International Journal of Quality Engineering and Technology (6 papers in press)

Regular Issues

• Application of the AHP method for checking the quality of mechanical transmission half shaft  
  by Imane Moufid, Lagrat Ismail, Bouazaoui Oussama 
  Abstract: Nowadays, the Moroccan automobile industry is in continuous progress, automobile manufacturers are doubling their efforts in order to meet the challenge toward global competition and customer requirements. However, quality issues present a brake on the evolution. Indeed, quality plays a very important role within the company. For that, our research is oriented towards the classification of quality defects, within a Moroccan automobile manufacturer of mechanical transmission parts. Thereby, a classification method based on the analytical hierarchy process (AHP) is applied to classify these defects according to criteria chosen by stakeholders during the manufacturing process. In this context, five quality defects will be considered and four criteria will be chosen, this classification helps us to analyse the defects in order of priority. As a result, after data collection and application of the AHP method, the primary defect that requires resolution is non-uniform hardness, with a score of 70%.
  Keywords: quality; analytical hierarchy process; AHP; quality defects; automotive industry; classification.
  DOI: 10.1504/IJQET.2024.10065282
   
  
• A new rotating machinery fault diagnosis method based on data driven and expert knowledge  
  by Zhenghui Li, Na Zhang, Feiya Lv, Jingya Yang, Rui Wang 
  Abstract: This paper presents data driven and expert knowledge method for diagnosing rotating machinery fault. The belief rule-based (BRB) inference method is used to model the complex nonlinear relationship between the abnormal vibration features of rotating machinery and its fault type. If the data of all features are used for diagnosis, then computation burden will be too large to realise real-time diagnosis. First, the inputs of BRB model are reduced and weighted through neural network based on data driven algorithm. The outputs of BRB model are fault types of rotating machinery. The belief rules activated by the inputs are combined by the evidential reasoning (ER) algorithm so as to obtain the fused belief structure about the fault, and then, the accurate diagnose result can be calculated from the fused result. The diagnosis results cannot only judge the fault type, but also give the probability of potential fault. The model parameters are open and interpretable. Finally, in the experiment of fault diagnosis of motor rotor, the effectiveness of the proposed method is illustrated.
  Keywords: fault diagnosis; rotating machinery; data driven; expert knowledge.
  DOI: 10.1504/IJQET.2023.10060526
   
  
• Empirical investigation of integrated Kaizen philosophy (continuous improvement) practices application for enhancing sustainable competitiveness of manufacturing industries of Ethiopia  
  by Haftu Hailu Berhe, Hailekiros Sibhato Gebremichael, Kinfe Tsegay Beyene 
  Abstract: The purpose of this study is to investigate the combined application of Kaizen practices in selected manufacturing industries of Ethiopia. The study adopted a mixed methods research supported on survey and company observation. The findings revealed that Kaizen practices are found to be partially practiced with a minimum of 50%. Besides, by following structured framework, companies saved and gained a total of 3,175,549.39 USD, attained operational results of customer satisfaction, delivery time, defect rate, productivity, sales volume and net profits with 67.92, 33.98, 49.29, 22.9, 33.02 and 26.33% respectively. However, sustaining practices is a worry for some of the companies. Ever since, the existing practices rarely presented empirical evidence reinforcing theories pertaining to application of Kaizen practices for sustainable competitiveness, therefore, researchers realize that this is the very first research may have value for triple helix actors: manufacturing industries, institutions and government policy makers in the context of a developing country.
  Keywords: Kaizen practices; continuous improvement; integrated application; enhancing sustainable competitiveness; empirical investigation; Ethiopian manufacturing industries.
  DOI: 10.1504/IJQET.2024.10065031
   
  
• Group runs control chart approach for monitoring simple linear profiles  
  by Vikas Ghute, Onkar Ghadge 
  Abstract: In this paper, a new method based on group runs (GR) control chart is proposed to enhance the monitoring of simple linear profiles in Phase II. The proposed method is based on simultaneous use of three independent GR control charts developed to monitor intercept, slope and error variance parameters of the simple linear regression model. The individual GR control charts of the proposed method are designed by integrating each of the individual Shewhart-type chart for monitoring intercept, slope and error variance with extended version of the CRL chart and method is referred to as GR Shewhart-3 method. The performance of the proposed GR Shewhart-3 method is evaluated by average run length criterion under sustained shifts of different magnitudes in the intercept, slope and error variance. A comprehensive comparison of proposed method with a number of competing methods is incorporated in the monitoring procedure to identify whether the proposed method has better performance than its main competitors. Simulation results revealed a good performance of the proposed GR Shewhart-3 method. Finally, the implementation of the proposed method is illustrated through an example.
  Keywords: average run length; ARL; profile monitoring; GR control chart; intercept; slope; error variance.
  DOI: 10.1504/IJQET.2024.10063775
   
  
• Monitoring of Poisson multi-stage process in Phase II with Bayesian estimation of parameters  
  by Fatemeh Sogandi, Fatemeh Ebrahimi 
  Abstract: In monitoring multi-stage processes, control charts are crucial tools that should consider both inter-stage and intra-stage links. However, most researchers have only focused on the cascade property, while there are many real-world applications with Poisson-distributed random variables. This paper proposes a Phase II monitoring scheme based on a Poisson state-space model that considers non-measurable or invisible process variables as latent variables for multi-stage processes. The Bayesian algorithm is used to estimate the parameters of the proposed model. Simulation results show that the proposed GEWMA control chart performs well in single and multiple stages, with various changes in the parameters, and helps to identify the out-of-control stage too.
  Keywords: Poisson distribution; Bayesian approach; control chart; multi-stage processes; diagnosing method.
  DOI: 10.1504/IJQET.2024.10063660
   
  
• Design and optimisation of CPW antenna using machine learning algorithms  
  by M. Ravi Kishore, K. C. B. Rao 
  Abstract: In this paper, a novel design and optimisation method of coplanar waveguide-based antenna with two radiating arms surrounded by coplanar ground has been proposed. Optimisation of lengths and widths of the CPW antenna arms produce better impedance matching, better gain and multiband radiation characteristics. The optimisation of the proposed antenna is carried out with the help of familiar machine learning algorithms namely KNN, decision tree, linear regression and ridge regression. These optimisation algorithms are implemented using python programming and applied to obtain optimised dimensions on the basis of root mean square error (RMSE). The output parameters chosen for optimisation are gain and bandwidth of the antenna. The proposed antenna is simulated, optimised and analysed using high frequency structure simulator (HFSS) software. The high gain antenna can be operated for dual resonance frequencies 2.4 GHz and 5.8 GHz with optimum bandwidth with peak gain of 10 dB.
  Keywords: coplanar waveguide antenna; optimisation; machine learning algorithms; KNN; decision tree; root mean square error; RMSE.
  DOI: 10.1504/IJQET.2024.10064167