More - A Focus on Esaote's World

The advanced robust process optimization techniques are widely adopted in the manufacturing field, in order to enhance the productivity and to make the process reliable by limiting the variability and the scraps.

 

More - A Focus on Esaote's World

 

Statistical methods for quality control and for higher productivity achievements, together with engineering techniques oriented to problem solving (including failure analysis, reverse engineering, electronic and acoustic microscopy and brainstorming of internal know-how), plays a key role for improving the manufacturing process in industry 4.0 scenario. For complex manufacturing such as US probes [1] becomes obvious that the improvement of process efficiency needed medium-long term, due to specific and critical phases of the production process in which different materials are involved. Furthermore, some stages are worker dependent.

By following the PDCA (Plan-Do-Check-Act) methodology it becomes strictly relevant the strenuous collection of historical (observational) data, based on processing cards and control charts. To this end, the use of M.E.S. (Manufacturing Execution System) software for internal data management allows for implementing an electronic spreadsheet, which constitutes the basic dataset. The analysis conducted on the dataset has been implemented via statistical modelling applied through a dedicated software (S.A.S. - Statistical Analysis System). The key points of the collaboration between engineers and statisticians can be outlined as in the following:

  • Identification and analysis of factors/variables that can influence the variability of the process never evaluated by the engineering;
  • Data Analysis: by considering more than 36 different factors and 38 response variables (qualitative and quantitative);
  • Distinction among systematic, noise and block effects for defining and planning the Design of Experiments [2].

The approach driven by the statistical analysis allows the engineering to distinguish the potential weak points of the manufacturing process of ultrasound (US) probes and to implement the corrective actions. In this context a method for the detection and the visualization of latent defects on US probes for medical imaging was carried out by means of NDT (Non-Destructive Testing), i.e. Scanning Acoustic Microscopy (SAM) [3]. The in-line inspection based on SAM analysis and the proposed statistical study can open a tangible and reliable contribution to the industry 4.0, in which the real time information is the key aspect for a continuous improvement.

[1] F. Bertocci, L. Francalanci, R. De Luca, M. Bassani, F. Gelli, P. Palchetti, “Design of medical ultrasound probe: Measurement system for the characterization of reverberations”, IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1-6, 2018.

[2] R. Berni, F. Bertocci, N.D. Nikiforova, G.G. Vining, “A Tutorial on Randomizing versus Not Randomizing Split-Plot Experiments”, Quality Engineering, Vol. 32, n°1, pp. 25‒45, 2020.

[3] F. Bertocci, A. Grandoni, T. Djuric-Rissner, “Scanning Acoustic Microscopy (SAM): A Robust Method for Defect Detection during the Manufacturing Process of Ultrasound Probes for Medical Imaging”, Sensors MDPI, Vol. 19, pp. 4868‒4887, 2019.

 

DOWNLAOD "Statistics for Continuous Improvement of Manufacturing Process of Ultrasound Probes for Medical Imaging" ARTICLE (PDF 4,50MB)

 


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