Research on Intelligent Manufacturing and Assembly

Pacemakers are critical in managing cardiovascular arrhythmias, yet device malfunctions remain a significant clinical challenge, impacting patient safety and outcomes. This study presents a structured comparison of pacemaker interrogation reports from three leading manufacturers: Abbott referred to as Manufacturer A/A Devices, Boston Scientific as Manufacturer B/B Devices and Medtronic as Manufacturer C/C Devices focusing on battery performance, lead functionality, pacing modes, and arrhythmia management. By analyzing the interrogated data, device reliability, longevity, and diagnostic capabilities of the devices are understood. Data were categorized and compared with each other to assess performance trends and clinical usability. Results revealed significant variations in battery longevity, lead performance monitoring, and arrhythmia detection capabilities among the devices. Manufacturer C interrogation reports provide trend analysis and battery life management whereas Manufacturer A provide real-time diagnostics and alerts, and Manufacturer B reports demonstrated long-term stability and efficiency. The findings highlight the need for standardized reporting practices across manufacturers to enhance data consistency, comparability, and clinical utility. Such standardization would streamline clinician workflows, improve decision-making, and ultimately higher patient outcomes. This study underscores the importance of real-world data to optimize pacemaker management and calls for collaborative efforts among manufacturers, clinicians, and regulators to develop unified reporting frameworks. By integrating predictive analytics and remote monitoring capabilities, future advancements in pacemaker achieve higher patient care and device performance.

In the present article, a fuzzification measure of robust design in condition of "desired target being best" is regulated, which consists of the "complement" of the membership value of objective response and PMOO. The mean value of "complement" of the membership value of a set of test data of objective response belonging to its desired target value in fuzzification is taken as an indicator to join the assessment of the 1^st part of partial preferable probability of the objective; the dispersion of a set of test data in term of membership with regard to the desired target value is taken as the other indicator to participate the assessment of the 2^nd part of partial preferable probability of the objective. Moreover, the fuzzification measure of robust design is regulated in term of PMOO. As utilizations, two instances are presented to illuminate the regulation in design.