Elimination of Stray Forces from Tire Dynamics Measurements or Beware the Backpath

ABSTRACT The forces that enter the mounted tire spindle of laboratory-type tire dynamics test machines include the following items: (1) direct tire-generated forces, tire nonuniformities, and tread pattern vibrations; (2) direct tire-transmitted rough road surface or cleat impact forces; (3) direct machine resonance-amplified versions of items 1 and 2; (4) machine frame backpath-transmitted versions of items 1–3; (5) dynamic loadcell crosstalk; (6) external noise from foundation vibrations; and (7) adjacent load station vibrations traveling through the machine frame. Although items 1 and 2 are sought in spindle vibration measurements, items 3–7 are also included in the mix and confound the measurement, confusing the analyst into thinking that machine properties are tire properties. Not only do items 3–6 not exist in vehicle operation but also comparison of results from one test machine to another can be an exercise in comparing machine to machine, not tire to tire. Tire dynamics measurements should simulate tires in roadway operation, not create a whole new set of problems that do not exist in vehicles. Elimination of item 7 paved the way to developing a tire failure warning system that operates on tire endurance test machines and can be adapted for operation on passenger vehicles to warn the driver of tire trouble. This article develops the theory of stray force measurement, describes a method for eliminating stray forces from experimental tire dynamics data, and provides experimental verification of the effectiveness of these methods.

DETERMINING THE BALANCE CONFIGURATION, IN CASE OF THE OSCILLATING MOVEMENT OF THE MAIN SPINDLE AT CNC LATHE

In the paper we present a mathematical model through which are determined the balance conditions, needed for the stability analysis of the oscillating movement of the main spindle at CNC lathe. We take into account Hamilton's variation principle, the axiom of impulse derivative and the axiom of kinetic moment derivative. We present the general movement equations that generate the oscillations based on the calculus hypotheses, performing the introduction of the external solicitations. Establishment of the balance configuration is done by imposing the conditions that the system of forces that act upon the ensemble spindle – bearings - tool causes a deformation of the spindle, without producing spindle vibration. We obtain the new differential equations of the movement, in which the forces and moments are determined from the static case, based on which we can determine the integration constants in the characteristic points of the main spindle.

DETERMINING THE BALANCE CONFIGURATION, IN CASE OF THE OSCILLATING MOVEMENT OF THE MAIN SPINDLE AT CNC LATHE

In the paper we present a mathematical model through which are determined the balance conditions, needed for the stability analysis of the oscillating movement of the main spindle at CNC lathe. We take into account Hamilton's variation principle, the axiom of impulse derivative and the axiom of kinetic moment derivative. We present the general movement equations that generate the oscillations based on the calculus hypotheses, performing the introduction of the external solicitations. Establishment of the balance configuration is done by imposing the conditions that the system of forces that act upon the ensemble spindle – bearings - tool causes a deformation of the spindle, without producing spindle vibration. We obtain the new differential equations of the movement, in which the forces and moments are determined from the static case, based on which we can determine the integration constants in the characteristic points of the main spindle.

Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process

Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy.