Graphs are structures that have a long history in mathematics and have been applied in almost every scientific and engineering field. Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision. We have two goals for this article. Firstly, we make this work self-contained by reviewing the basic concepts and notations for visibility graphs in 3D space that are used process of robot laser hardening. In process of robot laser hardening we have many open problems. One of most difficul problem is how to analyze SEM images of microstructure of robot laser hardenend specimens and make prediction of topographycal properties with image properties. Second, we connect these concepts to image processing and analysis from a conceptual level and discuss implementation details. 3D visibility computations are central to any computer graphics application. Drawing graphs as nodes connected by links in 3D space is visually compelling but computationally difficult. In computational geometry and robot motion planning, a visibility graph is a graph of intervisible locations, typically for a set of points and obstacles in the Euclidean plane. Each node in the graph represents a point location, and each edge represents a visible connection between them. That is, if the line segment connecting two locations does not pass through any obstacle, an edge is drawn between them in the graph. Thus, we use method of construction of visibility graphs in 3D space in SEM images of microstructure of robot laser hardenend specimens. At the end we use method of intelligent systems to predict topographical property of robot laser hardened specimens. With intelligent system we increase production of process of laser hardening, because we decrease time of process and increase topographical property of materials.