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The purpose of this paper is to increase the reliability of manufactured electronics and to reveal reliability significant factors. The experiments were focused especially on the influence of the reflow oven parameters presented by a heating factor.

The shear strength of the surface mount device (SMD) resistors and their joint resistance were analyzed. The resistors were assembled with two Sn/Ag/Cu-based and one Bi-based solder pastes, and the analysis was done for several values of the heating factor and before and after isothermal aging. The measurement of thickness of intermetallic compounds was conducted on the micro-sections of the solder joints.

The shear strength of solder joints based on the Sn/Ag/Cu-based solder alloy started to decline after the heating factor reached the value of 500 s · K, whereas the shear strength of the solder alloy based on the Bi alloy (in the measured range) always increased with an increase in the heating factor. Also, the Bi-based solder joints showed shear strength increase after isothermal aging in contrast to Sn/Ag/Cu-based solder joints, which showed shear strength decrease.

The interpretation of the results of such a comprehensive measurement leads to a better understanding of the mutual relation between reliability and other technological parameters such as solder alloy type, surface finish and parameters of the soldering process.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.