Statistical Process Monitoring With MTConnect

2012 ◽  
Author(s):  
Sri Atluru ◽  
Amit Deshpande
Keyword(s):  
Statistical Analysis ◽  
Process Monitoring ◽  
Manufacturing Industry ◽  
Numerical Control ◽  
Cnc Machine ◽  
Statistical Process ◽  
Extensible Markup ◽  
End Mills ◽  
Communications Protocol ◽  
Statistical Process Monitoring

Statistical Process Control (SPC) techniques are used widely in the manufacturing industry. However, it is sometimes observed that a deviation that is within the acceptable range of inherent process variation does not necessarily conform to specifications. This is especially true in the case of low volume; high precision manufacturing that is customary in aerospace and defense industries. In order to study the limitations posed by conventional SPC techniques in such manufacturing environments, a study was undertaken at TechSolve Inc., Cincinnati to develop a standalone SPC tool. The SPC tool so developed effectively communicates with an on-machine probe and analyzes the collected data to carry out a statistical analysis. MTConnect, a new-generation machine tool communications protocol, was used in developing the communication interfaces with the on-machine probe on a Computer Numerical Control (CNC) machine. The XML (eXtensible Markup Language) code used to extend the MTConnect schema to include the data obtained from the probing routines is also presented. The statistical analysis was developed as a Graphical User Interface (GUI) in LabVIEW. The statistical analysis was carried out as a case study by producing a widget. Real machining was carried out to produce 48 of these widgets using a combination of end mills and face mills. The data obtained during the subsequent quality testing was used to carry out the statistical analysis. The limitations of conventional SPC techniques during the developmental and analytical phases of the study are discussed. The presence of a chip during an on machine probing routine, the variations due to disparities in tool macro geometry, and the demand for conformance to requirements are studied in the view of a statistical process monitoring standpoint. Various alternatives are also discussed that aim to correct and improve the quality of machined parts in these scenarios.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

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