Journal of Intelligent Manufacturing

This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising.

This paper presents a ‘tool axis vector’ approach for machining sculptured surfaces. Such an approach is well suited for highly twisted, rolled, or bent surfaces. The tool paths are generated for a 5-axis milling machine. The proposed approach is based on tilt angle, cutting direction, and a vector normal to the cutting surface. Gouging is avoided by checking the interference between the cutting tool and the part surface. The algorithm also finds maximum path intervals that generate maximum admissible cusp height within the specified tolerance limits. Such an approach minimizes the tool path and machining time. The paper presents an example to illustrate the details of the algorithm.

This paper provides an overview of the manufacturing process of fused magnesia. A brief introduction to fused magnesia and its industrial production process are presented first. In order to meet the market requirements and reduce costs, fused magnesia industrial process begins to focus on these issues: high energy consumption, serious pollution, low utilization of raw materials. So the issues related to fused magnesia are reviewed. The literature work related to the fused magnesia manufacturing process is divided into four categories: modeling, optimization, control, and experimental constraints. As can be seen, with the continuous development of intelligent manufacturing technology, fused magnesia manufacturing process begins to emerge new opportunities. Research trends and opportunities are presented in the final section, with an emphasis on future potential intelligent technologies.

While seeking for global suppliers is a general trend for lower cost and better quality, it is not trivial for a company to assess the corresponding risks in supplier selection. This paper proposes the supplier selection method that applies failures modes and effects analysis (FMEA) to assess the risks in the decision process. As each supplier is evaluated under the common multi-criteria framework, risks are viewed as the possible deviations from expected performance, and they are interpreted as failure modes in risk analysis. Following the concepts of FMEA, each failure mode is examined with respect to the possible causes and effects. This method generates two technical deliverables for supporting risk analysis. Firstly, the FMEA document is developed to support the team’s discussion of supplier risks and accumulate the risk knowledge within the company. Secondly, the ranking numbers based on FMEA (i.e., risk priority numbers) are utilized to evaluate a discount on a supplier’s performance according to their risk level. A real-case example about selecting methanol suppliers in the global market is used to demonstrate the proposed method for risk analysis in practice.

A heuristic-based optimization algorithm is proposed in this paper for on-line scheduling and assignment of preventive maintenance jobs to processors, to minimize under availability constraints, on a given time-window, the total cost of the maintenance operations of a distributed system. This algorithm minimizes the cost of discharge of preventive maintenance tasks or jobs, while assigning the tasks along with balancing the processors load. It is shown that the problem is NP-hard. To solve it, the concept of job emergency is introduced and the priority rule for total flow time (PRTF) criterion is used in an adapted heuristic job-scheduling model. In addition, the algorithm considers the constraints of precedence among consecutive standby jobs and their emergency. It is depicted the specific properties of the proposed heuristic allowing jobs scheduling in the right order. Computational results illustrate the efficiency of the approach implemented on different system configurations.

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.

In this paper, an automated vision system is presented to detect and classify surface defects on leather fabric. Visual defects in a gray-level image are located through thresholding and morphological processing, and their geometric information is immediately reported. Three input feature sets are proposed and tested to find the best set to characterize five types of defects: lines, holes, stains, wears, and knots. Two multilayered perceptron models with one and two hidden layers are tested for the classification of defects. If multiple line defects are identified on a given image as a result of classification, a line combination test is conducted to check if they are parts of larger line defects. Experimental results on 140 defect samples show that two-layered perceptrons are better than three-layered perceptrons for this problem. The classification results of this neural network approach are compared with those of a decision tree approach. The comparison shows that the neural network classifier provides better classification accuracy despite longer training times.

By complying with the operational philosophy of virtual production lines, a back-end semiconductor manufacturing system can be controlled and managed with better reconfigurability. However, due to the absence of a fully-integrated information system and the gaining popularity of distributed computing, machine reconfiguration decisions are made by machine controllers on the shop floor where heterarchical control architecture is typically used. This research investigates how non-cooperative game theory could be applied for facilitating the decision process reconfiguration decision-making at the machine controller level as machines are competed by multiple jobs streams. This paper presents how material flow traffic can be better regulated in a reconfigurable manufacturing environment. A study using an industrial pilot system is discussed to demonstrate the applicability of the proposed approach, in which heuristics are used to determine the game specification.

Performance of the process reducing the slab width in hot plate mill called edging is critical to produce rolled products with a desired dimension, which otherwise increase the yield loss caused by trimming. This process, therefore, requires a stringent width control performance. In this paper, an edger set-up model generating the desired slab width required for the control is proposed based upon the neural network approach. This neural network model accounts for variation of the dimension of incoming slabs to predict the preset value of the width as accurately as possible. A series of simulations were conducted to evaluate the performance of the neural network estimator for a variety of operating conditions needed for producing rolled products of various dimensions. The results show that the proposed model can estimate the preset value of the slab width with good accuracy, thereby enhancing the dimensional accuracy of rolled products. The estimation performance is discussed in detail for various process operation conditions.

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.