Research on Intelligent Manufacturing and Assembly

Probabilistic multi-objective optimization based material selection is conducted for gear manufacturing. This method incorporates the new concepts of preferable probability and total preferable probability of an alternative, which are determined by comprehensively considering all possible property responses of the alternative. Each property response of a material contributes a partial preferable probability to the alternative in a linearly correlative manner, either positively or negatively, depending on whether it is a beneficial or unbeneficial type in the evaluation. The total preferable probability of an alternative is obtained by multiplying all partial preferable probabilities. The optimal choice is the alternative with the maximum total preferable probability. In gear manufacturing material selection, five criteria are considered: core hardness, surface hardness, surface fatigue limit, bending fatigue limit, and ultimate tensile strength. Core hardness is regarded as an unbeneficial response, while the other four are beneficial. Through quantitative assessment, carburized steel is ultimately chosen as the optimal material.

Controlling chaos in recurrent neural networks (RNNs) is a crucial challenge in both computational neuroscience and artificial intelligence. Chaotic behavior in these networks can hinder stability and predictability, particularly in systems requiring structured memory and temporal processing. In this study, we apply the periodic pulse method to stabilize the dynamics of chaotic RNNs using a sinusoidal activation function. Two network configurations (2 and 3 neurons) were analyzed using numerical simulations in MATLAB. Our results show that the periodic pulse method effectively suppresses chaotic behavior, as evidenced by a reduction of the largest Lyapunov exponent from 0.317 to -0.042. The system transitions from an unpredictable regime to a stabilized fixed point. This confirms the method’s potential to regulate nonlinear neural dynamics with minimal external perturbations. Future work will focus on extending this approach to larger recurrent networks (LSTMs, reservoir computing models) and comparing its performance with other chaos control strategies such as delayed feedback and chaotic synchronization. This study contributes to the understanding of chaos in neural networks and its potential applications in neuroscience and AI.

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.

Nowadays, the symbiosis of human abilities and the mastery of artificial intelligence will contribute to increased productivity and excellence in industry and social services. The use of artificial intelligence in various fields requires standardization of the safety of its knowledge and skills. International collaboration on artificial intelligence safety standardization is expanding. The UN has created a Global Advisory Body on Artificial Intelligence to support the efforts of the international community of specialists in managing intelligent systems related to the risks and safety of their use. The author proposes international standard of safe application of ensemble intelligent interoperable agents. Ensembles of agents with artificial intelligence are multi-agent synergistic self-organizing systems that function according to the laws of development, synergy and self-organization. Ensembles of intellectual agents solve the problem in the course of self-organization and cooperation according to the criteria of preference and restriction. The solution is considered found when, in the course of their nondeterministic interactions, agents reach the best consensus (temporary equilibrium or balance of interests), which is taken as a solution to the problem. The advantages of intelligent agents that allow you to build self-organizing ensembles are especially manifested in conditions of a priori uncertainty and high dynamics of the world around you, allowing you to build adaptive ensembles with communicative abilities, rebuilding your plans for events in real time. The higher the intelligence of each agent and the richer the opportunities for communication between agents, the more complex and creative behavior the ensemble can demonstrate. The intellect of the ensemble arises and manifests itself in the process of self-organization of intellectual agents. Intelligent agents use a physical, informal and logical model of the environment. That is, they use both attributes and sets of entities, processes, relationships, etc. Modern technologies allow you to create ensembles of intelligent agents with communication abilities, characterized by high openness, flexibility and efficiency, performance, scalability, reliability and survivability, approaching the intellectual abilities of a person and professional teams in their cognitive and functional capabilities and even sometimes surpassing them.