Optical Microfiber Sensor : A Review

Due to numerous benefits such as geometrical simplicity, compact size, high sensitivity, broad detection range, low noise, and high accuracy, optical devices have attracted a lot of interest for sensing applications. It is critical in a variety of sectors, including cultural relic preservation, warehouse products maintenance, manufacturing process control, semiconductor, agriculture, food production storage, environmental control, health industries, chemical and home improvement. It outperforms its electronic equivalent owing to its capacity to function in tough and demanding situations such as combustible surroundings, greater pressure and temperature levels, and the ability to send signals over long distances without electromagnetic interference. Optical fiber sensors are classified based on their operating principles such as interferometers, fiber Bragg gratings (FBG), resonators and whispering galleries modes (WGM). This paper presents a comprehensive review related to the optical microfiber sensor such as its properties, fabrication techniques, evanescent wave, optical micro resonators and recent study on the application of microfiber towards humidity sensing. This review could be beneficial to help other researchers to gain greater view in the field of optical microfiber sensor.

Data-driven process control for manufacturing spiral bevel and hypoid gears by using design for six sigma (DFSS) considering numerical loaded tooth contact analysis (NLTCA)

Data-driven process control considering both geometric and loaded contact performance evaluations has been an increasingly important stage in field of spiral bevel and hypoid gears. A new data-driven manufacturing process control strategy is proposed for a high performance spiral bevel and hypoid gears. Here, to distinguish with the conventional simulated loaded tooth contact analysis (SLTCA) using economical finite element software package, the numerical loaded tooth contact analysis (NLTCA) is of more flexibility and practicality. In light of the advantages of the improved design for six sigma (DFSS), it is integrated with NLTCA for establishing a novel data-driven process control of gear manufacturing. Firstly, in improved DFSS framework, quality function deployment (QFD) is used to determine four sub-objective high-performance evaluation items. Then, their data-driven relationships between machine settings are respectively determined by using NLTCA. In particular, the manufacturing process control is further converted into multi-objective optimization (MOO) modification of the hypoid generator settings. Finally, an interactive preference point approach is applied for data-driven control of its iterative step and it can obtain a robust solution from Pareto optimal front. A case study is provided to verify the proposed methodology.

Robust Additive Manufacturing Performance through a Control Oriented Digital Twin

The additive manufacturing process control utilizing digital twins is an emerging issue. However, robustness in process performance is still an open aspect, due to uncertainties, e.g., in material properties. To this end, in this work, a digital twin offering uncertainty management and robust process control is designed and implemented. As a process control design method, the Linear Matrix Inequalities are adopted. Within specific uncertainty limits, the performance of the process is proven to be acceptably constant, thus achieving robust additive manufacturing. Variations of the control law are also investigated, in order for the applicability of the control to be demonstrated in different machine architectures. The comparison of proposed controllers is done against a fine-tuned conventional proportional–integral–derivative (PID) and the initial open-loop model for metals manufacturing. As expected, the robust control design achieved a 68% faster response in the settling time metric, while a well-calibrated PID only achieved 38% compared to the initial model.

A Review of Manufacturing Process Control

Smart manufacturing concepts are being integrated into all areas of manufacturing industries, from the device level (e.g., intelligent sensors) to the efficient coordination of business units. Vital components of any manufacturing enterprise are the processes that transform raw materials into components, assemblies, and finally products. It is the manufacturing process where smart manufacturing is poised to make substantial impact through process control, i.e., the intelligent manipulation of process variables to increase operation productivity and part quality. This article discusses three areas of manufacturing process control: control-oriented modeling, sensing and monitoring, and the design and construction of controllers. The discussion will center around the following manufacturing processes: machining, grinding, forming, joining, and additive. While many other important processes exist, the discussions of control of these mechanical manufacturing processes will form a framework commonly applied to these processes and the discussion will form a framework to provide insights into the modeling, monitoring, and control of manufacturing processes more broadly. Conclusions from these discussions will be drawn, and future research directions in manufacturing process control will be provided. This article acknowledges the contributions of two of the pioneering researchers in this field, Dr. Yoram Koren and Dr. Galip Ulsoy, who have made seminal contributions in manufacturing process control and continued to build the body of knowledge over the course of many decades.