Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0.

MATLAB® has built in five ‘derivative-free optimizers’ (DFOs), including two ‘direct search’ (simplex search, pattern search) and three heuristic algorithms (simulated annealing, particle swarm optimization, and genetic algorithm), plus a few in the official user repository, such as Powell’s conjugate (PC) direct search recommended by MathWorks®. To help a practicing engineer or scientist to choose a MATLAB DFO most suitable for their application at hand, this paper presents a set of five benchmarking criteria for optimization algorithms and then uses four widely adopted benchmark problems to evaluate the DFOs systematically. Comprehensive tests recommend that PC be most suitable for a unimodal or relatively simple problem, whilst the genetic algorithm (with elitism in MATLAB, GAe) for a relatively complex, multimodal or unknown problem. The paper also provides an amalgamated scoring system and a decision tree as a guide for a practitioner to decide rapidly which DFO for their application objectives, while recommending GAe for optimizing structures and categories, and for offline global search together with PC for local parameter tuning or online adaptation. To verify these recommendations, all the six DFOs are further tested in a case study optimizing a popular nonlinear filter. The results corroborate the benchmarking results, also confirming that the benchmarking system would offer a useful tool in selecting a DFO for applications.

The aluminum alloys corresponding to the 6000 series are mainly manufactured by mechanical forming processes. Their properties are enhanced by the homogeneous distribution of intermetallic phases such as β-Al₅FeSi or α-Al_x (Fe, Mn) Si. By thermal homogenization treatment, the intermetallic compound β-Al₅FeSi changes its morphology from a needle type with a monoclinic structure to an acicular form known as α-Al₁₂(Fe, Mn)₃Si with an fcc structure. In the present study, samples of the 6063 alloy were subjected to different temperatures of homogenization (798, 823, and 848 K) and treatment times from 0 to 660 min (in intervals of 30 min) to evaluate their effects on the microstructures and morphologies of the intermetallic phases. For the kinetic study, the microstructures of the β and α intermetallic phases were quantified using the Image-Pro software. The results indicate that as the temperature and homogenization time increase, the percentage of phase α also increments. The results of the kinetic analysis revealed that the β → α transformation is controlled by two stages; the first corresponds to the diffusion of Mn atoms from the matrix to the interface of reaction for the formation of the intermetallic phases, while the second corresponds to the nucleation and growth of the iron- and manganese-rich intermetallic phases.

The design of an aluminium melting furnace has faced two challenges: mathematical modelling and simulative optimization. This paper first uses fluid dynamics to model the aluminium process mathematically. Then, the model is utilized to simulate a round shaped reverberatory furnace for melting aluminium alloys. In order to achieve the highest thermal efficiency of the furnace, modelling and simulation are performed to predict complex flow patterns, geometries, temperature profiles of the mixture-gas air through the main chamber, as well as the melting tower attached to the furnace. The results led to the establishment of optimal position and angle of the burner, which are validated through physical experiments, ensuring recirculation of the combustion gases through the melting chamber and the melting tower. Furthermore, a proper arrangement of refractory materials is derived to avoid heat losses through the outer surface of the furnace. Temperature profiles are also determined for the optimization to arrive at the final design of the furnace. Compared with manual designs previously practiced, the simulation-based optimal design of furnaces offers excellent guidance, an increase in the aluminium processing and magnesium removal for more refined alloys, and an increased processing rate of aluminium chip accession.

In this study, using mixtures of pure Al, Si, Mn, and Fe powders, the α-Al₉FeMnSi intermetallic compound was formed onto AISI 304L stainless steel samples by reactive sintering. The processing parameters were temperature (800, 850, and 900 °C) and applied pressure (15 and 20 MPa), using a constant holding time of 7200 s. In this paper, the influence of pressure and temperature on the microstructure, microhardness, and wear resistance of the formed layers was studied. Using X-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness testing, and wearing measurements (pin on disc tests), the cross-section and top side of the coatings were observed and analyzed. We were able to determine the phase composition of cladded layers and interfaces as well as their morphology. The results indicated that several layers were formed during reactive sintering, i.e., an Al-diffusion layer on the top of the substrate, an interface, and the α-Al₉FeMnSi coating itself. The microhardness values of the different layers formed were determined, ranging from 400 to 500 HV for the intermetallic coating, to 120 HV for the substrate. In this way, it was found that the formed intermetallic coating is suitable to increase the corrosion resistance of stainless steel. Additionally, all the coating showed high adherence to the substrate, exhibiting high microhardness and wear resistance. Pin on disc wearing tests showed the wearing mechanisms are predominantly delamination and ablation of the cladded layers and substrate.