A study on structural CAD data conversion between AVEVA MARINE® and Intergraph Smart 3D®

Multisensor data processing in dimensional metrology for collaborative measurement of a laser plane sensor combined to a touch probe

Turbomachinery aerodynamic and aeroacoustic modelling is recognized to be one of the main areas of interest in the frame of industrial engineering, due to the large number of applications concerning the rotating fluid machines. Numerical modelling is today widely integrated in the design and development process of industrial components, as well as Computational Fluid Dynamics (CFD) is a tool well integrated into the industrial development and production life-cycles. This has been made because of two main factors: the increase in the performance of relatively cheap computing systems and the progress made by development of integrated CAE virtual environment even more oriented to design and optimization. Furthermore well established and validated CFD methodologies able to deal with the high complexity of geometries and multiphysics approaches are available and practicable within the industrial environment. Nevertheless since for many of applications such as aerodynamics, heat exchange, etc., CFD is largely used, there are still some areas where it represents a challenge, in particular for its industrial applicability. One of these areas is the Computational AeroAcoustics (CAA), whose interest is rapidly growing due to the increasing demand of quick and efficient numerical tools for the design-to-noise approach to airframe components. The major difficulty of CAA is that noise is a secondary effect of other physical phenomena, and noise prediction has to deal with highly complex topics such as turbulence modelling and transition to turbulence prediction. Moreover the sound pressure compared with the hydrodynamic pressure is several orders of magnitude smaller making its numerical prediction very difficult. The goal of the present work is to perform investigations on two rotating fluid machines: the vertical axis wind turbines (VAWT) and the high speed transonic cruise propellers. The investigations performed were focused on the aerodynamic and aeroacoustic theoretical and numerical modelling in order to achieve the numerical results that actually miss in the literature improving the know-how for two classes of problems which play a central role in the modern industry: the renewable energies and the advanced transportation systems. VAWT modelling was approached from a theoretical point of view, by the implementation of a modern unsteady aerodynamic model for non-stationary airfoil aerodynamic coefficients prediction (the Beddoes Leishman model) into a classical theory, the Double Streamtube Paraschivoiu model. The acoustic modelling of WT was approached on a general basis with the modelling of the airfoil self-generated problem. It was chosen as it is one of the most prominent noise sources for wind turbines and it represents a big challenge for CFD. The preliminary literature review pointed out how there are different airfoil self-generated noise typologies, according to the laminar/turbulent characteristics of the flow field, airfoil aerodynamic parameters and far-field flow conditions. Different CFD numerical approaches were used for the different noise generation mechanisms, including transient RANS/LES simulations with a particular focus for novel turbulence modelling methodologies, involving transitional models and Embedded LES. The acoustic pressure flow computations were performed with the Ffowcs Williams Hawkings (FW-H) acoustic analogy and pressure time signals post-processed with an in-house built acoustic tool. Deep explorations of acoustic signal noise processing, Fast Fourier Transformation techniques and noise signal corrections have been necessary in order to achieve good agreement between experimental and computational data. The reference test-case was the NACA 0012 airfoil, chosen for the wide amount of experimental data available in literature in a significant range of velocities and angles of attack. The problem of propeller aerodynamic and aeroacoustic modelling was approached with the CFD and CAA simulation of a subsonic propeller, the NACA 4-(3)(08)-03, chosen for the experimental results available in literature for the 2-bladed and 7-bladed configurations. This test-case allowed us to set up properly the numerical simulation, the CAD model and the computational grid requirements for such kind of geometries. In a second stage the attention was focused on the NASA SR2 and SR3 geometries, both in the 8-bladed configuration. Experimental data in transonic conditions for wind tunnel and flight test were available for these two geometries, and numerical simulations were performed for 5 different data sets, for asymptotic Mach numbers between 0.6 and 0.9 and rotational speeds between 6000 and 9000 rpm. Results of the simulations are compared with experiments, showing the ability of this approach to predict noise with a discrepancy within a few dB for the different simulated conditions and microphones locations. Particular attention is given to the set of corrections to be applied to acoustic experimental data in order to be consistently compared with free field CFD results. The CFD simulation strategy has been refined to perform complete aerodynamic and aeroacoustic calculations with highly competitive computational cost.

Productivity refers to the physical relationship between the quantity produced (output) and the quantity of resources used in the course of production (input).Productivity is the ratio of output/input. Here output means No. of items produced or services rendered. Input consists of various resources viz men, machine, material and money. Productivity measures the efficiency of the production system. The efficiency with which resources are utilized is called productive efficiency. Higher productivity means producing more from a given amount of inputs or producing a given amount with lesser inputs Enhancing productivity in manufacturing industries is very essential in the present scenario. The present business scenario has undergone a lot of change. It is not the same as that existed before. Product life cycle has changed from years to months and variety has become the need of the hour. In fact the pace of change has accelerated. The competition has got intensified. Speed has become the essence of business. Today it is survival of the fittest. In addition to this customer satisfaction has gained prominence and undoubtedly it can be said that the customer has become the king of the market. Hence in today?s competitive manufacturing environment it is essential to get the most out of our existing assets. As it has been said that a nation, a state or an industry advances by using less to make more. In other words it is not important for industries to just increase production but it is even more important to maintain and enhance productivity.. The objective of this paper is to suggest suitable strategies for enhancing productivity in manufacturing industries. In order to enhance productivity in industries the first step is to measure overall productivity. Then the total productivity should be analyzed in terms of each input factor .To achieve this the cause and effect diagram of productivity shown in this paper can be used to pin point certain areas or factors which can be improved upon to enhance productivity. In other words by first analyzing the partial productivity indicators viz productivity of men, productivity of materials, productivity of machines and methods and then by combining all of them the overall productivity of a manufacturing industry can be increased. The other important areas which can be concentrated for improving productivity are rejection level, implementation of quality management systems, total cycle time, creativity and innovation, motivation of employees etc. Quality of a process has a direct impact on the productivity. Quality results in fewer rejections hence, lesser no of reworks and better productivity. Better quality standards save time and money which are very crucial to the efficiency of any process, be it manufacturing or service. Product life cycle doesn?t end after its purchase. When a product or a service is not as expected or as promised to the customer, it leads to customer churn. Today we live in a world where customer acquisition costs are high thanks to intense competition in every industry. In such a world retaining existing customers has become one of the main focus points of any business. This has led to companies pouring in huge chunk of money into customer service operations, which demand high level of productivity and quality, as it is a way in which the company interacts directly with the customer. Quality of such operations is crucial in meeting customer expectations. In this paper we also discussed a quality tool that can be implemented in any industry, be it manufacturing, IT, consulting etc. Critical TO Quality (CTQ) Drill Down Tree helps individuals or companies identify measurable parameters that can improve the quality of operations required to enhance a product or a service.

Loops are critical elements in boundary representation (B-rep) models because they link all edges corresponding to a face. Loops can be used in feature recognition for identifying depressions or protrusions. In real 3D CAD models, however, features typically lie across multiple faces, which is beyond the data structure of current B-rep models. This study presents a virtual loop concept to account for all loop types used in CAD models, and develops algorithms for recognizing them. In accordance with the complexity of the recognition algorithm, this study defines three types of loop: single, virtual, and multivirtual. A single loop is the current loop recorded in the B-rep model. A virtual loop lies across faces that are at least G1 continuous. Finally, a multivirtual loop lies across faces that are either G0 or G1 continuous. The proposed loop structure provides a more complete data structure for recognizing various types of features in feature-recognition modules. Several realistic CAD models are presented to confirm the feasibility of the proposed loop-recognition and feature-recognition methods.

Workpiece localization is the process of obtaining the location of a workpiece in a robot workspace. The location (position and orientation) is represented by the transformation between the workpiece (local) coordinate frame and the reference (world) frame. In this work, we propose a workpiece localization strategy to automate the localization process by collecting data sequentially and efficiently without the two common restrictive assumptions: the data used to calculate the transformation is readily available and the correspondence between the features used for calculation is known. Correspondingly, two subproblems are involved: (1) determining the correspondence between the measured data and the CAD model data, and (2) determining the next-best-views (NBVs) in case of limited measurement data. We assume the workpiece is convex and has at least three flat surfaces. We use the extended Gaussian images (EGIs) from the Gauss map of both the CAD model point clouds and measured point clouds to find the flat surfaces on the workpiece. A mixed integer convex optimization problem is solved to estimate the correspondence and the rotation between the flat surfaces in the CAD model and the measured point clouds. The translation part of the homogeneous transformation is obtained by solving a least-squares problem using the estimated correspondence. Potential views for further measuring the workpiece are generated by evaluating a defined search region to find the NBVs based on a specified criterion. The workpiece is considered to be fully localized when the distances in the estimated homogeneous transformation matrices are within a predefined threshold. Simulation results are provided to show the effectiveness of the proposed localization method.

This paper proposes a new CAD model for the design of lattice material components. The CAD model better captures the user’s design intent and provides a dual-scale framework to represent the geometry and material distribution. Conventional CAD model formats based on B-Rep generate millions of data files, which also makes design intent and material information missing. In the present work, a new shape-material model for lattice material components is proposed. At the macroscopic scale, a compact face-based non-manifold topological data structure is proposed to express the lattice shape-material information without ambiguity. At the microscopic scale, implicit function is adopted for the representation of lattice material components. Numerical experiments verify that the proposed CAD model provides a powerful support for design intent with minor space costs. Meanwhile, the representation method supports solid modeling queries of geometric and material information on each scale.

Optimizing CAD Models with Latent Space Manipulation

Reverse engineering applications for recovery of broken or worn parts and re-manufacturing: Three case studies

For the deployment of both Product Lifecycle Management (PLM) and Knowledge-Based Engineering (KBE) approaches, product and process engineering knowledge needs to be identified, acquired, formalized, processed and reused. While knowledge acquisition is still a bottleneck process, the formalized engineering knowledge is still too often encapsulated in CAD models and in KBE systems developed in vendor-specific environments. To address this issue, this paper introduces a possible solution enabling the enrichment of a CAD-KBE-PLM integration schema that provides a standardized and neutral representation of engineering knowledge for further reuse across heterogeneous CAD, KBE and PLM systems. To enrich this schema, the proposed solution combines the use of a Multi-CAD API library – which allows platform-independent and automatic extraction of engineering knowledge from CAD models into an XML-based representation – and a Knowledge Acquisition and Formalization Assistant (KAFA) which assist domain experts to formalize their procedural knowledge.

Each day, the sustainability issue gains more importance in all the fields of the society, reaching more recently also the industrial system, as recent data point to as one of the most responsible for the current environmental scenario, consuming large amounts of natural resources and generating pollution.The main concept of sustainability is to consider in the analyzes the triple bottom line, composed of economic, environmental and social factors throughout the life cycle of products, from the extraction of raw materials to after-use, and apply the concept of 6Rs (reduce, reuse, recycle, recover, redesign and remanufacture).In the field of process engineering one of the biggest challenges of this decade is the development of tools for environmental considerations.In order to quantify sustainability parameters of a manufacturing chain, a piece was machined by several machining processes, conventional milling, sawing, grinding and CNC machining, and first measured the level of noise generated during all processes verifying that the only equipment that exceeded the average sound level of 85db was the compressed air pistol, used for cleaning in CNC machining.For the CNC machining, were compared several machining strategies in terms of energy consumption obtained by a powermeter, three cutting speeds, two feeds rate, two different types of tools and also two different types of machines, achieving energy savings of 94% for the most optimized strategy.Finally, to quantify the environmental impacts generated in the CNC machining process, it was used a life cycle assessment program, which although originally applied to the product level, was also quite functional at the process level, despite some limitations such as the lack of specific data in the inventory and the high standard deviation, this which have been reduced by integrating the consumed actual energy into the program inventory.

We present a real-time system that renders antialiased hard shadows using irregular z-buffers (IZBs). For subpixel accuracy, we use 32 samples per pixel at roughly twice the cost of a single sample. Our system remains interactive on a variety of game assets and CAD models while running at 1080p and 2160p and imposes no constraints on light, camera or geometry, allowing fully dynamic scenes without precomputation. Unlike shadow maps we introduce no spatial or temporal aliasing, smoothly animating even subpixel shadows from grass or wires.

We further describe and analyze a real-time system for rendering antialiased hard shadows using irregular z-buffers (IZBs) that we first presented in Wyman et al. [1]. We focus on identifying bottlenecks, exploring these from an algorithmic complexity standpoint, and presenting techniques to improve performance. Our system remains interactive on a variety of game assets and CAD models while running at resolutions 1920 1080 and above and imposes no constraints on light, camera or geometry, allowing fully dynamic scenes without precomputation. We render sub-pixel accurate, 32 sample per pixel hard shadows at roughly twice the cost of a single sample per pixel. This allows us to smoothly animate even subpixel shadows from grass or wires without introducing spatial or temporal aliasing. Prior algorithms for irregular z-buffer shadows rely heavily on the GPU's compute pipeline. Instead we leverage the standard rasterization-based graphics pipeline, including hardware conservative raster and early-z culling. Our key observation is noting a duality between irregular z-buffer performance and shadow map quality; irregular z-buffering is most costly exactly where shadow maps exhibit the worst aliasing. This allows us to use common shadow map algorithms, which typically improve aliasing, to instead reduce our cost. Compared to state of the art ray tracers, we spawn similar numbers of triangle intersections per pixel yet completely rebuild our data structure in under 1 ms per frame.

Development of mass customised products demands various activities in the product development process, such as design, manufacturing process planning, manufacturing resource planning and maintenance process planning, to be considered and coordinated. In this research, a multi-population co-evolutionary genetic programming (MCGP) approach is introduced to identify the optimal design and its downstream product life cycle activities for developing mass customised product considering these different product life cycle activities and their relationships. In this research, two types of relationships between downstream product life cycle activities are considered: sequential relationships and concurrent relationships. The product design and its downstream life cycle descriptions are modelled by a multi-level graph data structure. These product life cycle descriptions are defined at two different levels: generic level for modelling the descriptions in a product family and specific level for modelling the descriptions of a customised product. The optimal design and its downstream life cycle activities are identified through the MCGP approach based on evaluations in different product life cycle aspects. Various methods have been developed to improve computation efficiency for the MCGP. Industrial case studies and comparative case studies have been implemented to demonstrate the effectiveness of the developed approach.

Product data modelling and management has become an increasingly feature for enterprises in order to strengthen their competitive position. Modelling can be regarded as a requirement analysis, whilst management is a means to apply powerful application to product data. This data describes the status of a product during the whole product life cycle as well as the transformation of one state to another. Operations performed in one phase of the product will definitely have impact on later phases and the product itself. It is of interest to analyse this impact and to extract methods to assist the designer of a product in order to improve the quality of a product. Based on an internal analysis of European research projects, this paper shows how product data management is used during the product life cycle of a product and how it can improve a products quality. The goal is to derive support strategies for the designer based upon the data structure and knowledge representation used within these projects. It describes an approach to measure product data structures and design knowledge representations. Different levels of complexity concerning the managed product knowledge in design support systems have been identified as well as different design support strategies.

Today, industrial production and supply chains are facing increased demands regarding flexibility and transparency of processes, caused by a trend for mass-customization and increasingly tighter regulations for the traceability of goods. To fulfill such challenging market demands, auto-ID technologies and semantic product descriptions are becoming part of future value chains. In this paper a modelling approach for a digital object memory (DOM) allowing for the attachment of product life cycle (PLC) information to everyday objects is presented. After reporting on the design aims, memory architecture and data structure, potential benefits of the chosen approach are presented.