To make the welding robot more reasonable and furthermore improve the productivity and reduce costs, two intelligent algorithms for welding path optimization, genetic algorithm (GA) and discrete particle swarm optimization, are proposed to optimize the welding robot path. Through the improved selection of the operator, the GA achieves the fastest iterative efficiency. By introducing the “swap operator”and “swap sequence”in the particle swarm optimization algorithm, the PSO algorithm is improved for the solution of the discrete problem (welding robot path planning) which is superior to the continuous optimization problem. Besides, for the better iterative efficiency of PSO, the parameters of traditional inertia weight are determined by a linear inertia weigh, which can improve the convergence performance of the algorithm. The modeling and solutions of the two algorithms are discussed in detail to illustrate the applications in the welding robot path optimization. In order to compare the pros and cons of the two algorithms, the same welding tasks are presented, and Matlab simulation is carried out. The simulation results show that both genetic algorithm and particle swarm optimization algorithm can obtain the optimal or near-optimal welding path by iterative calculations.

Recently, anthropocentric or human-centric approaches renewed its importance in smart manufacturing especially for assembly applications where human dexterity and informal knowledge are dispensable at present and in the near future. This paper analyzes the integration, design, and collaboration issues regarding anthropocentric researches in the past few years mainly in assembly domain. First of all, towards closed-loop system integration, the researches on integrating human in the cyber-physical system are elaborated and summarized. Then, human-centric designs (HCD) especially in shop-floor assembly field are analyzed. To emphasize human-robot hybrid assembly, several related issues including collaboration paradigm, task planning and assignment, interaction interfaces, and safety are discussed. At last, a survey on human cognitive and social limitation management is elaborated. Related research challenges and directions are discussed.

Energy efficiency is a buzzword of the 21st century. With the ever growing need for energy efficient and low-carbon production, it is a big challenge for high energy-consumption enterprises to reduce their energy consumption. To this aim, a forging enterprise, DVR (the abbreviation of a forging enterprise), is researched. Firstly, an investigation into the production processes of DVR is given as well as an analysis of forging production. Then, the energy-saving forging scheduling is decomposed into two sub-problems. One is for cutting and machining scheduling, which is similar to traditional machining scheduling. The other one is for forging and heat treatment scheduling. Thirdly, former forging production scheduling is presented and solved based on an improved genetic algorithm. Fourthly, the latter is discussed in detail, followed by proposed dynamic clustering and stacking combination optimization. The proposed stacking optimization requires making the gross weight of forgings as close to the maximum batch capacity as possible. The above research can help reduce the heating times, and increase furnace utilization with high energy efficiency and low carbon emissions.