Hard turning has become an attractive alternative to the more time-consuming and costly grinding technique. Unfortunately, high-quality prediction of the surface roughness generated during hard turning is difficult due to the technical complexities involved. Hence, it is currently receiving much research attention. The objective of this paper is to survey the current state of soft computing techniques for surface roughness prediction in hard turning. It focuses on three areas: data acquisition, feature selection and prediction model of surface roughness. Firstly, the characteristics of hard turning and surface roughness are introduced and a framework of soft computing techniques is presented. Then, the three key areas are surveyed thoroughly. Finally, the recommendations and challenges faced by industry and academia are discussed and conclusions are drawn.

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Optimal sensor deployment is the foundation of an effective monitoring/diagnosis system design. Unfortunately, owing to the technical complexity, any issues related to optimal sensor deployment can deteriorate the performance of the sensor system, which has consequently received much attention and interest. The present study aimed to review the current literature studying optimal sensor deployment looking at modeling characteristics and strategy implementation. First, the modeling characteristics with four key elements, namely cause-effect model, optimization benchmark, optimization strategies, and performance assessment, are surveyed thoroughly. Second, a wide variety of strategy implementation of sensor deployment are discussed in detail. Finally, the open-ended issues faced by industry and academia are discussed and several principle conclusions drawn.

Purpose: The uncertainty of remanufacturing parts is a key factor affecting the quality of remanufactured products. Therefore, the purpose of this paper is to measure the uncertainty of remanufactured parts and study the coupling mechanism of reassembly quality. Design/methodology/approach: First, uncertainty of remanufactured parts is analyzed, and the uncertainty measure model for remanufacturing parts based on entropy is constructed. Second, the nonlinear mapping model between the uncertainty and reassembly quality were studied using Gauss-Newton iterative method to reveal the coupling mechanism between uncertainty of remanufacturing parts and reassembly quality. Finally, the model is verified in the reassembly process of remanufacturing cylinder head. Findings: The method can guide reassembly operations to improve the reassembly quality with uncertainty of remanufactured parts. Originality/value: This study provides practical implications by developing a multivariate nonlinear mapping model for reassembly quality based on entropy to determine the uncertainty factors that affect the reassembly quality significantly and then correct the reassembly operation to better optimize the allocation of remanufacturing production resources. The study also theoretically contributes to reveal the coupling mechanism of reassembly quality with the uncertainty of remanufactured parts.

Many condition monitoring systems based on artificial intelligence process models for machining process monitoring have been developed intensively. However, given that machining processes are very complex (i.e., nonlinear, nonstationary, etc.), there is still no clear methodology to acquire machining monitoring systems allowing machining processes to be optimized, predicted, or controlled. In this study, the coupled hidden Markov model (CHMM), based on dynamic Bayesian networks(DBNs), is proposed to monitor a machining process by using multi-directional data fusion and to analyze the effect of the sensor layout on the monitoring accuracy. The features extracted by a singular spectrum and wavelet analysis constitute the input information to the system. The technique is tested and validated successfully by using two scenarios: tool wear condition monitoring (initial wear, gradual wear, or accelerated wear) for the milling process and surface roughness accuracy grade prediction (accuracy grade 9, accuracy grade 8, or accuracy grade 7) for the turning process. In the first case, the maximum recognition rate obtained by the single-sensor placement for tool wear is 83%, whereas in the case of the three-sensor placement, the model recognition rate is 89%. In the second application for turning, the maximum recognition rate obtained by the single-sensor and the double-sensor placements for surface roughness accuracy prediction is 77% and 85%, respectively. In the case of the three-sensor placement, the model recognition rate is 89%. The proposed approach can also be integrated into the diagnosis architecture for condition monitoring in other complex machining systems.