Since laser technology has considerable synergy with machining technologies, Laser Machining (LM) and Laser Assisted Machining (LAM) are relevant research topics. This paper attempts to give an overview of recent developments and research trends. Although scientific work on this area has contributed to the understanding of the process, there are still unresolved problems regarding the limitations of the techniques, optimum machining conditions, etc. The outcome of experimental investigations on LAM shows potential applications for this process but there are several issues to be resolved.

A method for estimating the build-time required by the laminated object manufacturing (LOM) process is presented in this paper. The proposed algorithm -taking into account the real process parameters and the information included in the part's STL-file -performs a minimum manipulation of the file, and calculates total volume, total surface area and flat areas involved in fine cross-hatching. A number of experiments performed verify the applicability of the algorithm in process build-time estimation. The time prediction estimates are within 7.6 per cent of the real build-times for the LOM process. It is believed that, through specific minor adjustments, the algorithm could well be employed in process build-time estimation for similar rapid prototyping processes.

A mastering of surface quality issues during machining helps avoiding failure, enhances component integrity and reduces overall costs. Surface roughness significantly affects the quality performance of finished components. A number of parameters, both material and process oriented, influence at a different extend the surface quality of the finished product. Aluminium alloy 5083 component surface quality, achieved in side end milling, constitutes the subject of the present case study. The design of experiment method is employed: that is, 18 carbide two-flute end mill cutters -manufactured by a five-axis grinding machine -have been assigned to mill 18 pockets in finishing conditions -having different combinations of geometry and cutting parameters values, according to the L 18 (2 1 3 3 7 ) standard orthogonal array. Process performance is estimated using the statistical surface texture parameters R a , R y and R z -measured during three different passes on the side surface of the pockets. The results indicate that process parameters -such as the cutting speed, the peripheral second relief angle and the core diameter -mostly influence surface texture. The experimental values are used to train a feed forward back-propagation artificial neural network for the prediction of the yield surface roughness magnitude.

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