Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. SPC is based on the averages of the samples taken. In this way, not every measurement is observed, and errors in measurements or out-of-control behaviors that are not shown graphically can be hidden. This research proposes a novel segmented control chart (SCC) that considers each measurement of the samples, expressed in matrix form. The vision system technique is used to segment measurements by shading and segmenting into binary values based on the control limits of SPC. Once the matrix is segmented, the seven main features of the matrix are extracted using the translation-, scale-, and rotation-invariant Hu moments of the segmented matrices. Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. The proposal can be replicated in any production sector and strengthen the control of the sampling process.
Roberto Baeza Serrato (Fri,) studied this question.
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