Dispersion-engineered silicon nitride waveguides for mid-infrared supercontinuum generation covering the wavelength range 0.8-6.5 mu m

We numerically demonstrate the generation of a mid-infrared supercontinuum (SC) through the design of an on-chip complementary metal oxide semiconductor (CMOS) compatible 10-mm-long air-clad rectangular waveguide made using stoichiometric silicon nitride (Si 3 N 4 ) as the core and MgF 2 glass as its lower cladding. The proposed waveguide is optimized for pumping in both the anomalous and all-normal dispersion regimes. A number of waveguide geometries are optimized for pumping at 1.55 μ m with ultrashort pulses of 50-fs duration and a peak power of 5 kW. By initially keeping the thickness constant at 0.8 μ m, four different structures are engineered with varying widths between 3 μ m and 6 μ m. The largest SC spectral evolution covering a region of 0.8 μ m to beyond 6.5 μ m could be realized by a waveguide geometry with a width of 3 μ m. Numerical analysis shows that increasing width beyond 3 μ m by fixing thickness at 0.8 μ m results in a reduction of the SC extension in the long wavelength side. However, the SC spectrum can be enhanced beyond 6.5 μ m by increasing the waveguide thickness beyond 0.9 μ m with the same peak power and pump source. To the best of our knowledge, this is first time report of a broad SC spectral evolution through numerical demonstration in the mid-infrared region by the silicon nitride waveguide. In the case of all-normal dispersion pumping, a flatter SC spectra can be predicted with the same power and pump pulse but with a reduced bandwidth spanning 950–2100 nm

Complexity in manufacturing systems and its measures: a literature review

Complexity in manufacturing systems still remains a challenge and leads to operational issues and increased production cost. In this paper, drivers of complexity and typical symptoms of complex manufacturing systems are identified. A comprehensive review of studies published within the last two decades to assess manufacturing system complexity are presented. The key contributions of this review are: 1) a classification of complexity assessment methods based on perceived complexity symptoms; 2) a comprehensive review of assessment methods with cross-evaluation to identify appropriate use based on available data; 3) recommendations for the wider academic and industrial community, based on research trends identified in the literature, as to how complexity assessment should be addressed in the future. It is concluded that the assessment of complexity is necessary so that it can be controlled effectively, however the industry suffers from a lack of practical tools to support in this endeavour

A Lightweight Approach for Human Factor Assessment in Virtual Assembly Designs: An Evaluation Model for Postural Risk and Metabolic Workload

© 2016 The Authors. The assessment and optimisation of postural stress and physical fatigue can be challenging and is typically conducted only after the design of manual operations has been finalised. However early assessment of manual operations and identification of critical factors that are deemed outside of an appropriate envelope can avoid the time and costs often associated with re-designing machines and layout for operator work processes. This research presents a low cost software solution based on a simplified skeleton model that uses operator position and workload data extracted from a simulation model used for virtual manufacturing process planning. The developed approach aims to assess postural stress and physical fatigue scores of assembly operations, as they are being designed and simulated virtually. The model is based on the Automotive Assembly Worksheet and the Garg's metabolic rate prediction model. The proposed research focuses on the integration of virtual process planning, ergonomic and metabolic analysis tools, and on automating human factor assessment to enable optimisation of assembly operations and workload capabilities at early design stage

Design Evaluation of Automated Manufacturing Processes Based on Complexity of Control Logic

Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. Assessing complexity realizes the reduction and management of complexity sources which contributes to lowering associated engineering costs and time, improves productivity and increases profitability. This paper proposes an approach for evaluating the design of automated manufacturing processes based on the structural complexity of the control logic. Six complexity indices are introduced and formulated: Coupling, Restrictiveness, Diameter, Branching, Centralization, and Uncertainty. An overall Logical Complexity Index (CL) which combines all of these indices is developed and demonstrated using a simple pick and place automation process. The results indicate that the proposed approach can help design automation logics with the least complexity and compare alternatives that meet the requirements during initial design stages

A method to assess assembly complexity of industrial products in early design phase

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product's assembly. The approach is based on Hückel's molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Ab-initio study of the bandgap engineering of Al(1-x)Ga(x)N for optoelectronic applications

A theoretical study of Al(1-x)Ga(x)N, based on full-potential linearized augmented plane wave method, is used to investigate the variations in the bandgap, optical properties and non-linear behavior of the compound with the variation of Ga concentration. It is found that the bandgap decreases with the increase of Ga in Al(1-x)Ga(x)N. A maximum value of 5.5 eV is determined for the bandgap of pure AlN which reaches to minimum value of 3.0 eV when Al is completely replaced by Ga. The static index of refraction and dielectric constant decreases with the increase in bandgap of the material, assigning a high index of refraction to pure GaN when compared to pure AlN. The refractive index drops below 1 for photon energies larger than 14 eV results group velocity of the incident radiation higher than the vacuum velocity of light. This astonishing result shows that at higher energies the optical properties of the material shifts from linear to non-linear. Furthermore, frequency dependent reflectivity and absorption coefficients show that peak value of the absorption coefficient and reflectivity shifts towards lower energy in the UV spectrum with the increase in Ga concentration. This comprehensive theoretical study of the optoelectronic properties of the alloys is presented for the first time which predicts that the material can be effectively used in the optical devices working in the visible and UV spectrum.Comment: 18 pages, 7 figure