Search

Low-carbon manufacturing is an inevitable requirement for the green transformation of enterprises. For batch hobbing, continuous improvement of process parameters is an important way to achieve low-carbon optimization under the constraints of limited data and time-varying machining configurations. This is the research gap that needs to be filled. Therefore, in this paper, a dynamic modeling and continuous optimization method for comprehensive carbon efficiency (CCE) of hobbing based on data-driven discrete simulation is proposed. Specifically, the study integrates ML (meta-learning) and DEVS (discrete event system specification) in the hobbing process to create a dynamic model of CCE. The dynamic model combines the generalization of the data-driven approach and the capability to abstract events of the discrete simulation approach, which can autonomously adapt to the current machining configuration and output machining results in real time. On this basis, a modified multi-objective seagull optimization algorithm (MOSOA) is used for the continuous optimization of CCE in batch hobbing. Finally, the effectiveness and superiority of the proposed method are verified by a case study and comparative analysis. Moreover, this paper analyzes the effect of process parameters on CCE under different working conditions and provides guidance for gear hobbing.

This paper provides a case study of an assessment of an oil leaking accident site after long-term artificial interference. A cost-effective, rapid and quantitative assessment was carried out in the following order: site hydrogeology study, joint on-site geophysical prospecting (high-density electrical method and ground-penetrating radar method), borehole investigation, sample testing and integrated assessment. The results indicate that oil plumes have been transported from the leak spot to the downstream area, suggesting that surface artificial rainfall is helpful in driving the crude oil plumes. Aquifer heterogeneity dominates the behaviours of the plume movement at site scale. Two residual oil plumes were confirmed, with an estimated total volume of 325·6 m3for the contaminated soil and groundwater. At last, a site-specific joint leaching, pumping and treating method is proposed for the clean-up of the residual crude oil. This study is provided for a precise assessment prior to remediation.

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.

A large number of changeovers exist in multi-item and small lot size production mode, which lead to a long setup time and significantly influence productivity. However, setup time has received significantly less attention in previous studies regarding the single machine scheduling (SMS) problem. This paper investigates the SMS problem with sequence-dependent setup time (SDST). First, a model of SMS-SDST is developed to minimise the makespan. An earliest due date (EDD), SDST, and ant colony optimisation (ACO) heuristic algorithm is presented. In order to evaluate the performance of the proposed algorithm, Taguchi’s robust design method is employed to define the best parameter values. Related results are analysed by statistical tools. The computational experiments are compared with an ACO algorithm and a genetic algorithm. The experimental results and statistical analyses show that the proposed EDD-SDST-ACO is effective for the problem.

Computer vision–based inspection has been widely applied in automated quality inspection of lots of products. Extant research pays more attention to non-deformable parts, but little to deformable parts, such as hoses, which are popularly used in mechanical and electronic products to transport liquid because of advantages of deformability. Hence, a problem of automated inspection of hose assembly was defined and divided into four subproblems of online inspection, offline data analysis, improvement of inspection, and online quality control. Focusing on the first subproblem, a computer vision–based system was developed to deal with online inspection. Concerning the crucial unit of the system, objection recognition, features of hoses, and related fasteners were analyzed to present four propositions on shape and color constraints, and then a series of systematic techniques both in the spatial and spectral domains were put forward and discussed to perform image processing, object extraction, and feature recognition. Experiments show that average accuracies of object recognition and inspection are over 92% and even up to 100%, respectively, within an average running time of less than 30 s, which meets requirements of online inspection both on accuracy and efficiency. Since the system structure and corresponding methods are task-irrelevant, it can be generalized to figure out other inspection applications involving rotational parts.