# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 1 Introduction
### Scissor Product Packaging Machining Process  
In the scissor manufacturing industry, different scissor products have different shapes. After the primary manufacturing process, a scissor product needs to be packaged using a machining device. During the forming process, Polyvinyl Chloride (PVC) and other materials can be combined to form a blister package component. The packaged part of the scissor package product includes the blister top cover, blister bottom cover, blister lining, and card. The blister packing machine is a standard package machining device used to form a blister element and heat seal it with a card. High-quality packaging protects the scissor product from abrasion. However, different scissor shapes may form blisters. The card is difficult to handle because of the blister component's rough surface. Figure 2 shows the packaging procedure. The different lidding structures require different equipment settings, such as pressure, dwell time, and seal temperature settings, in the heat-sealing process. The glue material coating weight is critical for determining the seal quality. An incorrect  
Figure 1: Procedure of a semiconductor manufacturing process. heat seal may cause package leakage, resulting in the possibility of product abrasion. The heat-seal process may eventually cause moisture to enter the package, affecting product quality. Thus, scissor products should be packaged with heat-seal coatings of appropriate thickness. During the heat-sealing process, the machine seals from the left side to the right side of the package component to maintain appropriate heat-seal coating thickness. Therefore, the scissor product package should be placed with the appropriate area between the product-containing cavities during the trimming process. The package quality should be validated through leakage testing, and any unqualified package should be withdrawn. The main factors in the packaging machining process include the heat-sealing time, cooling time, the blister bottom cover material, and the card material. This study uses equipment data such as heating time, pressure, and cooling time to predict packaging quality. Innovative IIoT-related technologies have transformed the manufacturing industry into an intelligent, dynamically scalable, and demand-driven service sector with a cost-effective business model. The application of PdM in the manufacturing industry faces a challenge in using manufacturing data across spatial boundaries. We develop an adaptive boosted decision tree algorithm for yield prediction and fault diagnosis to address these challenges. This study aims to enhance the calculation performance of ensemble-learning algorithms (ELAs). ELAs are integrated algorithms containing multiple ML algorithms. Compared to the commonly used statistical or ML model, the ensemble-learning approach enhances computation performance. Ensemble learning integrates multiple ML models to produce better predictive performance than a single ML model. The main principle behind an ensemble model is that it combines a group of weak learners to form a keen learner, thereby improving the overall performance of a learning model. This study aims to predict, detect, identify, and classify failures and degradation in the manufacturing process before criticality. This study's contribution is to prevent unqualified production, identify root causes for follow-up processes, and enable efficient evidence-based maintenance planning and manufacturing process optimization. Thus, we propose an improved ML algorithm and data analytics technologies for PdM in the manufacturing field. Our research contributions can be summarized as follows. * Develop a new approach to improve the computation performance of boosted decision trees. * Investigate the effect of ELAs and single models on manufacturing. * Forecast yield failure in the semiconductor manufacturing process. * Predict the package quality of the blister packing machine. * Provide a prediagnostic suggestion for equipment configuration to improve work efficiency.