Multi-camera complexity assessment system for assembly line work stations

In the last couple of years, the market demands an increasing number of product variants. This leads to an inevitable rise of the complexity in manufacturing systems. A model to quantify the complexity in a workstation has been developed, but part of the analysis is done manually. Thereto, this paper presents the results of an industrial proof-of-concept in which the possibility of automating the complexity analysis using multi camera video images, was tested

The effectiveness of combining rolling deformation with wire-arc additive manufacture on β-Grain refinement and texture modification in Ti-6Al-4V

In Additive Manufacture (AM), with the widely used titanium alloy Ti–6Al–4V, the solidification conditions typically result in undesirable, coarse-columnar, primary β grain structures. This can result in a strong texture and mechanical anisotropy in AM components. Here, we have investigated the efficacy of a new approach to promote β grain refinement in Wire–Arc Additive Manufacture (WAAM) of large scale parts, which combines a rolling step sequentially with layer deposition. It has been found that when applied in-process, to each added layer, only a surprisingly low level of deformation is required to greatly reduce the β grain size. From EBSD analysis of the rolling strain distribution in each layer and reconstruction of the prior β grain structure, it has been demonstrated that the normally coarse centimetre scale columnar β grain structure could be refined down to < 100 μm. Moreover, in the process both the β and α phase textures were substantially weakened to close to random. It is postulated that the deformation step causes new β orientations to develop, through local heterogeneities in the deformation structure, which act as nuclei during the α → β transformation that occurs as each layer is re-heated by the subsequent deposition pass

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

Topology Optimization via Machine Learning and Deep Learning: A Review

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO perspective addresses "why" to use ML for TO, while the ML perspective addresses "how" to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined

Integrated Models and Tools for Design and Management of Global Supply Chain

In modern and global supply chain, the increasing trend toward product variety, level of service, short delivery delay and response time to consumers, highlight the importance to set and configure smooth and efficient logistic processes and operations. In order to comply such purposes the supply chain management (SCM) theory entails a wide set of models, algorithms, procedure, tools and best practices for the design, the management and control of articulated supply chain networks and logistics nodes. The purpose of this Ph.D. dissertation is going in detail on the principle aspects and concerns of supply chain network and warehousing systems, by proposing and illustrating useful methods, procedures and support-decision tools for the design and management of real instance applications, such those currently face by enterprises. In particular, after a comprehensive literature review of the principal warehousing issues and entities, the manuscript focuses on design top-down procedure for both less-than-unit-load OPS and unit-load storage systems. For both, decision-support software platforms are illustrated as useful tools to address the optimization of the warehousing performances and efficiency metrics. The development of such interfaces enables to test the effectiveness of the proposed hierarchical top-down procedure with huge real case studies, taken by industry applications. Whether the large part of the manuscript deals with micro concerns of warehousing nodes, also macro issues and aspects related to the planning, design, and management of the whole supply chain are enquired and discussed. The integration of macro criticalities, such as the design of the supply chain infrastructure and the placement of the logistic nodes, with micro concerns, such the design of warehousing nodes and the management of material handling, is addressed through the definition of integrated models and procedures, involving the overall supply chain and the whole product life cycle. A new integrated perspective should be applied in study and planning of global supply chains. Each aspect of the reality influences the others. Each product consumed by a customer tells a story, made by activities, transformations, handling, processes, traveling around the world. Each step of this story accounts costs, time, resources exploitation, labor, waste, pollution. The economical and environmental sustainability of the modern global supply chain is the challenge to face

Industrial application of fuzzy systems; adaptive fuzzy control of solder paste stencil printing

This paper presents an adaptive fuzzy control algorithm for the control of the solder paste stencil printing stage of surface mount printed circuit board assembly. The proposed method of automatic solder paste stencil printing consist of four blocks; fuzzy feature extraction, defect classifcation of paste deposits, adaptive fuzzy rule-based model identifcation and subsequently an optimal control action for the stencil printing process. Experimental results are presented to illustrate the capability of the algorithm