Aprendizado de máquina para suporte à decisão do nível de abrangência de bloqueio de lotes na inspeção de saída de produtos eletrônicos

Sandro Breval  Santiago; Ayumi Santana; Luciana Rolim; Erika Nozawa

doi:10.20397/2177-6652/2026.v26i3.3451

Authors

Sandro Breval Santiago
Ayumi Santana Instituto Kodigos - IKT https://orcid.org/0009-0005-5372-4533
Luciana Rolim Instituto Kodigos - IKT https://orcid.org/0009-0009-6889-1739
Erika Nozawa Instituto Kodigos - IKT https://orcid.org/0009-0004-0947-930X

DOI:

https://doi.org/10.20397/2177-6652/2026.v26i3.3451

Keywords:

Manufatura eletrônica; Inspeção de saída; Aprendizado de máquina; Qualidade preditiva; Desbalanceamento de classes.

Abstract

Purpose: To propose a machine learning-based predictive system to support the decision on the lot-blocking scope in the outgoing inspection of an electronic manufacturing process with SMT lines, automatically classifying the most suitable containment scope for each detected nonconformity.

Methodology: An adaptation of CRISP-DM was followed, including business understanding, data analysis, target variable construction through operational rules, feature engineering, modeling and evaluation. Random Forest, XGBoost, LightGBM and SVM were compared in two approaches: training only with real data and training with selective minority-class augmentation through SMOTENC, using historical records from a factory located in the Manaus Free Trade Zone.

Originality/Relevance: The study addresses a scarcely explored practical gap: the decision on the blocking scope after defect detection, rather than defect detection itself. It also proposes a strategy to derive the target variable when the containment decision is not yet recorded in the system.

Main results: Random Forest trained only with real data achieved the best performance, with an F1-macro of 0.7937 on the test set. SMOTENC did not provide consistent gains on unseen real data, indicating that the main limitation of the system is the reduced number of defect records.

Theoretical contributions: The work transforms existing operational data into predictive support for risk containment in the SMT process and demonstrates the feasibility of deriving the target variable through operational rules, making blocking decisions more traceable, consistent and data-driven.

Author Biographies

Ayumi Santana, Instituto Kodigos - IKT

Graduada em Ciência da Computação

Universidade Federal do Amazonas

Luciana Rolim, Instituto Kodigos - IKT

Mestre em Engenharia Eletrica

Universidade Federal do Amazonas

Erika Nozawa, Instituto Kodigos - IKT

Mestre em Computação

Universidade Federal do Amazonas

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