Energy Consumption Modeling and Analyses in Automotive Manufacturing Plant

For closed loop control of machining forces in the turning process, it is well established that identification of the mechanistic force model is necessary to ensure stable operation of the process. This work proposes a novel approach to update the mechanistic force model by incorporating uncertainty in the deterministic framework. Force coefficient values reported in literature are based on wide spectrum of machining conditions and so cause difficulty in predicting the machining force using the mechanistic force model. This variability stems from variation in material workpiece input quality variation. This work proposes to treat force coefficient and process variables (shear stress and friction angles) as random variables and use Bayesian Statistical techniques to infer true distribution of force coefficients via observing cutting force and feed force values and updating shear stress and friction angle joint probability distribution. A numerical analysis is performed for calculating force coefficients for Titanium alloy (Ti6-Al4V) Markov Chain Monte Carlo (MCMC) simulation is performed to sample from the posterior distribution of the force coefficient. A single update cycle shows high reduction in the variability of the force coefficient. Numerical simulations presented indicate that it is possible to implement Bayesian update scheme in a closed loop control of cutting force for online identification of force coefficients and shear stress and friction angle distributions with few required update cycles and efficiently rejects the disturbance caused by changing machining parameters. MACHINING FORCES MONITORING AND CONTROL: A REVIEW Machining process force monitoring is valuable for tool wear state, chatter detection and overall process health. Though accurate means of measuring force with piezoelectric based dynamometers exist, they cannot be deployed in industrial environment mostly because of the inhibitive cost. Strain gauge based force sensors are relatively inexpensive, but suffer from low bandwidth because of slower response. There have been attempts to estimate the feed force by measuring feed axis motor current [1][2][3][4]. However, this method requires sweeping regions that machine will be operating in and generating a reliable model that will produce satisfactory estimation. Machining force control problem has been investigated by variety of researchers over 4 decades by now. The pioneering work in this area is done by Ulsoy, Koren and Mesory [5][6][7] which discuss about Adaptive control, variable gain control, online estimation of the parameters. Some of the control structures are discussed in this work, mainly to give idea about the approaches already taken, and what can be done to improve them. Integrator based controller based Adaptive Control Constraint (ACC) system This approach was proposed first by [6], where the feed servo dynamics are represented by a second order dynamic system. The cutting force dynamic is represented as a first order dynamic system with the time constant solely dependent upon