scholarly journals Defect Prediction For Assembled Products: A Novel Model Based On The Structural Complexity Paradigm

2021 ◽  
Author(s):  
Elisa Verna ◽  
Gianfranco Genta ◽  
Maurizio Galetto ◽  
Fiorenzo Franceschini
Keyword(s):  
Objective Assessment ◽  
Structural Complexity ◽  
Objective Evaluation ◽  
Industrial Applications ◽  
Defect Prediction ◽  
The Novel ◽  
Subjective Knowledge ◽  
Design And Optimization ◽  
Novel Approach ◽  
Novel Model

Abstract Increased assembly complexity is one of the main challenges in manufacturing. Complexity can induce increased assembly cost and time, operational issues, costly defects and quality losses. Several approaches have been proposed in the literature to predict product defects by using assembly complexity as predictor. Despite defect prediction is of utmost importance from the early stages of product and related quality inspection design, most approaches are not directly applicable because they rely on the operators' prior subjective knowledge and are designed for specific industrial applications. To overcome this issue, the present research proposes a novel approach to predict product defects from a more objective evaluation of complexity. This is one of the first attempts to predict product defects and improve its quality with a purely objective assessment of the complexity of the assembled product, without the need for operators' evaluations and assembly experience. Defect rates in the model are predicted by using a recent conceptual paradigm of complexity that considers only structural properties of assembly parts and their architectural structure. The novel model is applied to a real assembly process in the electromechanical field and is compared with one of the most accredited in the literature, i.e. the Shibata-Su model. Empirical results show that, despite the super-linear relationship between defect rates and complexity in both models, the objective approach used in the novel model leads to more accurate and precise predictions of defectiveness rates, as it does not include the variability introduced by operators' subjective assessments. Adopting this novel model can effectively improve the estimate of product defects and support designers’ decisions for assembly quality-oriented design and optimization, especially in early design phases.

scholarly journals Quality Monitoring of Assembled Products by Defect Prediction Models

2021 ◽  
Author(s):  
Elisa Verna ◽  
Gianfranco Genta ◽  
Maurizio Galetto ◽  
Fiorenzo Franceschini
Keyword(s):  
Experimental Data ◽  
Control Charts ◽  
Prediction Models ◽  
Structural Complexity ◽  
Defect Prediction ◽  
Production Volume ◽  
Industrial Case Study ◽  
Novel Approach ◽  
Low Volume ◽  
Defect Prediction Models

Abstract Typically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor Defects Per Unit (DPU) of assembled products based on the use of defect prediction models. Unlike traditional control charts requiring preliminary experimental data to estimate the control limits (phase I), the proposed DPU-chart is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is built on the structural complexity of assembled product. The novel approach may be of interest to researchers and practitioners to speed up the construction of the chart, especially in cases of low-volume productions due to the limited amount of data. The description of the method is supported by a real industrial case study in the electromechanical field.

scholarly journals Defects-per-unit control chart for assembled products based on defect prediction models

2021 ◽  
Author(s):  
Elisa Verna ◽  
Gianfranco Genta ◽  
Maurizio Galetto ◽  
Fiorenzo Franceschini
Keyword(s):  
Experimental Data ◽  
Phase I ◽  
Prediction Models ◽  
Structural Complexity ◽  
Defect Prediction ◽  
Production Volume ◽  
Industrial Case Study ◽  
Novel Approach ◽  
Low Volume ◽  
Defect Prediction Models

AbstractTypically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor defects-per-unit (DPU) of assembled products based on the use of defect prediction models. The innovative aspect of such DPU-chart is that, unlike conventional SPC charts requiring preliminary experimental data to estimate the control limits (phase I), it is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is based on the structural complexity of the assembled product. By avoiding phase I, the novel approach may be of interest to researchers and practitioners to speed up the chart’s construction phase, especially in low-volume productions. The description of the method is supported by a real industrial case study in the electromechanical field.

DC-Modulated AC/AC Converters

2013 ◽  
Vol 341-342 ◽  
pp. 1317-1325
Author(s):  
Lin Luo Fang
Keyword(s):  
Output Voltage ◽  
Input Voltage ◽  
Voltage Regulation ◽  
Industrial Applications ◽  
The Novel ◽  
Novel Approach ◽  
Regulation Method ◽  
Input Side ◽  
Voltage Frequency ◽  
Ac Converters

Traditional methods of AC/AC converters have general drawbacks: output voltage is lower than input voltage, the input side THD is poor and output voltage frequency is lower than input voltage frequency by using voltage regulation method and cycloconverters. We introduce the novel approach - DC-modulated AC/AC converters in this paper, which successfully overcomes the drawbacks. Simulation and experimental results of the DC-modulated AC/AC converter are the evidences to verify our design. These methods will be very widely used in industrial applications.

Evaluation Of The Functional Of Space-Planning Arrangement Of A Building

2019 ◽  
pp. 4-9
Keyword(s):  
Numerical Simulation ◽  
Life Cycle ◽  
Initial Data ◽  
Objective Assessment ◽  
Objective Evaluation ◽  
Point Of View ◽  
Shopping Centers ◽  
Space Planning ◽  
Planning Decisions ◽  
The Cost

The article focuses on the problem of the lack of objective evaluation of space-planning arrangement of buildings as a creative approach of the architect to the performing of functional tasks by the object. It is proposed to create a methodology for assessing the functional of space-planning solutions of buildings on the basis of numerical simulation of functional processes using the theory of human flows. There is a description of the prospects of using this method, which makes it possible to increase the coefficient of compactness, materials and works saving, more efficient use of space, reduce the cost of the life cycle of the building, save human forces and time to implement the functional of the building. The necessary initial data for modeling on the example of shopping and shopping-entertainment centers are considered. There are three main tasks for algorithmization of the functional of shopping centers. The conclusion is made about necessity of development of a method for objective assessment of buildings from the point of view of ergonomics of space-planning decisions based on the study of human behavior in buildings of different purposes.

Exploring normative perceptions and misperceptions of private, environmentally impactful behaviours

2020 ◽  
Author(s):  
Elaine Gallagher ◽  
Bas Verplanken ◽  
Ian Walker
Keyword(s):  
Social Norms ◽  
Behaviour Change ◽  
Social Contexts ◽  
The Novel ◽  
Normative Influence ◽  
Change Mechanism ◽  
Novel Approach ◽  
Classical Studies ◽  
Referent Group

Social norms have been shown to be an effective behaviour change mechanism across diverse behaviours, demonstrated from classical studies to more recent behaviour change research. Much of this research has focused on environmentally impactful actions. Social norms are typically utilised for behaviour change in social contexts, which facilitates the important element of the behaviour being visible to the referent group. This ensures that behaviours can be learned through observation and that deviations from the acceptable behaviour can be easily sanctioned or approved by the referent group. There has been little focus on how effective social norms are in private or non-social contexts, despite a multitude of environmentally impactful behaviours occurring in the home, for example. The current study took the novel approach to explore if private behaviours are important in the context of normative influence, and if the lack of a referent groups results in inaccurate normative perceptions and misguided behaviours. Findings demonstrated variance in normative perceptions of private behaviours, and that these misperceptions may influence behaviour. These behaviours are deemed to be more environmentally harmful, and respondents are less comfortable with these behaviours being visible to others, than non-private behaviours. The research reveals the importance of focusing on private behaviours, which have been largely overlooked in the normative influence literature.

scholarly journals Design and Optimization of a Centrifugal Pump for Slurry Transport Using the Response Surface Method

Machines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 60
Author(s):  
Khaled Alawadhi ◽  
Bashar Alzuwayer ◽  
Tareq Ali Mohammad ◽  
Mohammad H. Buhemdi
Keyword(s):  
Response Surface ◽  
Centrifugal Pump ◽  
Industrial Applications ◽  
Pump Efficiency ◽  
Optimized Design ◽  
Centrifugal Pumps ◽  
Local Pressure ◽  
Design And Optimization ◽  
Geometry Characteristic ◽  
Line Design

Since centrifugal pumps consume a mammoth amount of energy in various industrial applications, their design and optimization are highly relevant to saving maximum energy and increasing the system’s efficiency. In the current investigation, a centrifugal pump has been designed and optimized. The study has been carried out for the specific application of transportation of slurry at a flow rate of 120 m3/hr to a head of 20 m. For the optimization process, a multi-objective genetic algorithm (MOGA) and response surface methodology (RSM) have been employed. The process is based on the mean line design of the pump. It utilizes six geometric parameters as design variables, i.e., number of vanes, inlet beta shroud, exit beta shroud, hub inlet blade draft, Rake angle, and the impeller’s rotational speed. The objective functions employed are pump power, hydraulic efficiency, volumetric efficiency, and pump efficiency. In this reference, five different software packages, i.e., ANSYS Vista, ANSYS DesignModeler, response surface optimization software, and ANSYS CFX, were coupled to achieve the optimized design of the pump geometry. Characteristic maps were generated using simulations conducted for 45 points. Additionally, erosion rate was predicted using 3-D numerical simulations under various conditions. Finally, the transient behavior of the pump, being the highlight of the study, was evaluated. Results suggest that the maximum fluctuation in the local pressure and stresses on the cases correspond to a phase angle of 0°–30° of the casing that in turn corresponds to the maximum erosion rates in the region.

scholarly journals Experimental investigation on a novel approach for laser surface hardening modelling

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
L. Orazi ◽  
A. Rota ◽  
B. Reggiani
Keyword(s):  
Surface Hardening ◽  
Carbon Diffusion ◽  
Industrial Applications ◽  
Laser Surface ◽  
Simplified Approach ◽  
Laser Surface Hardening ◽  
Novel Approach ◽  
Laser Treatments ◽  
High Flexibility ◽  
Short Time

AbstractLaser surface hardening is rapidly growing in industrial applications due to its high flexibility, accuracy, cleanness and energy efficiency. However, the experimental process optimization can be a tricky task due to the number of involved parameters, thus suggesting for alternative approaches such as reliable numerical simulations. Conventional laser hardening models compute the achieved hardness on the basis of microstructure predictions due to carbon diffusion during the process heat thermal cycle. Nevertheless, this approach is very time consuming and not allows to simulate real complex products during laser treatments. To overcome this limitation, a novel simplified approach for laser surface hardening modelling is presented and discussed. The basic assumption consists in neglecting the austenite homogenization due to the short time and the insufficient carbon diffusion during the heating phase of the process. In the present work, this assumption is experimentally verified through nano-hardness measurements on C45 carbon steel samples both laser and oven treated by means of atomic force microscopy (AFM) technique.

scholarly journals A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI

2021 ◽  
Vol 11 (2) ◽  
pp. 674
Author(s):  
Marianna Koctúrová ◽  
Jozef Juhár
Keyword(s):  
Visual Stimuli ◽  
Computer Interface ◽  
Single Subject ◽  
Detection Accuracy ◽  
The Novel ◽  
Activity Detection ◽  
Speech Activity ◽  
Novel Approach ◽  
Eeg Data ◽  
Speech Activity Detection

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

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