of the young generation, Industry 4.0 technologies, Industry 4.0, preferences, quality features of products. Thesis category: research paper.

An overview of material removal processes and its importance

Good surface finishing, quality, and maximizing metal removal are characteristics feature of machining operation. The process variables affect the product’s surface roughness and rate of metal removal. Proper management of the process variables makes it easier to lower machining costs and enhance product quality. In order to get the best product, extensive research has been done previously to optimize the procedure specifications in any machining process. This paper studies how important the material removal process is, its benefits, and its relevancy in the manufacturing industries. The most recent turning process research uses the Response Surface Methodology (RMS). Process variables including feed, cutting speed, and depth of cut work well. In most circumstances, a technical need for mechanical products, the surface profile, and the roughness of a machined workpiece are two of the most significant product quality features. The necessary surface quality must be obtained for a part to function as intended. Finding a simple solution and a completely reliable approach is crucial due to the surface quality mechanism’s process dependence and the various uncontrollable elements that affect relevant phenomena.

A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks

As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction.

Part II: On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha: Discussing Lower Bounds and Correlated Errors

Prior to discussing and challenging two criticisms on coefficient alpha , the well-known lower bound to test-score reliability, we discuss classical test theory and the theory of coefficient alpha . The first criticism expressed in the psychometrics literature is that coefficient alpha is only useful when the model of essential tau -equivalence is consistent with the item-score data. Because this model is highly restrictive, coefficient alpha is smaller than test-score reliability and one should not use it. We argue that lower bounds are useful when they assess product quality features, such as a test-score’s reliability. The second criticism expressed is that coefficient alpha incorrectly ignores correlated errors. If correlated errors would enter the computation of coefficient alpha , theoretical values of coefficient alpha could be greater than the test-score reliability. Because quality measures that are systematically too high are undesirable, critics dismiss coefficient alpha . We argue that introducing correlated errors is inconsistent with the derivation of the lower bound theorem and that the properties of coefficient alpha remain intact when data contain correlated errors.

Multi-stage and multi-compartment model for dynamic simulation of horizontal fluidized bed granulator

This work presents a multi-chamber and multi-compartment model of a horizontal fluidized bed apparatus for layering granulation, coating, and drying. Each chamber is designed individually and process conditions, such as spray rate, particle feed or gas temperature, can be adjusted for each chamber separately. Particle growth is described by population balance modeling. The characteristics of the particle exchange rates between the individual process chambers are determined by separation functions to account for different weir configurations. Additionally a drying model is presented that takes initial droplet size and contact angle as well as partial wetting of the particle surface into account. Because of the great variety of parameters that influence this model, a parametric study is carried out to identify how construction or process parameters affect the product quality. The influence of different separation functions, thermal process conditions, and initial droplet properties on key product quality features such as particle size distribution, product moisture content, and degree of surface wetting are presented and discussed. The developed model can be used to describe product quality and particle transport in complex horizontal fluidized bed apparatuses.

Assessing the lifetime performance index with Lomax distribution based on progressive type I interval censored sample

In manufacturing industry, the lifetime performance index is applied to evaluate the larger-the-better quality features of products. It can quickly show whether the lifetime performance of products meets the desired level. In this article, first we obtain the maximum likelihood estimator of with two unknown parameters in the Lomax distribution on the basis of progressive type I interval censored sample. With the MLE we proposed, some asymptotic confidence intervals of are discussed by using the delta method. Furthermore, the MLE of is used to establish the hypothesis test procedure under a given lower specification limit L. In addition, we also conduct a hypothesis test procedure when the scale parameter in the Lomax distribution is given. Finally, we illustrate the proposed inspection procedures through a real example. The testing procedure algorithms presented in this paper are efficient and easy to implement.

Optimization of continuous-flow heterogeneous catalytic oligomerization of 1-butene by design of experiments and response surface methodology

Oligomerization of light olefins allows the production of clean, sulphur-free fuels or fuel additives with reduced aromatics content, and useful chemicals. Commercial heterogeneous catalytic oligomerization technologies were developed to meet market demands in flexible fashions, whereby the operating conditions may be adjusted in favor of the desired product specifications and yields. For achieving superior performances, the development of expeditious evaluation and optimization tools is important, albeit not trivial because light olefins oligomerization involves complex reaction systems and product quality depends on many factors. In this sense, the design of experiments (DoE) and response surface methodology (RSM) are valuable tools, yet unexploited for light olefins oligomerization processes. In this work, DoE/RSM models were developed and validated for the oligomerization of 1-butene – derivable from fossil or renewable sources of organic carbon – over a superior hierarchical zeotype catalyst, under high pressure, continuous flow conditions, targeting clean diesel range products. The models allowed to investigate the individual and cross effects of reaction pressure, temperature and weight hourly space velocity on conversion and yields of specific product fractions. The optimization studies also accounted for product quality features such as, reduced aromatics content; these studies counted with the analytical input of Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry (GC×GC-ToFMS) for characterizing the products. Importantly, DoE/RSM tools may contemplate not only kinetic data, but also product quality indicators, and help identify most relevant process parameters impacting on the productivity.

A Dynamic Big Data Model for Quality Tracing of Steel Industries

Quality tracing is a very important issue for steel industries. Due to the complexity and variety of quality data which tends to be changed into many forms during the whole product life cycle, it is difficult to express, record and trace through the whole product life cycle from designing, manufacturing, distributing, using and disposing. In this paper, according to the Big Data theory and the analysis of the characteristics of the steel products and the feature of their manufacturing processes, a dynamic integrated Big Data model of quality for steel industries is proposed for quality tracing issues. It is a dynamically growing multi-layered structure which consists of a four phases converting procedure: 1) the basis quality bill of material(BQBOM) which represents voice of customers; 2) the general process bill of material(GPBOM) which represents the process procedures, methods, and relative parameters during the generation of the products; 3) the production and scheduling bill of material(PSBOM) which represents the production plan controls and relative quality control specifications; and 4) the final quality bill of material(FQBOM) which represents the final product quality features to be delivered to customers and users. Techniques and procedures of the realization framework is also specified in this paper. This model would be useful for developers to build a quality system in a Big Data production environment.

Quality Function Deployment in India: A Review

The term Quality Function Deployment (QFD) plays a very crucial and important role in the manufacturing sector not only from theoretical perspective but also from managerial perspective. QFD is a system for designing a product or service based on customer demands that involves all members of the producer or supplier organization. In the present scenario, automobiles are the most effective medium of transportation. QFD is a four phase model, which includes the house of quality (HOQ), parts deployment,, process planning and production planning. Among the various stages, the HOQ is most commonly used stage and its aim is to reflect customer desires (E.I.). For this, the organization conducts surveys, the research, trend research; Planning of the product also involves study of different territorial and climatic condition and other such factors. Through this, the organization will get an idea about the views and choices of their customers, opinions about the various components of the product, common complaints, etc. It is through these set of activities that the organization is able to identify specific product quality features. Apart from these error identifying techniques, a method known as Failure Mode and Effects Analysis (FMEA) is developed for preventing both part and process type failures. This research work shows how Quality Function Deployment is helpful in improving the development process with a special focus on needs of customer i.e. what features are needed by customer while using and expectations.

Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines

The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines (SVMs). Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio (SNR), can be obtained. This can guide general technological design optimization.

Reconfigured piecewise linear regression tree for multistage manufacturing process control

In a multistage manufacturing process, extensive amounts of observational data are obtained by the measurement of product quality features, process variables, and material properties. These data have temporal and spatial relationships and may have a non-linear data structure. It is a challenging task to model the variation and its propagation using these data and then use the model for feedforward control purposes. This article proposes a methodology for feedforward control that is based on a piecewise linear model. An engineering-driven reconfiguration method for piecewise linear regression trees is proposed. The model complexity is further reduced by merging the leaf nodes with the constraint of the control accuracy requirement. A case study on a multistage wafer manufacturing process is conducted to illustrate the procedure and effectiveness of the proposed method.

Motivational and Cognitive Structures of Greek Consumers in the Purchase of Quality Food Products

Following the means-end chain (MEC) analysis methodology, the study attempts to identify the psychologically-based, personal values-related motives of high-quality food purchasing in Greece. It is hypothesized that high social class consumers are quality-and health-conscious and exhibit a strong preference for high-quality foods. Olive oil is selected as the target-product of the survey, due to its importance for the everyday diet of Greeks and the agricultural economy of the country. A number of intrinsic and extrinsic quality cues are used as important product quality features. The application of the MEC methodology starts with the selection of a sample with the above-defined profile. It continues with the selection of very important olive oil quality attributes and the practical implementation of laddering-type interviews. The ultimate task of the study includes the development of sample's cognitive map, where the links between olive oil quality attributes, use benefits and consumers' values are clearly designated. The overall conclusion is that “high quality,” “healthiness/safety,” “tastiness,” “convenience” and “ethical consciousness” constitute the main motivational areas of high income and educational level consumers behind the selection of quality olive oil brands.

PH—Postharvest Technology: Evaluation of Cut Lily Flower Vase Life by Computer Vision

Computer vision systems have been used increasingly in the horticultural industry for the objective assessment of product quality features. The application of this technique to the floricultural industry is a new concept. In the current study, the use of computer vision as a means of determining the vase life of cut lily flowers was examined. Both the shape and the colour of the cut flower images captured on days 7, 9 and 11 following harvest were analysed. Consumer panel assessment of the cut flowers was also done for comparison. The experimental results show that the colour assessment by the computer vision system and consumer panel gave good agreement. Misclassification by computer vision for colour assessment was 10%. However, it was found that the image analysis of shape based on area and polar descriptors yielded poorer results because the computer vision system only analysed the shape in one direction.

The application of multidimensional analysis to assessment of the quality of food products and their packaging

The aim of this work was to analyse the effect of quality features of both products and their packaging on customer preferences. The experimental materials were different kinds of fruit juices offered by various producers. Subsequently, physico-chemical and sensory properties of the fruit juices were determined. Packaging quality was ascertained by testing its physical and mechanical features and visual evaluation of its general appearance. The results obtained in these researches were analysed by multidimensional methods (factor analysis, discriminant function analysis). These results indicate that product analysis has to comprise assessment of both food quality and attractiveness of its packaging. Sensory properties of a product and quality of its packaging are of fundamental importance, so foodstuff manufacturers should seriously consider customers' preferences in this regard. This study demonstrated the potential of multidimensional analysis (MDA) for extracting the most significant components among quality features of a product and its packaging, as well as the interrelations between them. Copyright © 2000 John Wiley & Sons, Ltd.

The application of multidimensional analysis to assessment of the quality of food products and their packaging

The aim of this work was to analyse the effect of quality features of both products and their packaging on customer preferences. The experimental materials were different kinds of fruit juices offered by various producers. Subsequently, physico-chemical and sensory properties of the fruit juices were determined. Packaging quality was ascertained by testing its physical and mechanical features and visual evaluation of its general appearance. The results obtained in these researches were analysed by multidimensional methods (factor analysis, discriminant function analysis). These results indicate that product analysis has to comprise assessment of both food quality and attractiveness of its packaging. Sensory properties of a product and quality of its packaging are of fundamental importance, so foodstuff manufacturers should seriously consider customers' preferences in this regard. This study demonstrated the potential of multidimensional analysis (MDA) for extracting the most significant components among quality features of a product and its packaging, as well as the interrelations between them. Copyright © 2000 John Wiley & Sons, Ltd.