| dc.description.abstract | Sewing thread Breakage is one the leading cause for damage the garments productivity all over
the world. In Bangladesh, various factories face the similar problem. The factors that are affecting
the breakage are depended on the properties and quality of the yarn. Except for the thread quality,
some other features also involve to breakage the thread. Types of the fabric, the weight of the unit
area, the thickness of the fabric, thread count, strength, elongation % of the sewing thread and
Needle number, machine speeds, yarn tension, stitch per inch are major factors for thread breakage.
An appropriate prediction mechanism technique can apply to significantly reduce this problem. In
this work, we have proposed an intelligent system that can make an effective prediction of a
possible sewing thread breakage. Eleven (11) features and nine (09) well known supervised
machine learning algorithms we have applied to analyze the prediction accuracy percentages. The
overall process has been categorized into four stages. Stage 1: we have provided a comprehensive
literature review where we summarize various related machine learning algorithms. Stage 2:
Thread breakage data have been collected by the survey questionnaires and practical observations
from the different apparel industries of Bangladesh. Stage 3: The feature factors data have been
filtered and processing. Finally, fed the data to suitable machine learning algorithms to determine
the predictive model and accuracy percentage of sewing thread breakage. ANN (artificial neural
network) multilayer perceptron scaling accuracy specified for 11 functions, Comparative
supervised machine learning, decision tree (J48), random forest, stochastic gradient, and sequential
optimization have 100% accuracy, and with other classification algorithms such as Perceptron
Multilayer, accuracy (99.80%) and logistic regression (98, 40%). Unlike Naive Bayesian Naive
Bayesian (94%) has been updated. In Bangladesh, most companies are unaware of the cost of
refining yarn and remanufacturing productivity. The main reasons for thread cutting are the lack
of skills associated with production and inexperienced labor This intelligent system can predict the
condition of a seamstress by measuring simple information that plant personnel find useful.
Finally, it will improve productivity and the economy as a whole. | en_US |
