Quality issues caused by casting defects are commonly complicated to solve, because the part-specific process parameters are not traced to the individual cast part. This problem can be mitigated by the traceability of each cast part with an identifier code. Therefore, a study of the influence of marked surface topography and post-treatments on code symbol quality is desirable for a well-designed traceability system. In this work, the code symbol quality of laser, dot peen, and electrolytic marking methods on three as-cast surfaces of Al–Si alloy, after sandblasting and heat treatment, is evaluated comparatively with a customized image processing software. The result shows that the laser marking method produces the highest performance for different as-cast surfaces; electrolytic marking provides acceptable results only on the smooth surfaces of high-pressure die casting; dot peen marking produces the codes of high symbol contrasts, which are similar to those of laser marking, especially for rough as-cast surfaces of sand casting. However, for all marking methods, the code qualities of all surface topographies decrease substantially after post-treatments. Considering that dot peen marking has satisfying performances as well as low equipment and maintenance costs, this method is more suitable for small- and medium-size foundries to start to trace each cast part in an economical manner.

Permanent mold casting produces the second most aluminum cast parts among all casting processes. In this process, mold coating changes the heat transferred from the molten metal to the mold by acting as an insulating layer. Moreover, the coating thickness is a significant variable regarding the coating’s thermal resistance, which strongly influences the microstructure of cast parts and the thermal shock on expensive molds. However, in casting production, coating peeling-off and repeated recoating result in an inhomogeneous coating thickness distribution. Due to the high working temperatures of the molds, no efficient online coating thickness measurements exist. We propose an indirect evaluation method based on the as-cast surface corresponding to the coating area. Our experiments analyzed as-cast and coating surfaces at nine different coating thicknesses. The results show a close correlation between the as-cast surface roughness parameter of arithmetical mean height Sa and the coating thickness. Based on this correlation, we can derive the coating thickness from the corresponding as-cast surface analysis. Furthermore, the coating peel-off area and other casting surface defects are easily recognized in these surfaces. In our next work plan, an affordable optical camera and proper light conditions will be tested by taking photos of as-cast surfaces, and an algorithm for the real-time automatic evaluation will be developed.