Process Planning Optimization In Reconfigurable Manufacturing Systems

Trends and perspectives in dynamic environments point towards a need for optimal operating levels in reconfigurable manufacturing activities. Central to the goal of meeting this need is the issue of appropriate techniques for manufacturing process planning optimization in reconfigurable manufacturing, i.e. (i) what decision making models and (ii) what computational techniques, provide an optimal manufacturing process planning solution in a multidimensional decision variables space? Conventional optimization techniques are not robust, hence; they are not suitable for handling multidimensional search spaces. On the other hand, process planning optimization for reconfigurable manufacturing is not amenable to classical modeling approaches due to the presence of complex system dynamics. Therefore, this study explores how to model reconfigurable manufacturing activities in an optimization perspective and how to develop and select appropriate non-conventional optimization techniques for reconfigurable process planning.In this study, a new approach to modeling Manufacturing Process Planning Optimization (MPPO) was developed by extending the concept of manufacturing optimization through a decoupled optimization method. The uniqueness of this approach lies in embedding an integrated scheduling function into a partially integrated process planning function in order to exploit the strategic potentials of flexibility and reconfigurability in manufacturing systems. Alternative MPPO models were constructed and variances associated with their utilization analyzed. Five (5) Alternative Algorithm Design Techniques (AADTs) were developed and investigated for suitability in providing process planning solutions suitable for reconfigurable manufacturing. The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. The comparative performances of the developed AADTs when tasked to solve an instance of a MPPO problem were analyzed and evaluated. In particular, the relative performances of the novel variant of simulated annealing in comparison to: (a) (i) a simulated annealing search, and (ii) a genetic search in the Boltzmann Machine Architecture, and (b) (i) a modified genetic algorithm and (ii) a genetic algorithm with a customized threshold operator that implements an innovative extension of the diversity control mechanism to gene and genome levels; were pursued in this thesis.Results show that all five (5) AADTs are capable of stable and asymptotic convergence to near optimal solutions in real time. Analysis indicates that the performances of the implemented variant of simulated annealing are comparable to those of other optimization techniques developed in this thesis. However, a computational study shows that; in comparison to the simulated annealing technique, significant improvements in optimization control performance and quality of computed solutions can be realized through implementing intelligent techniques. As evidenced by the relative performances of the implemented cooperative schemes, a genetic search is better than a simulated annealing search in the Boltzmann Machine Architecture. In addition, little performance gain can be realized through parallelism in the Boltzmann Machine Architecture. On the other hand, the superior performance of the genetic algorithm that implements an extended diversity control mechanism demonstrates that more competent genetic algorithms can be designed through customized operators. Therefore, this study has revealed that extending manufacturing optimization concepts through a decoupled optimization method is an effective modeling approach that is capable of handling complex decision scenarios in reconfigurable manufacturing activities. The approach provides a powerful decision framework for process planning optimization activities of a multidimensional nature. Such an approach can be implemented more efficiently through intelligent techniques. Hence; intelligent techniques can be utilized in manufacturing process planning optimization strategies that aim to improve operating levels in reconfigurable manufacturing with the resultant benefits of improved performance levels

Perspectives on Dual-Purpose Smart Water Power Infrastructures for Households in Arid Regions

In hot arid climates, freshwater and power are produced simultaneously through seawater desalination since these regions receive little rainfall. This results in a unique urban water/power cycle that often faces sustainability and resilience challenges. Elsewhere, such challenges have been addressed through smart grid technologies. This chapter explores opportunities and initiatives for implementing smart grid technologies at household level for a case study in Qatar. A functional dual-purpose smart water/power nanogrid is developed. The nanogrid includes multiloop systems for on-site water recycling and on-site power generation based on sustainability concepts. A prototype dual-purpose GSM-based smart water/power nanogrid is assembled and tested in a laboratory. Results of case study implementation show that the proposed nanogrid can reduce energy and water consumptions at household level by 25 and 20%, respectively. Economic analysis shows that implementing the nanogrid at household level has a payback period of 10 years. Hence, larger-scale projects may improve investment paybacks. Extension of the nanogrid into a resilient communal microgrid and/or mesogrid is discussed based on the concept of energy semantics. The modularity of the nanogrid allows the design to be adapted for different scale applications. Perspectives on how the nanogrid can be expanded for large scale applications are outlined

System approach for building energy conservation

Energy use in residential and commercial buildings and towers represents more than 30% of energy consumption. The increase in number of buildings and towers in most of the major cities worldwide led to several initiatives for energy conservation programs with the main objective to achieve energy savings. Most energy strategies include energy conservation beside the increase in the penetration of renewable energy technologies. This paper shows business model and engineering design framework for practical implementation of energy conservation in buildings. Key performance indicators are modeled and used to evaluate energy conservation strategies and energy supply scenarios as part of the design and operation of building energy systems. The proposed system approach shows effective management of building energy knowledge on the basis of Energy Semantic Networks (ESN), which supports the simulation, evaluation, and optimization of several building energy conservation scenarios. Case study hotel is used to illustrate the proposed building energy conservation framework. 2014 The Author.Scopus2-s2.0-8492350384

Manufacturing process planning optimisation in reconfigurable multiple parts flow lines

Purpose: This paper explores the capabilities of genetic algorithms in handling optimization of the critical issues mentioned above for the purpose of manufacturing process planning in reconfigurable manufacturing activities. Two modified genetic algorithms are devised and employed to provide the best approximate process planning solution. Modifications included adapting genetic operators to the problem specific knowledge and implementing application specific heuristics to enhance the search efficiency. Design/methodology/approach: The genetic algorithm methodology implements a genetic algorithm that is augmented by application specific heuristics in order to guide the search for an optimal solution. The case study is based on the manufacturing system. Raw materials enter the system through an input stage and exit the system through an output stage. The system is composed of sixteen (16) processing modules that are arranged in four processing stages. Findings: The results indicate that the two genetic algorithms are able to converge to optimal solutions in reasonable time. A computational study shows that improved solutions can be obtained by implementing a genetic algorithm with an extended diversity control mechanism. Research limitations/implications: This paper has examined the issues of MPP optimization in a reconfigurable manufacturing framework with the help of a reconfigurable multiparts manufacturing flow line. Originality/value: The results of the case illustration have demonstrated the practical use of diversity control implemented in the MGATO technique. In comparison to MGAWTO, the implemented MGATO improves the population diversity through a customized threshold operator. It was clear that the MGATO can obtain better solution quality by foiling the tendency towards premature convergence

A metaheuristic approach to manufacturing process planning in reconfigurable manufacturing systems

Manufacturing process planning (MPP) is concerned with decisions regarding selection of an optimal configuration for processing parts. For multiparts reconfigurable manufacturing lines, such decisions are strongly influenced by the types of processes available, the relationships for sequencing the processes and the order of processing parts. Decisions may conflict, hence the decision making tasks must be carried out in a concurrent manner. This paper outlines an optimization solution technique for the MPP problem in reconfigurable manufacturing systems (RMSs). MPP is modelled in an optimization perspective and the solution methodology is provided through a metaheuristic technique known as simulated annealing. Analytical functions for modelling MPP are based on knowledge of processes available to the manufacturing system as well as processing constraints. Application of this approach is illustrated through a multistage parallel–serial reconfigurable manufacturing line. The results show that significant improvements to the solution of this type of problem can be gained through the use of simulated annealing. Moreover, the metaheuristic technique is able to identify an optimal manufacturing process plan for a given production scenario

Supercapacitor Performance of Nickel-Cobalt Sulfide Nanotubes Decorated Using Ni Co-Layered Double Hydroxide Nanosheets Grown in Situ on Ni Foam

In this study, to fabricate a non-binder electrode, we grew nickel-cobalt sulfide (NCS) nanotubes (NTs) on a Ni foam substrate using a hydrothermal method through a two-step approach, namely in situ growth and an anion-exchange reaction. This was followed by the electrodeposition of double-layered nickel-cobalt hydroxide (NCOH) over a nanotube-coated substrate to fabricate NCOH core-shell nanotubes. The final product is called NCS@NCOH herein. Structural and morphological analyses of the synthesized electrode materials were conducted via SEM and XRD. Different electrodeposition times were selected, including 10, 20, 40, and 80 s. The results indicate that the NCSNTs electrodeposited with NCOH nanosheets for 40 s have the highest specific capacitance (SC), cycling stability (2105 Fg-1 at a current density of 2 Ag-1), and capacitance retention (65.1% after 3,000 cycles), in comparison with those electrodeposited for 10, 20, and 80 s. Furthermore, for practical applications, a device with negative and positive electrodes made of active carbon and NCS@NCOH was fabricated, achieving a high-energy density of 23.73 Whkg-1 at a power density of 400 Wkg-1

Angle-insensitive co-polarized metamaterial absorber based on equivalent circuit analysis for dual band WiFi applications

A novel and systematic procedure to design a co-polarized electromagnetic metamaterial (MM) absorber with desired outputs and resonance frequencies for dual-band WiFi signal absorption is presented. The desired resonance frequencies with expected S parameters' values were first designed as an equivalent circuit with extensive analysis and then implemented into frequency-selective MM absorber by numerical simulation with precise LRC elements, satisfying least unit cell area (0.08λ), substrate thickness (0.01λ) and maximum effective medium ratio (12.49). The absorber was simulated for the maximum angle of incidence for both the normal and oblique incidences at co-polarization. The absorptions at the desired resonance frequencies were found at a satisfactory level by both simulation and practical measurement along with a single negative value to ensure metamaterial characteristics. The proposed equivalent circuit analysis approach can help researchers design and engineering co-polarization insensitive MM absorbers using conventional split-ring resonators, with perfection in output and desired resonance frequencies without the necessity of lumped elements or multilayer substrates. The proposed metamaterial can be utilized for SAR reduction, crowdsensing, and other WiFi-related practical applications.This work was supported by the Universiti Kebangsaan Malaysia research grant GUP-2020-017. This work was also supported by Grant NPRP11S-0102-180178 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar, and the claims made herein are solely the responsibility of the authors.Scopu

Forced vibration characteristics of embedded graphene oxide powder reinforced metal foam nanocomposite plate in thermal environment

Abstract Dynamic behavior of a new class of nanocomposites consisted of metal foam as matrix and graphene oxide powders as reinforcement is presented in this study in the framework of forced vibration. Graphene oxide powders are dispersed through the thickness of a plate made from metal foam material according to four various functionally graded patterns on the basis of the Halpin-Tsai micromechanical homogenization method. Also, three kinds of porosity distributions including two symmetric and one uniform patterns are considered for the metal foam matrix. As external effects, the plate is rested on the Winkler-Pasternak substrate and under uniform thermal and transverse dynamic loadings. By an incorporation of the refined higher order plate theory and Hamilton's principle, the governing equations of the dynamically loaded graphene oxide powder reinforced metal foam nanocomposite plate are derived and then solved with Galerkin exact solution method to achieve the resonance frequencies and dynamic deflections of the structure. Moreover, the influence of different boundary conditions is taken into account. The results indicate that the forced vibrational response of the graphene oxide powder strengthened metal foam nanocomposite plate is dramatically dependent on various parameters such as graphene oxide powders' weight fraction, different boundary conditions, various porosity distributions, foundation parameters and temperature change of uniform thermal loading