Yao Changfeng

Effects of cutting parameters on surface residual stress and its mechanism in high-speed milling of TB6

Formation mechanism and influence of cutting parameters on residual stress in flank milling of TB6 are investigated through orthogonal experiments. The thermal–mechanical coupling effect on forming residual stress is studied by analyzing the microstructure of metamorphic layer. Experiment results show that cutting temperature varies between 300 °C and 518 °C under experiment conditions, while residual compressive stress on machined surface is −199.8 to −41.7 MPa. Tensile residual stress occurs on subsurface when increasing milling parameters excessively. When cutting TB6 with parameters of fz = 0.02 mm/z, vc = 60 m/min, ae = 0.2 mm and ap = 10 mm, only the compressive residual stress can be detected, which is good for its fatigue life. The depth of compressive residual stress layer is 10–20 µm.

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.