Predictive process planning models for laser cladding and hardening applications
Journal of Physical Chemistry & Biophysics

Journal of Physical Chemistry & Biophysics
Open Access

ISSN: 2161-0398

+44 20 3868 9735

Predictive process planning models for laser cladding and hardening applications

International Conference and Trade fair on Laser Technology

July 20-22, 2015 Orlando, Florida, USA

R J Urbanic

Posters-Accepted Abstracts: J Phys Chem Biophys

Abstract :

Laser based applications are utilized in the manufacturing domain typically in the laser cutting and welding domains. Within the mould making industry, there are limited laser based manufacturing operations. Laser engraving is performed; however, with the introduction of high power diode lasers, which have lower running and maintenance costs, this situation has changed and new opportunities have emerged. There is much research and development activity related to laser cladding, and to a lesser extent, laser hardening. Laser cladding can be used to apply a coating of high strength and corrosion resistant materials for long-term reliability and performance, or it can be utilized to repair tools and moulds, turbine engine parts, etc. Laser hardening can be accurately performed on targeted areas to alter the local material hardness without masking or other pre-processing tasks. Unique process planning challenges exist for these processes, which will be discussed in this work. Unlike machining, where there are defined material libraries, process parameter ranges for cutting machine-cutting tool pairs, no coupling between the width and depth of cut, and so forth, the laser cladding/hardening applications have unique challenges to be addressed. There is significant coupling between the process parameters and output results (bead shape, hardening region), conditions change due to the operating environment (base and clad material types, laser spot size, heat profile, etc.), the bead shape changes when overlapping and stacking beads, and so forth. Much research is being performed to understand these issues, and to develop predictive models for effective process planning.