Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.
Minimizing the machining cost of an individual cutting tool has become important for computer-based machining industry. This paper mathematically presents an Optimum Material Removal Control (OMRC ) Model,where the material removal rate ( MRR )i s comprehensively introduced,to accomplish the dynamic machining control and tool life determination of a cutting tool under an expected machining quantity. To resolve the incessant cutting-rate control problem,Calculus of Variations is implemented for the optimum solution. Additionally,the decision criteria for selecting the dynamic solution are suggested and the sensitivity analyses for key variables in the optimal solution are fully discussed. The versatility of this study is furthermore exemplified through a numerical illustration from the real-world industry with VISUAL BASIC. It is shown that the theoretical and simulated results are in good agreement. This study absolutely explores the very promising solution to dynamically organize the MRR in minimizing the machining cost of a cutting tool for the contemporary machining industry.
The rapid self-infiltration in the reactive Si/C, AgCuInTi braze/Si3N4, AI/TiC and other high-temperature systems indicates that dynamic contact angles could markedly influence the capillary invasion in these systems. In view of the scarcity of suitable models of the dynamic phenomenon for reactive systems, the classical models due to De Gennes, Joos, and Blake and Haynes for inert liquids were applied to the reactive capillary flows. An assessment of the model outcomes in light of published measurements on flow of Si through porous carbon points to their limitation in describing the reactive capillary flows. It is shown through analysis that the combined effect of an unstable contact angle and transitions in flow mechanisms (interface- versus diffusion-control) due to pore shrinkage (or expansion) can qualitatively explain the rather distinctive and abrupt drop in the flow velocity with increasing distance in the Si/C couples. The use of a scalar to represent the pore size distribution in real solids is, however, a major source of the discrepancy between theory and experiment.
We present a model to calculate the threshold ion bombarding energy required for overall rearrangement of target atoms in the ion-beam-enhanced deposition of carbon nitride films. This model describes the transient modification of the film-bonding configuration by successive individual ion impact events. Each ion impact is treated as a cylindrical thermal spike with a finite initial width, taking into account energy loss and energy dissipation processes. It is shown that the rearrangement of atoms during a cylindrical spike is the dominant mechanism leading to the formation of carbon nitride phase. The predicted characteristic energy range in which bombardment of the target atoms has at least one rearrangement is in good agreement with the experimental observation. The sp(3) bonding structure is dominant in carbon nitride films synthesized by ion-beam-enhanced deposition technique. It is suggested that one way to enhance the potential for formation of sp(3) configuration is to increase the thermal energy of target atoms to promote their rearrangement.
Control of flow progression to achieve a void-free mold filling is an important task in the resin transfer molding process. This paper presents an adaptive control method to control the flow front velocity by tracking a desired flow front location. The parameter estimation is used for on-line modeling. The input-output model of RTM makes use of lineal sensors for measuring the resin flow front. Several models of varying complexity are examined in this paper. The proper selection of model structure is important for the adaptive controller to achieve accurate tracking of the desired trajectory. Simulation results suggest that the control performance can be improved by selecting a proper model structure. Furthermore, it is found that nonlinear input-output models or the models with more undetermined parameters may not offer much advantage over simple linear models.
