Abstract
A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system-state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the deoxyribonucleic acid, repeatable and predictable. The process genes are the quanta of information that aligns to represent a chain of activities (process). They describe the causal links between occurring events forming a pattern (pathway) that leads to a well-specified output (e.g., a product with a defect or otherwise). The creation of process genomics requires the knowledge of system observed or latent parameters (state) as well as the state change at specified time intervals (discretization). The process genomics theory is tested in an industrial case study for quality assessment and control of glue dispensing in micro-semiconductor manufacturing. The resulting definitions of the system state and interrelationship of control parameters contribute to the development of the process genes. The outcome of the gene alignment is the geometric interpretation of the glue droplet formation. A predicted or observed droplet within the production tolerance leads to a nondefective product. The principle of creating production genomics is to find and rectify the defect-causing genes or to disrupt the sequences that lead to producing defective products, leading to a zero-defect manufacturing process.
Highlights
S YSTEMS equipped with sensory inputs should be able to recognize and predict temporal sequences of events
The results show that both types of errors in the Random forest (RF) model are less than k nearest neighbors (kNN) and multilayer perceptron (MLP) models in this experiment
The challenges posed by the complexity and timing demand of the case study motivated us to go beyond the existing ML and AI techniques that could not offer satisfactory results on their own
Summary
S YSTEMS equipped with sensory inputs should be able to recognize and predict temporal sequences of events. A functional protein is searchable by codons start and stop points in a DNA sequence This is important in gene prediction because it can reveal where coding genes are in an entire genomic sequence. In this example, a functional protein can be discovered using ORF3 because it begins with a start codon, has multiple amino acids, and ends with a stop codon, all within the same reading frame. The genetic register of the process will be saved in a gene/DNA of the process library, where the “good genes” (optimum solution creating events) and “bad genes” (fail conducing events) and the sequence of their occurrences will be registered and used for optimization purposes by encouraging good genes and eliminating bad genes In the latter case, by adjusting machines, material, logic, etc., to prevent/avoid the occurrence of bad genes (e.g., defect inducing events). The comparison helps the authors to explain the merits and applicability of the proposed method for problem solving in real-world industrial applications
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