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Manufacturing industries nowadays need to reconfigure their production lines promptly as to acclimate to rapid changing markets. Meanwhile, exercising system reconfigurations also needs to manage innumerous types of manufacturing apparatus involved. Nevertheless, traditional incompatible manufacturing systems delivered by exclusive vendors usually increase manufacture costs and prolong development time. This paper presents a novel RMS framework, which is intended to implement a Redis master/slave server mechanism to integrate various CNC manufacturing apparatus, hardware control means, and data exchange protocols through developed configurating codes. In the RMS framework each manufacturing apparatus or accessory stands for an object, and information of recognized CNC control panel image features, associated apparatus tuned parameters, communication formats, operation procedures, and control APIs, are stored into the Redis master cloud server database. Through implementation of machine vision techniques to acquire CNC controller panel images, the system effectively identifies instantaneous CNC machining states and response messages once the embedded image features are recognized. Upon demanding system reconfigurations for the manufacturing resources, the system issues commands from Redis local client servers to retrieve the stored information in the Redis master cloud servers, in which the resources for registered CNC machines, robots, and built-in accessories are maintained securely. The system then exploits the collected information locally to reconfigure involved manufacturing resources and starts manufacturing immediately, and thus is capable to promptly response to fast revised orders in a comitative market. In a prototyped RMS architecture, the proposed approach takes advantage of recognized feedback visual information, which is obtained using an invariant image feature extraction algorithm, and effectively commands an industrial robot to accomplish demanded actions on a CNC control panel, as a regular operator does daily in front of the CNC machine for manufacturing.

The research studies constituents of fatty acids (FA) in coffee beans to identify their categories. Since fatty acids are the fundamental constituents of coffee aroma and flavor, challenges occur to classify the beans' original species in the roasted state. The examined samples in this study cover 74 coffee beans from different origins and are separated into Arabica and Robusta species based on their fatty acid composition. This research develops a discriminant strategy to identify categories of examined coffee beans. This study analyzes an experimental dataset using multiple data structure strategies during the identification process, which are different from traditional approaches that aim to improve coffee bean species classification and recognition rate. Furthermore, the developed coffee bean identification strategies implement various normalization and error analyses during the data reasoning process. This research concludes that fatty acids C18:1, C18:2, and C18:3 own essential characteristics for the coffee beans. Practical applicationsThis research develops an innovative strategy to identify coffee beans in well-known categories and implements various normalization and error analyses during the data processing process. The developed model solves the information loss problem due to data normalization and improves the accuracy of coffee been classification.Consequently, the study concludes that fatty acids C18:1, C18:2, and C18:3 characterize essential features for coffee beans, and the research results balance fundamental chemistry and engineering principles and serve the general food processing and preservation technology industries.

Based upon the CANopen communication protocol and the LabVIEW graphic programing procedures, this paper develops a closed-loop control architecture for a parallel three-axis (Delta) robotic arm mechanism. The accomplishments include prototyping a parallel three-axis robotic arm mechanism, assembling servomotors with associated encoders and gearsets, coding CANopen communication scripts for servomotor controllers and a host supervision GUI, coding forward/inverse kinematics scripts to compute the required servomotor rotations and the coordinates of a movable platform or the mechanism, coding tracking error compensation scripts for effective closed-loop griper control, and coding integration scripts to command and supervise the mechanism motion on the LabVIEW-based host GUI. During the development stage, this research designed and prototyped the parallel three-axis robotic arm mechanism based upon basic Delta robot kinematics. To control the mechanism effectively and accurately, this study implemented the CANopen communication protocol, which characterizes high speed and stable transmission. The protocol applies to the CANopen communication channels among the controllers and the host supervision GUI. On the LabVIEW development platform, the coded supervision GUI performs issuing/receiving messages to the CANopen-based controllers. The controllers excite the servomotors and actuate the parallel mechanism to track prescribed trajectories in a closed-loop control fashion. Meanwhile, an electromagnet attached to the movable platform of the robotic mechanism performs satisfactory picking/placing object actions.