José V. Abellán-Nebot

TL;DR: In this paper, the authors present a generic view of machining monitoring systems and facilitate their implementation, and present six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) most frequent sensory features applied in modelling machining process, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the

TL;DR: In this paper, a generic framework for machining-induced variation representation based on differential motion vectors is presented, which can be explicitly incorporated in the stream-of-variance model.

TL;DR: In this article, a methodology is proposed to facilitate the implementation of sensor-based fixtures in multi-station machining processes (MMPs), which involves three key steps: (1) an identification of station-induced variations; (2) a sensor placement optimization method for designing sensorbased fixtures; and (3) a compensability analysis.

TL;DR: In this article, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part oriented to manufacturing design activities (manufacturing and product tolerance analysis and synthesis).

TL;DR: In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts.