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Additive Manufacturing (AM) allows for the manufacturing of complex geometry parts as a single object regardless of their geometric complexity. However, manufacturing a consolidate object is not always to be better than the assembly approach. There are several practical scenarios which often require an assembly approach, i.e., (1) to achieve specific mechanical functionality in the product and aesthetic aspects; (2) to fit a product component in a predefined buildable space, particularly the case where large-scale products are required to be fitted with a developing platform space; and (3) to improve the additive manufacturing productivity (by minimizing processing time, improving product quality, surface finish and reducing material/support cost). In this paper, a computational framework is proposed to decompose the initial object design into several assemblies with the aim to minimize the total AM processing time. The decomposed components can be arranged into a minimum printing height space, thus it reduces the number of printing layer significantly, yielding to a build-up time reduction even if some time is spent in required assembling operations. Computation framework is implemented for three medium size object geometries. The presented study points out that the part decomposition and assembling approach can considerably reduce the total AM processing time. The methodology supports AM to be extended for a broad range of medium size products, and leads to strengthening the customizability of the products.

In this paper a novel knowledge based fuzzy control system with Probabilistic Roadmap (PRM) method is proposed for path planning and target seeking behaviour of mobile robot in presence of obstacles. The proposed method runs in two folds, first fold generates the shortest path between the start position to target position in a priori known cluttered environment, wherein the PRM is used to construct straight-lined path by connecting the intermediate nodes. However, the second fold steps help to convert the sharp corners into the smoothen curves throughout the path. The knowledge based fuzzy control system ensures the smooth turns by adjusting the suitable heading angle, there by correcting the position and orientation of the robots to find local/global targets. The presented methodology has been simulated and tested in various environments. Simulation results justify that, the method is not only capable to find an optimal or near-optimal robot path in complex obstacle present environments while avoiding any dead end situation, but also ensures a good control over robot velocity and smooth turning at the turning junctions. In all the simulation experiments it is found that, the method has shorten the path length by more than 5 percent while smoothening and subsequently navigation time was also reduced appreciably.

In Paper and Pulp (P&P) industry resource management scenarios, such as processing cell(s) efficiency reduction, equipment break-down or failures, alternative energy source (biomass and solar energy) utilization/ synchronization with regular energy sources and management decided change in production rate or product quality often occur in industrial processes causing material flow disruption, energy and production losses. In such scenarios, immediate and appropriate actions are required to be taken to minimize energy and production losses. Emerging resource management options tackle these issues through four different ways i.e., by mixing of readymade pulp at various processing stages, by varying the percentage of input raw material, by adopting a parallel utilization of alternative energy sources and by switching on/off some processing units or parallel working segments. These ways are implementable with immediate effect, accurately monitorable and their degree of application can be regulated within a wide range, thus denoted as adaptive solutions. This research work proposes a discrete-event simulation based real-time resource management technique for paper and pulp industry, wherein some resource management challenges are addressed and solved strategically with adaptive solutions. The presented examples validate that using the developed approach, the effective adaptive strategy can be timely planned to resolve occasionally or permanently happening resource management challenges ensuring an un-interrupted material flow throughout the plant along with the minimization of productivity and energy losses.

To improve energy efficiency of a Paper & Pulp industry, full capacity utilization of the processing equipment and diverse energy source utilities are promoted, while same time processing of diverse raw materials, meeting of strict processing time requirements, seamless material flow and obtaining required product quality are important concerns to be fulfilled. This is similar to a resources management problem while energy is considered as one of the measurable resources, wherein estimation of energy consumptions is crucial/complicated aspect. Discretized form of energy consumption (approximate energy consumed by unit amount of intermediate products at different intermediate processing stages) concept is utilized here with discrete event simulation for consumed energy estimation/analysis. The research deals with a real time working Paper & Pulp industry resource management problem, wherein overall energy consumption rate and production rate were analyzed for some commonly occurring situation of efficiency reduction of the processing station(s). Energy efficient solutions are evaluated with varying production flow (varying the percentage of raw material) and resources management options (turning on/off paper machines). Energy efficient solution which ensures minimum loss in production rate and minimizes disturbances in material flow is suggested for implementation.

This paper addresses the problem of trajectory tracking and control of a low-cost flying agent, equipped with a microcontroller for real-time navigation. A flying agent (aircraft-type quadcopter) has been designed and developed for steering control along a planned trajectory. A novel control scheme is developed using MATLAB/Simulink to ensure the right balance among pitch, roll and yaw of the flying agent wings. The proposed control scheme utilises a PID controller which regulates the error dynamics to stabilise the navigation. Various trajectories have been generated and verified through simulation studies as well as tested in a real model. The real-time experiments verify the manoeuvrability of the controller, which also checks the compatibility with hardware interfaces.

© 2017 Springer Science+Business Media New YorkThis paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision ¿how much to order¿ is made periodically on the basis of the pre-decided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that¿the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis.

A new model for evaluating the energy consumed by a machine tool for processing a prismatic workpiece was developed. The model takes into account the energy absorbed by different machine components such as the auxiliary systems, the axes, the axes chiller, the tool change system, chip conveyor and the spindle system. The relationships between the power absorption of each considered machine component and the main cutting parameters were adequately modeled. The cutting energy was also taken into consideration. Since the wear of the tool was included in the model, the energy absorbed by the machine during passive phases (e.g. tool changes) was also evaluated. Each machine component model was opportunely configured according to the considered production phase. Some of the parameters of the machine tool energy model were identified through experimental tests performed on a real machining center equipped with linear motors. A multivariable energy optimization was carried out considering the cutting speed, the feed and the radial depth of cut as the main parameters. The energy minimization was performed through exhaustive enumeration methods. Results were properly discussed and analyzed. The optimization analysis was also repeated emulating various machine tool configurations and different production scenarios.