Laser assisted machining (LAM) is one of the most efficient ways to improve the machinability of difficultto-cut materials (e.g. Nickel-based superalloys). In the conventional LAM process, the laser beam is focused ahead of the cutting area at a fixed location, which leads to a series of restrictions, e.g. small heating area and non-uniform heat distribution due to the limitation of beam size and energy distribution. In this paper, a novel spatially and temporally (S&T) controlled laser heating method was proposed, in which a large area can be heated up with a small laser spot by controlling the beam scanning, i.e., laser power, path and speed of scanning. The laser configuration for the prescribed HAZ (heat affected zone) was achieved by solving the inverse problem where the laser power together with either laser path or laser speed were optimised to achieve a particular temperature distribution in the chip to be removed by the following milling cutter. The proposed S&T laser heating method was thoroughly validated both for the forward and, the more important, inverse heating models by performing extensive temperature experiments by both infrared thermal camera and thermocouple array and further verified by laser assisted milling (LAMill) tests of Inconel 718 for large widths of cuts. The results showed that by applying path-optimised LAMill based on the inverse solution of the thermal problem, the peak and mean principal cutting forces were reduced by 55% and 47.8% respectively compared with the conventional dry milling process while the surface roughness improved by at least 14%. Moreover, after controlling the HAZ using the inverse thermal problem, a microstructure analysis of the machined surface showed that the proposed laser heating method avoids overheating of the workpiece below the planned depth of cut for the milling operation.

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The environment can affect people’s health by relieving stress, and rural landscape as a special environment might influence human’s stress relief. This study takes different types of rural landscapes as the research object to explore their impact on stress levels, which are shown by photos. As an independent variable, the rural landscape is divided into three levels: Type 1 (natural landscape), type 2 (productive landscape), and type 3 (artificial landscape). Seventy-three subjects were randomly assigned to each type of rural landscape. Salivary cortisol, blood pressure, heart rate, and a subjective rating state scale (brief profile of mood states, BPOMS) were used as indicators of stress. At the same time, the influence of preference and familiarity on the stress relieving effect was also discussed. A paired t-test and one-way analysis of variance (ANOVA) were used as the main statistical methods. In the results of t-test for pre-posttest, significant difference was observed in high blood pressure, heart rate, and total mood disturbance (TMD) of type 1 and type 2, and the high and low blood pressure of type 3; ANOVA analysis revealed that for the difference of pre-posttest, significant difference was observed in the TMD value among the three types; except for type 3, blood pressure, heart rate, and BPOMS values were significantly affected by preference and familiarity. The conclusions include the following: The three types of rural landscapes have a positive effect on relieving stress; the productive landscape has the best effect on relieving stress; and users’ landscape preferences and familiarity with the environment can affect the effect of stress relief in rural landscapes.

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