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Under the wave of Industry 4.0, traditional pharmaceutical manufacturing enterprises are in urgent need of intelligent manufacturing transformation and upgrading, and exploring the optimal realization path of intelligent manufacturing transformation can help accelerate the transformation process of pharmaceutical manufacturing enterprises. This paper uses grounded theory research methods to conduct a multi-case study to summarize six influencing factors of the intelligent manufacturing transformation of Chinese pharmaceutical manufacturing enterprises; and then analyzes the correlation between the intelligent manufacturing influencing factors based on the DEMATEL method and the NK model, and identifies the key influencing factors according to this, and then draws the fitness landscape map of the intelligent manufacturing transformation, and finally arrives at the optimal path selection of the intelligent manufacturing transformation of pharmaceutical manufacturing enterprises. The study enriches and extends the research paradigm of intelligent manufacturing transformation, and provides lessons for pharmaceutical manufacturing enterprises to realize intelligent manufacturing transformation and upgrading. [ABSTRACT FROM AUTHOR]

With the introduction of new technologies, the scope of miniaturization has broadened. The conditions under which complicated products are designed, manufactured, and assembled ultimately influence how well they perform. The intricacy and crucial functionality of products are frequently only fulfilled through the use of high-precision components such as micro gears. In power transmission systems, gears are used in a variety of industries. Micro gears or gears with micro features, with tolerances of less than 5 μm, are pushing manufacturing processes to their technological limits. Monte-Carlo simulation methods enable an accurate forecast of inaccuracies in compliance. The complexity of the micro gear's design, on the other hand, increases the simulation computation and runtime. An alternative method for simulation is to create a surrogate model to predict the behavior. This paper proposes a statistical surrogate model to predict the conformity of a pair of micro gears. Afterward, the advantage of the surrogate model enables the optimal tolerance assignment while taking gear functionality and production cost into account. [ABSTRACT FROM AUTHOR]

Manufacturing large and/or complex structural components is today non-trivial and far-too expensive, due to limitations in the state-of-the-art production processes. Swarm robotics could then bring a different perspective and it may promise flexible, autonomous, and highly robust solutions for a large variety of applications; hence, its adoption in industry and construction may change manufacturing rules. The present contribution introduces a continuous model to capture the behavior of a swarm of manufacturing agents (e.g., drones, 3D printers, etc..) as well as a very simple, but effective, numerical implementation to simulate the evolution of such a swarm. The paper also presents one- and two-dimensional examples, showing the potentiality of the proposed approach in predicting swarm behaviors. [ABSTRACT FROM AUTHOR]