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A modular fixture system is a flexible alternative to a dedicated fixture. In this paper, an automatic modular fixture design system is suggested for creating prismatic parts. The system contains fixture planning, fixture modelling, and fixture assembly. The part geometrical database and process plans are considered as inputs to the system. A rule- based method is applied to assign the feasible locating scheme and the locating and clamping datum. The search strategy is designed to determine the most suitable locating and clamping points. The final fixture assembly is made with the standard fixture components using the CATVBA Editor inside CATIAV5. The proposed methodology is successfully validated with a case study.

Fixtures are critical part of both traditional and modern manufacturing operations. The time allocated to the design and manufacture of these components significantly impacts production cycle time and can improve the quality of current and future products, and therefore, significant consideration is required to design fixture and their applications in manufacturing. In this article, design of an automatic modular fixture modeling system is proposed for prismatic components. The proposed system consists of fixture planning, fixture layout, and fixture assembly modules. The process and setup plans are used as input into the system. A rule-based system is developed to assign suitable locating scheme to locate and clamp datum for fixture planning phase. The search strategy is designed to select the locating and clamping points based on geometric and functional constraints. The final fixture assembly is achieved using standard fixture components and CATVBA editor using CATIA V5 as the platform. The graphical fixture database is constructed using commercial fixture components from manufacturers such as Fixture Works and Carrlane. The proposed methodology is successfully validated with multiple case studies. [ABSTRACT FROM AUTHOR]

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant's generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications. [ABSTRACT FROM AUTHOR]