Good cell quality of meshes translate into accurate results within optimum time after computation. But more often than not, we get a mesh output, which is far from accuracy. There are number of factors affecting a mesh, that might compromise with the final result. This chapter focuses on the various shortcomings of a mesh and their repair algorithms.

Mesh Decimation/Simplification

Mesh decimation/simplification is the method of reducing the number of elements used in a mesh while maintaining the overall shape, volume and boundaries preserved as much as possible. It is a type of algorithm that aims to transform a given mesh into another with fewer elements (faces, edges and vertices). The decimation process usually involves a set of user-defined quality criteria, that maintains specific properties of the original mesh as much as possible. This process reduces the complexity of a mesh.

 

 

Mesh Hole-Filling

To analyze a mesh model, it must be complete. Often, some mesh models carry holes in them, which must be filled. The unseen areas of the model appear as holes, which are aesthetically unsatisfying and can be a hindrance to algorithms that expect a continuos mesh. The Fill Hole command fills the holes and gaps in the mesh.

Note – The Fill Hole command only works on triangulated mesh and not tetrahedral mesh

 

 

Mesh Refinement

Certain situations arise which makes us concerned about the accuracy a model in certain areas. Such scenarios prompt us to have fine mesh in those areas to ensure accurate results. However, creating a surface mesh of the entire model with a fine mesh size may ask for unnecessary hours to analyze the fine mesh in those regions where the results are not as important to you. The answer to this issue is the usage of refinement points.

A refinement point identifies a region or volume of space in which a finer mesh has to be generated. Mesh refinement can be defined by identifying an absolute size for the local mesh. Mesh refinement ends up in creating more number of elements in the specified region of the model.

 

 

Mesh Smoothing

Mesh smoothing is also known as mesh relaxation. Sometimes it is necessary to modify that mesh after a mesh generation. It is achieved either by changing the positions of the nodes or by removing the mesh altogether. Mesh smoothing results in the modification of mesh point positions, while the topology remains as it is.

 

In the previous session, we have learned what Mesh is and the various aspects upon which a mesh can be classified. Mesh generation requires expertise in the areas of meshing algorithms, geometric design, computational geometry, computational physics, numerical analysis, scientific visualization and software engineering to create a mesh tool.

Over the years, mesh generation technology has evolved shoulder to shoulder with increasing hardware capability. Even with the fully automatic mesh generators there are many cases where the solution time is less than the meshing time. Meshing can be used for wide array of applications, however the principal application of interest is the finite element method. Surface domains are divided into triangular or quadrilateral elements, while volume domain is divided mainly into tetrahedral or hexahedral elements. A meshing algorithm can ideally define the shape and distribution of the elements.

A key step of the finite element method for numerical computation is mesh generation algorithms. A given domain is to be partitioned it into simpler ‘elements’. There should be few elements, but some portions of the domain may need small elements so that the computation is more accurate there. All elements should be ‘well shaped’. Let us take a walkthrough of different meshing algorithms based of two common domains, namely quadrilateral/hexahedral mesh and triangle/tetrahedral mesh.

Algorithm methods for Quadrilateral or Hexahedral Mesh

Grid-Based Method

The grid based method involves the following steps:

• A user defined grid is fitted on 2D & 3D object. It generates quad/ hex elements on the interior of the object.
• Some patterns are defined for boundary elements followed by forming a boundary element by applying boundary intersection grid.
• This results in the generation of quadrilateral mesh model.

 

Medial Axis Method

Medial axis method involves an initial decomposition of the volumes. The method involves few steps as given below:

• Consider a 2D object with hole.
• A maximal circle is rolled through the model and the centre of circle traces the medial object.
• Medial object is used as a tool for automatically decomposing the model in to simple meshable region.
• Series of templates for the region are formed by the medial axis method to fill the area with quad element.

 

Plastering method

Plastering is the process in which elements are placed starting with the boundaries and advancing towards the centre of the volume. The steps of this method are as follows:

• A 3D object is taken.
• One hexahedral element is placed at boundary.
• Individual hexahedral elements are projected towards the interior of the volume to form hexahedral meshing, row by row and element by element.
• The process is repeated until mesh generation is completed.

 

Whisker Weaving Method

Whisker weaving is based on the concept of the spatial twist continuum (STC). The STC is the dual of the hexahedral mesh, represented by an arrangement of intersecting surfaces, which bisect hexahedral elements in each direction. The whisker weaving algorithm can be explained as in the following steps:

• The first step is to construct the STC or dual of the hex mesh.
• With a complete STC, the hex elements can then be fitted into the volume using the STC as a guide. The loops can be easily determined from an initial quad mesh of the surface.
• Hexes are then formed inside the volume, once a valid topological representation of the twist planes is achieved. One hex is formed wherever three twist planes converge.

 

Paving Method

The paving method has the following steps to generate a quadrilateral mesh:

• Initially a 2D object is taken.
• A node is inserted in the boundary and the boundary node is considered as loop.
• A quadrilateral element is inserted and a row of elements is formed.
• The row of element is placed around the boundary nodes.
• Again this same procedure adopt for next rows.
• Finally quad mesh model is formed.

 

Mapping Mesh Method

The Mapped method for quad mesh generation involves the following steps:

• A 2D object is taken.
• The 2D object is split into two parts.
• Each part is either a simple 2D rectangular or a square object.
• The simple shape object is unit meshed.
• The unit meshed simple shape object is mapped in its original form and then joined back to form actual object.

 

Algorithm methods for Triangular and Tetrahedral Mesh

Quadtree Mesh Method

With the quadtree mesh method, square containing the geometric model are recursively subdivided until the desired resolution is reached. The steps for two dimensional quadtree decomposition of a model are as follows:

• A 2D object is taken.
• The 2D object is divided into rectangular parts.
• A Detail tree of divided object is provided.
• The object is eventually converted into triangle mesh.

 

 

Delaunay Triangulation Method

A Delaunay triangulation for a set P of discrete points in the plane is a triangulation DT such that no points in P are inside the circum-circle of any triangles in DT. The steps of construction Delaunay triangulation are as follows:

• The first step is to consider some coordinate points or nodes in space.
• The condition of valid or invalid triangle is tested in every three points which finds some valid triangle to make a triangular element.
• Finally a triangular mesh model is obtained.

Delaunay Triangulation maximizes the minimum angle of all the angle of triangle and it tends to avoid skinny triangles.

 

Advancing Front Method

Another very popular family of triangular and tetrahedral mesh generation algorithms is the advancing front method, or moving front method. The mesh generation process is explained as following steps:

• A 2D object with a hole is taken.
• An inner and outer boundary node is inserted. The node spacing is determined by the user.
• An edge is inserted to connect the nodes.
• To start the meshing process, an edge AB is selected and a perpendicular is drawn from the midpoint of AB to point C (where C is node spacing determined by the user) in order to make a triangular element.
• After one element is generated, another edge is selected as AB and a point C is made, but if in case any other node lets point D within the defined radius, then ABC element is cancelled and instead, an element ABD is formed.
• This process is repeated until mesh is generated.

 

Spatial Decomposition Method

The steps for spatial decomposition method are as follows:

• Initially a 2D object is taken.
• The 2D object is divided into minute parts till we get the refined triangular mesh.

 

Sphere Packing Method

The sphere packing method follows the given steps:

• Before constructing a mesh, the domain is filled with circles.
• The circles are packed closely together, so that the gaps between them are surrounded by three or four tangent circles.
• These circles are then used as a framework to construct the mesh, by placing mesh vertices at circle centers, points of tangency, and within each gap while using generated points. Eventually, the triangular mesh is generated.

 

 

 

 Source

Singh, Dr. Lokesh, (2015). A Review on Mesh Generation Algorithms. Retrieved from http://www.ijrame.com

The quality of a mesh plays a significant role in the accuracy and stability of the numerical computation. Regardless of the type of mesh used in your domain, checking the quality of your mesh is a must. The ‘good meshes’ are the ones that produce results with fairly acceptable level of accuracy, considering that all other inputs to the model are accurate. While evaluating whether the quality of the mesh is sufficient for the problem under modeling, it is important to consider attributes such as mesh element distribution, cell shape, smoothness, and flow-field dependency.

Element Distribution

It is known that meshes are made of elements (vertices, edges and faces). The extent, to which the noticeable features such as shear layers, separated regions, shock waves, boundary layers, and mixing zones are resolved, relies on the density and distribution of mesh elements. In certain cases, critical regions with poor resolution can dramatically affect results. For example, the prediction of separation due to an adverse pressure gradient depends heavily on the resolution of the boundary layer upstream of the point of separation.

Cell Quality

The quality of a cell has a crucial impact on the accuracy of the entire mesh. The quality of cell is analyzed by the virtue of three aspects: Orthogonal quality, Aspect ratio and Skewness.

Orthogonal Quality: An important indicator of mesh quality is an entity referred to as the orthogonal quality. The worst cells will have an orthogonal quality close to 0 and the best cells will have an orthogonal quality closer to 1.

Aspect Ratio: Aspect ratio is an important indicator of mesh quality. It is a measure of stretching of the cell. It is computed as the ratio of the maximum value to the minimum value of any of the following distances: the normal distances between the cell centroid and face centroids and the distances between the cell centroid and nodes.

Skewness: Skewness can be defined as the difference between the shape of the cell and the shape of an equilateral cell of equivalent volume. Highly skewed cells can decrease accuracy and destabilize the solution.

Smoothness

Smoothness redirects to truncation error which is the difference between the partial derivatives in the equations and their discrete approximations. Rapid changes in cell volume between adjacent cells results in larger truncation errors. Smoothness can be improved by refining the mesh based on the change in cell volume or the gradient of cell volume.

Flow-Field Dependency

The entire effects of resolution, smoothness, and cell shape on the accuracy and stability of the solution process is dependent upon the flow field being simulated. For example, skewed cells can be acceptable in benign flow regions, but they can be very damaging in regions with strong flow gradients.

Correct Mesh Size

Mesh size stands out as one of the most common problems to an equation. The bigger elements yield bad results. On the other hand, smaller elements make computing so long that it takes a long amount of time to get any result. One might never really know where exactly is the mesh size is on the scale.

It is important to consider chosen analysis for different mesh sizes. As smaller mesh means a significant amount of computing time, it is important to strike a balance between computing time and accuracy. Too coarse mesh leads to erroneous results. In places where big deformations/stresses/instabilities take place, reducing element sizes allow for greatly increased accuracy without great expense in computing time.

If you are an engineering professional, most likely you are aware of how a physical product comes to life. From the early days of sketching and blueprints, manufacturing of a commodity has come a long way. The modern methodology of creating a product has not only changed drastically, but it has become way more efficient and precise in its approach. Today’s engineer lives and thrives in the world of 3-dimensional models. Whatever masterpiece a designer has in his mind, he has the tools and system to give it life. And it is not just limited to inception of a new idea being turned to a product; it has made the art of reverse engineering being implemented more than ever.

So what are the factors that have revolutionized this craft?

It is the safe to say that with the invention of new tools, techniques and computer, the road to new product development has become more smooth, accurate and flexible. Although a professional can get deep into the subject matter, this article gives a brief overview of the product development from technical perspective.

The footsteps to a new product can be summarized in the following sequence.

 

To put it in words, here is how the entire sequence goes:

• Scanning: Whether you have an entirely new idea on your mind, or you want to base your idea on an already existing product; you need a reference. Your reference can be either technical manuals from the manufacturer or the physical product itself. The first step is to scan the product using 3D scanners. 3D scanning technology comes in many shapes and forms. Scanners capture and store the 3D information of the product. The scanned information gets stored in the form of closely spaced data points known as Point Cloud.

• Point Cloud: A point cloud is a collection of data points defined by a given coordinates system. In a 3D coordinates system, for example, a point cloud may define the shape of some real or created physical system.

• Mesh: Point clouds are used to create 3D meshes. A mesh is a network that constitutes of cells and points. Mesh generation involves point clouds to be connected to each other by the virtue of vertices, edges and faces that meet at shared edges. There are specific softwares for carrying of meshing function.

• 3D Model: Once the meshed part is generated, it goes through required software applications to be transferred to Computer Aided Design (CAD) tools to get transformed into a proper 3D CAD model. 3D model is the stage where whole sorts of applications such as sewing, stitching, etc, are implemented to create a prototype.

• Testing: A prototype goes through numerous tests in this phase, to check for limitations and possible calibrations if necessary. This is done to determine the optimum stage where the prototype can be turned to a product.

• Product: This is where the entire process comes to an end. Once a prototype is evaluated and finalized, it is sent for production in order to introduce it to the market.

 This introductory part gives you a summary of product development and the related technical terms. In the next chapters, we will dive deep and go through all the mentioned stages, one by one.

No output is always perfect no matter how much the technology has evolved. Even though point cloud generation has eased up manufacturing process, it comes with its own anomaly. Generally, a point cloud data is accompanied by Noises and Outliers.

Noises or Noisy data means the data information is contaminated by unwanted information; such unwanted information contributes to the impurity of the data while the underlying information still dominates. A noisy point cloud data can be filtered and the noise can be absolutely discarded to produce a much refined result.

If we carefully examine the image below, it illustrates a point cloud data with noises. The surface area is usually filled with extra features which can be eliminated.

 

 

After carrying out Noise Reduction process, the image below illustrates the outcome, which a lot smoother data without any unwanted elements. There are many algorithms and processes for noise reduction.

 

 

Outlier, on the contrary, is a type of data which is not totally meaningless, but might turn out to be of interest. Outlier is a data value that differs considerably from the main set of data. It is mostly different from the existing group. Unlike noises, outliers are not removed outright but rather, it is put under analysis sometimes.

The images below clearly portray what outliers are and how the point cloud data looks like once the outliers are removed.

 

 

 

Point Cloud Decimation

We have learned how a point cloud data obtained comes with noise and outliers and the methods to reduce them to make the data more executable for meshing. Point cloud data undergoes several operations to treat the anomalies existing within. Two of the commonly used operations are Point Cloud Decimation and Point Cloud Registration.

A point cloud data consists of millions of small points, sometimes even more than what is necessary. Decimation is the process of discarding points from the data to improve performance and reduce usage of disk. Decimate point cloud command reduces the size of point clouds.

The following example shows how a point cloud underwent decimation to reduce the excess points.

 

 

Point Cloud Registration

Scanning a commodity is not a one step process. A lot of time, scanning needs to be done separately from different angles to get views. Each of the acquired data view is called a dataset. Every dataset obtained from different views needs to be aligned together into a single point cloud data model, so that subsequent processing steps can be applied. The process of aligning various 3D point cloud data views into a complete point cloud model is known as registration. The purpose is to find the relative positions and orientations of the separately acquired views, such that the intersecting regions between them overlap perfectly.

Take a look at the example given below. The car door data sets have been merged to get a complete model.

 

 

 

 

 

Whether working on a renovation project or making an information data about an as-built situation, it is understandable that the amount of time and energy spent on analysis of the object/project in hand can be quite debilitating. Technical literatures over the years, has come up with several methods to make a precise approach. But inarguably, the most prominent method is the application of Point Clouds.

3D scanners gather point measurements from real-world objects or photos for a point cloud that can be translated to a 3D mesh or CAD model.

But what is a Point Cloud?

A common definition of point clouds would be — A point cloud is a collection of data points defined by a given coordinates system. In a 3D coordinates system, for example, a point cloud may define the shape of some real or created physical system.

Point clouds are used to create 3D meshes and other models used in 3D modeling for various fields including medical imaging, architecture, 3D printing, manufacturing, 3D gaming and various virtual reality (VR) applications. A point is identified by three coordinates that, correlate to a precise point in space relative to a point of origin, when taken together.
There are numerous ways of scanning an object or an area, with the help of laser scanners which vary based on project requirement. However, to give a generic overview of point cloud generation process, let us go through the following steps:

1. The generation of a point cloud, and thus the visualization of the data points, is an essential step in the creation of a 3D scan. Hence, 3D laser scanners are the tools for the task. While taking a scan, the laser scanner records a huge number of data points returned from the surfaces in the area being scanned.

2. Import the point cloud that the scanner creates into the point cloud modeling software. The software enables visualizing and modeling point cloud, which transforms it into a pixelated, digital version of the project. 

3. Export the point cloud from the software and import it into the CAD/BIM system, where the data points can converted to 3D objects.

Different 3D point cloud file formats

Scanning a space or an object and bringing it into designated software lets us to further manipulate the scans, stitch them together which can be exported to be converted into a 3D model. Now there are numerous file formats for 3D modeling. Different scanners yield raw data in different formats. One needs different processing software for such files and each & every software has its own exporting capabilities. Most software systems are designed to receive large number of file formats and have flexible export options. This section will walk you through some known and commonly used file formats. Securing the data in these common formats enables the usage of different software for processing without having to approach a third party converter.

Common point cloud file formats

OBJ: It is a simple data format that only represents 3D geometry, color and texture. And this format has been adopted by a wide range of 3D graphics applications. It is commonly ASCII (American Standard Code for Information Interchange).

PLY: The full form of PLY is the polygon file format. PLY was built to store 3D data. It uses lists of nominally flat polygons to represent objects. The aim is to store a greater number of physical elements. This makes the file format capable of representing transparency, color, texture, coordinates and data confidence values. It is found in ASCII and binary versions.

PTS, PTX & XYZ: These three formats are quite common and are compatible with most BIM software. It conveys data in lines of text. They can be easily converted and manipulated.

PCG, RCS & RCP: These three formats were developed by Autodesk to specifically meet the demands of their software suite. RCS and RCP are relatively newer.

E57: E57 is a compact and widely used vendor-neutral file format and it can also be used to store images and data produced by laser scanners and other 3D imaging systems.

Challenges with point cloud data

The laser scanning procedure has catapulted the technology of product design to new heights. 3D data capturing system has come a long way and we can see where it’s headed. As more and more professionals and end users are using new devices, the scanner market is rising in a quick pace. But along with a positive market change, handling and controlling the data available becomes a key issue.

Five key challenges professionals working with point cloud face are:

• Data Format: New devices out there in the market yields back data in a new form. Often, one needs to bring together data in different formats from different devices against a compatible software tool. This presents a not-so-easy situation

• Data Size: With the advent of new devices, scanning has become cheaper with greater outputs. It is possible to scan huge assets from a single scan. This has resulted in the creation of tens of thousands of data points. A huge data of points can be challenging to handle and share between project partners.

• Inter-operability: Integration between new technologies with the existing software can be quite arduous. Although, with careful investment of time and money, the goal can be achieved nonetheless.

• Access: All the professionals involved in the entire lifecycle of a product can benefit from having access to point cloud data. But multiple datasets in multiple formats usually makes it more of a hassle.

• Ownership: Who owns point cloud data? In the past, EPCs and the contractors who capture the data become custodians of the information.

• Rendering: Different formats can result in rendering problems for point clouds.

The quality control and inspection process in reverse engineering usually take three steps to determine if the 3D CAD model of the part is available or not. Those three steps are as follows:

• The first step involves scanning the part and generating a computer model's point cloud format.
• The second step involves merging and aligning the two computer models (CAD and Scanned one) to validate the part's specifications.
• The final step, i.e., step three, includes visual computer model inspections and alignment of the merged models for any deviations in the geometry and dimensions.

The quality control outcome would be some recommendations for corrections, so the part meets perfectly with the blueprint specifications before the part's mass production.

Following are some uses of Reverse engineering inspection: 

• Reverse engineering inspection comes in handy while carrying out Zero Article Inspection or ZAI. Zero article inspection is a type of workflow where the upcoming physical part doesn't follow the master design model but rather a derivative of the master model due to fluctuations in dimensions and tolerances. The review of the last digital representation before downstream purposes is known as zero article inspection. In this case, reverse engineering inspection provides sufficient information, allowing the inspector to check tolerances, dimensions, and any other information relevant to such projects. It gives assurance and confidence to produce quality components.
• Reverse engineering inspection is an essential inspection of stylized parts/surfaces - where just dimensional inspection is not enough.
• Reverse engineering inspection is an essential player during First Article Inspection (FAI). During the part manufacturing process, when an issue is detected with the manufactured part, the notification of the same has to go back to its design. The purpose is to keep the 3D CAD model in sync with the actual piece as manufactured. The hybridization of reverse engineering and inspection makes such updates easy to convey and apply, plus keeps the feedback circle opens across departments.
• Reverse engineering inspection has found significant use in the additive manufacturing industry. Such a process helps bring down process-generated errors while calibrating & modifying building criteria to cut down the process itself's influence.

3D Scanning – The process of collecting 3-Dimensional data from physical object through a variety of data acquisitions devices.

3D to CAD: The process of converting 3-Dimensional point to a dimensionally defined graphic model.

Accuracy: The extent of how close a measurement is to the recognized true value.

Annotation Models: A digital model containing specific coordinate locations verifying deviations from nominal data.

AS IS CAD: A CAD model that represents actual manufactured parts rather than a designed CAD model.

CAD (Computer-aided design): The use of computer technology to assist in the creation, analysis, or modification of a design.

CAM (Computer Aided Manufacturing): The use software that can support both machine tools and 3D CAD modeling capabilities.

Cone beam: A conical-shaped x-ray beam that produces two dimensional images of an object.

Design intent: The process of taking a manufactured part into account with inherent errors and modifying the same till it is true.

GD&T (Geometric Dimensioning and Tolerancing): System of languages and symbols used for defining and communicating engineering tolerances.

Hand held scanner: Portable camera that is used for capturing 3D imagery of objects with a laser or structured light based.

Hybrid Model: Combining two different modeling processes to accurately define 3D geometry.

Laser Scanner: A device used to capture 3D surface geometry, consisting of a laser output and a sensor to interpret the data.

LIDAR: A combination of the words: “Light” and “RADAR.” A LIDAR scanner employs RADAR’s technique of emitting a signal and measuring distances to objects based off of the signals reflection.

Long Range Scanning: Acquiring data at expansive distances from hundreds of feet away to miles away. Data can be captured through a variety of devices including LIDAR, Time of Flight and phase shift scanners.

Modeling: Digitally creating 2D or 3D object using CAD or data manipulation software, such as Polyworks, Geomagic or Solidworks.

NanoCT: Capturing images using CT (Computed Tomography), with a resolution of the images defined in nanometers.

Parametric Model: A sketch driven model that builds a design tree that can be opened in a CAD environment and allows the operator to manipulate the model.

Parametric Modeling: This process is taking 3d scan data and through the use of design tools, creating a sketch driven model with consistent relationships between features in the feature tree.

Phase Based Scanners: LIDAR Scanners that take measurements by sending laser pulses towards an object and measuring the phase shift of the pulses’ reflection off of the object.

Point Cloud: A set of points defined by X, Y, and Z coordinates that represent the external surfaces of an object.

Prismatic Modeling: Creating CAD geometry using basic geometry shapes, i.e. planes, cylinders, cones etc, to define correct shapes of the 3D geometry.

Re-Engineering: The process of modifying an existing part or assembly of parts digitally to improve its performance or use.

Repeatability: The variation in measurements taken with the same piece of equipment, under the same conditions, across multiple tests.

Reverse Engineering: The process by which a man-made object is dismantled to reveal its architecture, designs, or to extract knowledge from the object in order to know about its functioning and structural integrity.

SCAN to CAD: The process of collecting 3D data using 3D scanning hardware and converting the dimensional data to CAD format using a variety of software packages.

2D Drawings / Schematics: A 2D print that describes the physical characteristics of an object, how it should be made, assembled, handled, etc. These can be used to provide basic dimensional values to define its function.

Sectioning: The process of creating 2D profiles through sections of an object.

Short range scanning: A process used to collect dimensional data in 3D space from short ranges.

Solid Modeling: Defining an object with CAD tools such as extrudes, revolves, sweeps, etc. A solid model is enclosed and is said to have mass and volumetric values can be calculated.

Surface Model: An objects exterior skin defined by CAD features or NURBS surfaces.

Triangulation Scanner: Projecting a known pattern of light grids or fringe onto an object in order to calculate surface geometry by analyzing the distortions of the pattern.

White Light Scanner: A 3D camera projecting a known pattern of light grids or fringe onto an object in order to calculate surface geometry by analyzing the distortions of the pattern.

Sometimes, situations arise where you don’t have access to a part’s original design documentation from its original production. This might be due to the absence of the original manufacturer altogether or stoppage on the production itself.

Reverse engineering empowers us to analyze a physical part and explore how it was originally built to replicate, create variations, or improve on the design. The goal is to ultimately create a new CAD model for use in manufacturing.

Let us take a look at the steps involved in reverse engineering. Commonly, it involves careful executions of the following steps:

• Scanning

The first step involves using a 3D scanner for collecting the geometric measurements and dimensions of the existing part quickly and accurately using projected light patterns and camera system. Generally, the types of scanners used for such execution are blue light scanner, white light scanner, CT scanner and /or laser scanner. The former two captures the outward dimension and measurements while the latter two is capable of scanning the entire inside out.

• Point Cloud

Once a certain part is scanned, the data gets transformed in the form of point clouds. Point cloud is a 3D visualization consisting of thousands or even millions of points. Point clouds define the shape of a physical system.

• Meshing/Triangulation

This stage serves involves conversion of point clouds to mesh (STL or Stereolithographic format). Mesh generation is the practice of converting the given set of points into a consistent polygonal model that generates vertices, edges and faces that only meet at shared edges. Common software tools used to merge point clouds are Polyworks, Geomagic, ImageWare, MeshLab. The meshed part is then run for alignment in the mentioned software tools.

• Parametric/Non-parametric Modeling

After the meshed part is aligned, it goes through either of two stages. The first option involves applying surface modeling on meshed part in tools such as Polyworks. It results in the generation of non-parametric model (IGES or STEP format). An alternate option is creating a sketch of the meshed part instead of putting it through surfacing. This work-process is known as parametric modeling (.PRT format). For a non parametric model, predicting future data is based on not just the parameters but also in the current state of data that has been observed. For a parametric model to predict new data, knowing just the parameters is enough.

• CAD Modeling

The next stage consists of transferring the data through CAD software tools such as NX, Catia, Solidworks, Creo etc, for applying functions such as ‘stitch’, ‘sew’, ‘knit’, ‘trim’, ‘extrude’, ‘revolve’ etc for creation of 3D CAD model.

• Inspection

This stage includes visual computer model inspections and alignment of the merged models against actual scanned parts (STL) for any discrepancies in the geometry as well as dimensions. Generally, inspection is carried out by using tools such as Polyworks or Geomagic. Reverse engineering inspection provides sufficient information to check tolerances, dimensions and other information relevant to the project.

• Documentation

Documentation of 3D stage model depends solely on one’s technical/business requirements. This step is about converting 3D model to 2D sketch, usually with the help of tools such as inventor or Isodraw/Coraldraw, citing measurements which can be used for reference in the future.

 

As we have seen during the introduction, the first step to reverse engineer a product is through scanning with the help of 3D scanners. Early eras have seen the painstaking task of obtaining dimensions of an existing product. These methods were time consuming and needed attention to details from the very first stage.

However, with the rapid development in the scanning technology, the inception of a product has caught speed and the chances of errors have reduced dramatically which has made 3D scanning and measurement an important part, starting from design stage to inspection stage.

3D laser scanning is the technology to capture a physical object’s exact size and shape using a laser beam to create a digital 3-dimensional representation of the same. 3D laser scanners create “point clouds” of data from the surface of an object.

We will go through point clouds in later sections.

3D Scanning Technology

3D laser scanning efficiently takes the measurements of contoured surfaces and complex geometries which require huge amounts of data for their accurate description because doing this with the use of traditional measurement methods is impractical and time consuming. It creates accurate point cloud data by acquiring measurements and dimensions of free-form shapes.

The basic working principle of a 3D scanner is to collect data of an entity. It can either be:

• an object
• an environment (such as a room)
• a person (3D body scanning)

In reverse engineering, laser scanner’s primary aim is to provide a lot of information about the design of an object which in the later stages gets converted to 3D CAD models, considering the compatibility of 3D scans and Computer Aided Design (CAD) software. 3D scans are even compatible with 3D printing which requires some specific computer software.

3D scanning technologies varies with different physical principles and can be classified as follows:

• Laser triangulation 3D scanning technology: In this category, the laser scanner projects a laser beam on a surface and measures the deformation of the laser ray.

• Structured light 3D scanning technology: This technology acquires the shape of a surface by measuring the deformation of a light pattern.

• Photogrammetric technology: It is also known as 3D scan from photography. It reconstructs an object from 2D to 3D and has specific computational geometrical algorithms for the task.

• Laser pulse 3D scanning technology: This unique process collects geometrical information by evaluating the time taken by a laser beam to travel between its emission and reception.

Contact based 3D scanning technology: This process requires contact between the probe and the object, where the probe is moved firmly over the surface to acquire data.

Types of scanners

Apart from scanning technologies, there are various types of 3D scanners. Some are built for short range scanning while others are ideal for medium or long range scanning. The built and usage of specific scanners hugely depend upon size of the object to be scanned. The scanners used for measuring small objects vastly differ from the ones that are used for large bodied objects, such as a ship.

Here is a brief summary of types of 3D laser Scanners:

1. Short Range 3D scanners: Short Range 3D scanners utilize either a Laser triangulation technology or Structured Light technology.

2. Laser based 3D scanners: Laser scanners work by projecting laser a beam or multiple laser beams on an object and capturing its reflection with sensors, which are located at a fixed distance from the scanners.

3. Structured light 3D scanners: These are also known as white light scanners. However, most structured scanners use blue or white LED light. The light pattern usually consists of a certain geometrical shape such as bar or block or any other shape, which is projected onto the object. The sensors consider the edge of the pattern to determine the 3D shape of the object. Blue or white light scanners are generally used to obtain outward dimension.

4. Medium and Long range scanners: Long range 3D scanners are used for large objects such as buildings, ships, aircrafts, and military vehicles. These scanners rotate and spin a mirror which reflects the laser outward towards the object or areas to be 3D scanned.

5. Arm based scanners: Arm based scanners are very useful when measuring small minor parts, as it can be maneuvered by attaching it to the arm and is generally portable.

Benefits of 3D Laser Scanners

3D scanners have contributed a lot over the years and needless to say, it comes up with many benefits. Some of them are as follows:

• Able to scan tough surfaces, such as shiny or dark finishes.
• This is strictly for the handheld or other portable scanners. But given their importance, it is safe to say that the portability of scanners has played a great role in easing up engineering.
• The scanning technology has made it possible to capture millions of point in a considerably less time.
• Scanners are less sensitive to changing light conditions and ambient light.
• Scanning of complex contours and geometrical figures have become more convenient with the invention of groundbreaking scanning technologies.
• Nowadays, laser scanners have become so diverse that they are produced depending upon variety of projects or the objects to be scanned.