In this study, we investigate a production planning problem in hybrid manufacturing remanufacturing production system. The objective is the determine the best mix between the manufacturing of new products, and the remanufacturing of recovered products, based on economic and environmental considerations. It consists to determine the best manufacturing and remanufacturing plans to minimising the total economic cost (start-up and production costs of new and remanufactured products, storage costs of new and returned products and disposal costs) and the carbon emissions (new products, remanufactured products and disposed products). The hybrid system consists of a set of machines used to produce new products and remanufactured products of different grades (qualities). We assume that remanufacturing is more environmentally efficient, because it allows to reduce the disposal of used products. A multi-objective mathematical model is developed, and a non dominated sorting genetic algorithm (NSGA-II) based approach is proposed. Numerical experience is presented to study the impact of carbon emissions generated by new, remanufactured and disposed products, over a production horizon of several periods.

In the industry, automated inspection is important for ensuring the high quality and allows acceleration of procedures for quality control of parts or mechanical assemblies. Although significant progress has been made in precision machining of complex surfaces, precision inspection of such surfaces remains a difficult problem. Thus the problem of the conformity of the parts of complex geometry is felt more and more. Motivated by the need to increase quality and reduce costs, and supported by the progress made in the field of it as well as the automation of production which in recent years has seen a considerable evolution in all these stages: from design to control through manufacturing. Due to, we used a 3D computer aided inspection technique on a physical gear using a coordinate measuring machine equipped with a “PC-DMIS” measurement and inspection software. Our work consists in developing a procedure for inspection for reproduction of gear profile by reconstruction of a circle involute gear from a cloud point’s measurement. In order to obtain a reliable result. In this works, we design the CAD-model of the part as accurately as possible (using a mathematical model) and matched with the 3D points cloud that represents the measurement that obtained from scanner. we compare the measurement cloud points from coordinate measurement machine with the mathematical model of construction by ICP (Iterative Closest Point) methods in order to obtain a conformed result and to show the impact of the dimensional inspection and geometric.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

In this paper we address the integration of two-level supply chain with multiple items, production facility and retailers’ demand over a considered discrete time horizon. This two-level production distribution system features capacitated production facility supplying several retailers located in the same region. If production does take place, this process incurs a fixed setup cost as well as unit production costs. In addition, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles and routing costs are incurred. This work aims at implementing a solution to minimize the sum of the costs at the production facility and the retailers. The methodology adopted to tackle this issue is based on a hybrid heuristic, greedy and genetic algorithms that uses strong formulation to provide a good solution of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71 $ and the COE equal to 0.79 $/kWh for an LPSP = 0.01%, where the ten households are 0.99 % satisfied by renewable energy sources.

Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.

Today, and to quickly meet the high demands of variability, supply chain efficiency and energy optimization, business markets are looking for modern manufacturing technologies and as a solution, industry 4.0 is using the benefits of integrating modern manufacturing technologies and information systems to promote production capabilities. In this context, intelligent industry represents a new generation of automatic production systems based on the concepts of intelligent industry, intelligent manufacturing, control and intelligent inspection, such as inspection on coordinate measuring machines (CMMs). This technology allows many machines to be integrated into a plant and controlled online using the MBD (Model Based Design) quality system. The problem of conformity of parts with complex geometry is becoming more and more important. The objective of this work is to present a 3D inspection technique on a virtual model (MBD: Model Based Design), using a coordinate measuring machine equipped with a “POWER INSPECT” measurement and inspection software. The interest of this technique is to show the impact of the dimensional inspection and geometric tolerance process of the CAD model for the CAI (Computer aided Inspection) approach on the fidelity of the finished product for additive manufacturing (AM) including intelligent industry.

In this paper we consider infinite dimensional systems subjected to stochastic structured multiperturbations. We address the problem of robustness optimization with respect to state feedback but allow both unbounded input and perturbations. Conditions are derived for the existence of a stabilizing controller ensuring that the norm of the closed loop operator below a prespecified bound. Such controllers will be called suboptimal controllers. The suboptimality conditions are obtained in terms of a Riccati equation which satisfies an operator inequality. Finally, we give a lower bound for the supremal achievable stability radius via the Riccati equation.

This paper deals with the robust stabilization of infinite dimensional systems subjected to stochastic and deterministic perturbations. First, we give conditions providing the stability of the parameterized system. Then, we investigate the maximization of the stability radius by state feedback. We establish conditions for the existence of suboptimal controllers. Using these conditions we characterize the supreme achievable stability radius via an infinite dimensional Riccati equation.

Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.

In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

: In this paper, we have studied a different fault tolerant control (FTC) strategies for a three-phase induction motor (3p-IM). Further we introduce Backstepping controller (BC) and Input-output linearization controller (IOLC). To provide a direct comparison between these FTCs approaches, the performances are evaluated using the control of 3p-IM under failures, variable speed, and variable parameters. A comparison between the two control strategies is proposed to prove the most robust one. The simulation results show the robustness and good performance of the fault tolerant control with Input-output linearization controller compared to one with Backstepping controller. The FTC with IOLC is more stable and robust against failures, load torque perturbation and speed reversion

This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

As renewable energy elucidation, the solar refrigeration of fruits such as date palm is a storage alternate to preserve food in healthy parameters of conditioning. This statistical and numeric study investigates the energy gain cost case around the diverse dimensions’ models of positive cold stores (02, 04, and 06 cold rooms), concerning energetic disparity and numerous financial fluctuations of the applied systems. The results of computation and analysis regarding panels of construction, equipment, consumption, and maintenance for classic, absorption, and adsorption refrigeration systems that conserve dates palm into these three cold stores. In the end, the comparison of technical and economic elements in tables and figures by enumerating their advantages and inconveniences. Classic Bitzer, Absorption WFC SC 5, and Adsorption AG ACS 15 and 08 are models in which their evaluation is relating to their costs. In Biskra, these results mean that adsorption chiller termed AG ACS (15 plus 08) is illustrious by its parameters of simplicity, lifespan, safety, and security, valued to 1147.5 €/m² and median cost up ten years of using is 92972 €

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

Today, and to quickly meet the high demands of variability, supply chain efficiency and energy optimization, business markets are looking for modern manufacturing technologies and as a solution, industry 4.0 is using the benefits of integrating modern manufacturing technologies and information systems to promote production capabilities. In this context, intelligent industry represents a new generation of automatic production systems based on the concepts of intelligent industry, intelligent manufacturing, control and intelligent inspection, such as inspection on coordinate measuring machines (CMMs). This technology allows many machines to be integrated into a plant and controlled online using the MBD (Model Based Design) quality system. The problem of conformity of parts with complex geometry is becoming more and more important. The objective of this work is to present a 3D inspection technique on a virtual model (MBD: Model Based Design), using a coordinate measuring machine equipped with a “POWER INSPECT” measurement and inspection software. The interest of this technique is to show the impact of the dimensional inspection and geometric tolerance process of the CAD model for the CAI (Computer aided Inspection) approach on the fidelity of the finished product for additive manufacturing (AM) including intelligent industry.

The coflex Interlaminar Technology is an interlaminar stabilization device indicated for use in one or two level lumbar stenosis from L1-L5. It is used in skeletally mature patients with at least moderate impairment in function who experience relief in flexion from their symptoms of leg/buttocks/groin pain, with or without back pain, and who have undergone at least 6 months of non-operative treatment. Our study is focused on the evaluation and biomechanical analysis of osteosynthesis implants and in particular the Corflex-F implant to redefine a new approach to the "Coflex" interspinatus implant using particles swarm optimisation for additive manufacturing, then to study these biomechanical performances.