In the task of carrying heavy objects, it is easy to cause back injuries and other musculoskeletal diseases. Although wearable robots are designed to reduce this danger, most existing exoskeletons use high-stiffness mechanisms, which are beneficial to load-bearing conduction, but this restricts the natural movement of the human body, thereby causing ergonomic risks. This article proposes a back exoskeleton composed of multiple elastic spherical hinges inspired by the biological spine. This spine exoskeleton can assist in the process of bending the body and ensure flexibility. We deduced the kinematics model of this mechanism and established an analytical biomechanical model of human–robot interaction. The mechanism of joint assistance of the spine exoskeleton was discussed, and experiments were conducted to verify the flexibility of the spine exoskeleton and the effectiveness of the assistance during bending.

In this paper, we proposed a new hybrid approach, combining ANN and DE(Differential Evolution), for job-shop scheduling. Job-shop scheduling can be decomposed into a constraint satisfactory part and an optimization part for a specified scheduling objective. For this, an NN and DE-based hybrid scheduling approach is proposed in this paper. First, several specific types of neuron are designed to describe these processing constraints, detecting whether constraints are satisfied and resolving the conflicts by their feedback adjustments. Constructed with these neurons, the constraint neural network (CNN) can generate a feasible solution for the JSSP. CNN here corresponds to the constraint satisfactory part. A gradient search algorithm can be applied to guide CNN operations if an optimal solution needs to be found at a fixed sequence. For sequence optimization, a DE is employed. Through many simulation experiments and practical applica¬tions, it is shown that the approach can be used to model real production scheduling problems and to efficiently find an optimal solution. The hybrid approach is an ideal combination of the constraint analysis and the optimization scheduling method.

Manufacturing system is a typical complex system, while task assignment problem is an important topic in manufacturing system. It is one of the most difficult problems in the theory research for manufacturing system. In this paper, task assignment model in manufacturing system was modeled with the concept of Holonic Manufacturing System including basic system model, communication model, represent model and optimization model. Task assignment model based on operation cost and lead time is applied to cooperative activity among orders in a Holonic community. A hybrid PSO algorithm was utilized to the combination of the task assignment problem. Simulation result shows that the model and the algorithm are effective to the problem.

Today’s market dynamics have made operation process in manufacturing system extremely complex and difficult. Enterprise need to continuous adjust its strategies on evaluating and designing their task and order assignment methods to provide products at the lowest possible cost while reducing the total lead time. The evaluation model based on Holonic Manufacturing System is presented in this paper. The model incorporates operation cost and lead time as the object. A hybrid PSO algorithm with Shuffled Complex Evolution Algorithm(SCE) is utilized to compute the combination optimum for Order assignment problem . Simulation result shows that the model and the algorithm are effective to the problem.

High temperature gas turbines require ceramic abradable coatings for sealing to ensure high efficiency. In this study, a novel method is proposed to deposit porous ceramic coatings through deposition of ceramic spray powder particles in the semi-molten state. The commercially available alumina (Al2O3) powders were spheroidized and screened to a particle size range from 40 to 50 µm for spray deposition. Flame spraying was employed for coating deposition. During deposition, the substrate surface was kept at 500°C. The effect of melting degree of spray particles on coating microstructure was investigated by changing the flame power and spray distance. The porosity of flame-sprayed Al2O3 coatings was estimated by image analysis on coating cross-sectional microstructures. The results showed that porous Al2O3 coatings were successfully prepared with a porosity range up to 59% by flame spray. Moreover, spray parameters such as acetylene flow rate and spray distance have significant influences on the particle melting state, thus the microstructure and the porosity of the coating. With the decrease of acetylene flow rate and spray distance, the porosity of coatings increased due to the decrease of the melting degree of the sprayed particles. At a spray distance of 20 mm, when the acetylene flow rate was reduced from 400 to 200 L/h, the porosity increased from 37% to 59%. The results clearly demonstrated the feasibility to prepare porous abradable coatings of high porosity through surface-melted spray particles.