Shilong Wang

Static response of stranded wire helical springs to axial loads: A two-state model

Stranded wire helical springs are fundamental mechanical components used in high-end vibration absorption systems. The static axial response model is an important tool for the design and manufacturing of the spring. The wires within the spring have been assumed to be in contact with each other when the spring is unloaded by commonly used models for modelling the static axial response; hence, significant error has been introduced. To improve the estimation accuracy of the static axial response, this article proposes a two-state model by assuming that the spring possesses two states during the loading process. Moreover, in this model, the friction between adjacent wires is neglected and the spring is unwound to be a straight strand in the initial step of the analysis. The model is almost piecewise linear and is able to model the nonlinearity of the load–strain relationship of the spring. Adopting the proposed model, the dependence of the stiffness of the spring on the spring geometries is analysed. To evaluate the presented model, the compression experiments are carried out. Compared to a commonly used static response model, the proposed two-state model features better accuracy that is validated by the experimental results.

An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem

In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

Thermal error modelling of motorised spindle in large-sized gear grinding machine

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.

A novel monitoring method for turning tool wear based on support vector machines

Tool wear monitoring is critical for ensuring product quality and productivity. This article presents a novel tool wear prediction model based on improved least squares support vector machine method, combined with leave-one-out technique and Nelder–Mead technique. Leave-one-out is applied to tune the regularization factor and radial basis function kernel parameter of least squares support vector machine for enhancing the global search ability. Nelder–Mead is applied to raise the local search ability. The optimized least squares support vector machine based tool wear prediction model is constructed by learning the highly nonlinear correlationships between tool cutting conditions and actual tool wear. The effectiveness of the proposed prediction model is validated by experiments. Compared with particle swarm optimization algorithm-based least squares support vector machine and basic least squares support vector machine, Nelder–Mead-leave-one-out-based least squares support vector machine demonstrates a better performance in prediction accuracy, generalization, robustness, and convergence. The average accuracy obtained in tests for tool wear prediction is above 97%. This model provides theoretical basis for the machining condition configuration in the actual processing.

Cloud Manufacturing Service-Oriented Platform for Group Enterprises

The purpose of this paper is to put forward a new cloud manufacturing (CMfg) service-oriented platform for Group Enterprises (GEs), providing high-efficiency and intelligent manufacturing services by organizing isolated manufacturing resources in a collaborative manner. Relevant research has rarely focused on a general services platform and application model oriented at GEs. Incorporating the thought of cellular manufacturing, a new service-oriented platform for GEs is proposed in this paper. The core running process is critical for the development of the serviced-oriented platform. Finally, simulation by using Hypertext Preprocessor (PHP) and MySQL proved the feasibility and practicability of the platform for GEs.

A Social Sustainability Assessment Model for Manufacturing Systems Based on Ergonomics and Fuzzy Inference System

Economy, environment and society are the three pillars of sustainability. Sustainability assessment is a critical tool for analyzing and improving sustainability performance of manufacturing systems. However, most previous research has either focused exclusively on the environmental dimension, or considered the three pillars together, thus being too broad in social indicators. Research gaps exist in studies on the social dimension of sustainability. This paper presents a social sustainability assessment framework from the perspective of ergonomics. The proposed assessment framework consists of three aspects, i.e., work task, work environment and human-machine interaction. A novel weighted Mamdani fuzzy inference system (FIS) is designed to obtain a social sustainability score, which is further translated into a social sustainability index.

A linear PI cross-coupled control for servo system of CNC gear hobbing machine

The rotation mismatch between the hobbing cutter and rotary workbench in CNC gear hobbing machine will produce additional vibration during the process of gearing, thus reducing the machining precision. To achieve a better speed match, this paper proposes a synchronized control scheme based on cross-coupled control with proportional-integral (PI) compensator. Permanent-magnet synchronous motors (PMSM) in the servo system are controlled by vector control, which results in the simplification of their dynamic equations. The whole system with proposed control scheme is deduced mathematically. The simulation verifies the validity of the proposed control scheme.