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An energy-efficient intelligent manufacturing system could significantly save energy compared to traditional intelligent manufacturing systems that do not consider energy issues. Intelligent energy estimation of machining processes is the foundation of the energy-efficient intelligent manufacturing system. This paper proposes a method for machining activity extraction and energy attributes inheritance to support the intelligent energy estimation of machining processes. Fifteen machining activities and their energy attributes are defined according to their operating and energy consumption characteristics. Activities and energy attributes are extracted mainly from NC program supplemented with blank dimensional information. An effective extraction method of activities and energy attributes is the basis for the intelligent energy calculating of machining process. Based on an investigation on the extraction procedure of activities and energy attributes, energy attributes inheritance method is further discussed. Four types of energy attribute inheritance rules are summarized according to the different inheritance characteristics. Based on these four types of inheritance rules, the energy attributes can be transmitted from activity to Therblig as effective inputs of Therblig-based energy model of machining processes. The proposed methodology is finally demonstrated through two machining cases. [ABSTRACT FROM AUTHOR]

Energy efficiency has become an important factor that should be included in Intelligent Manufacturing due to the increasingly rising energy price and severe energy shortage issues. Energy demand modeling method is the foundation of improving the energy efficiency of manufacturing; therefore, an energy demand modeling methodology for machining processes is proposed. In this method, machining processes are divided into a series of activities, and Therblig, as one of the basic concepts of Motion study, is introduced to represent the basic energy demand unit. Moreover, a mathematical model of energy demand of machining processes is established by linking the activity and Therblig with machining state. Finally, case studies are performed to illustrate the validity and feasibility of the proposed methodology. [ABSTRACT FROM AUTHOR]

Robotic Minimally Invasive Surgery, and the engendered computer-integration, offers unique opportunities for quantitative computer-based surgical-performance evaluation. In this work, we examine extension of traditional manipulative skill assessment, having deep roots in performance evaluation in manufacturing industries, for applicability to robotic surgical skill evaluation. This method relies on: defining task-level segmentation of modular sub-tasks/micro-motions called 'Therbligs' that can be combined to perform a given task; and analyzing intra- and inter-user performance variance by studying surgeons' performance over each 'Therbligs'. Any of the performance metrics of macro-motions-from motion-economy, tool motion measurements to handed-symmetry-can now be extended over the micro-motion temporal segments. Evaluation studies were based on video recordings of surgical tasks in two settings: first, we examined performance of two representative manipulation exercises (peg board and pick-and-place) on a da Vinci surgical SKILLS simulator. This affords a relatively-controlled and standardized test-scenarios for surgeons with varied experience-levels. Second, task-sequences from real surgical videos were analyzed with a list of predefined 'Therbligs' in order to investigate its overall usefulness. [ABSTRACT FROM AUTHOR]