Virtual prototyping tools have already captivated DoD interest as a viable design tool. One of the key challenges is to extend the capabilities of Virtual Reality technology beyond its current scope of design reviews. Here we present the design and implementation of a Constraint Site Visit Executive Simulation designed to support interactive assembly and disassembly tasks within virtual space.
Smart block configurations can be instantly/securely sent and received reducing exposure/delays in rear echelons. As an example, oversight of Manoeuvre Requests could be securely implemented with greater transparency and also potential battlefield applications messaging system could be leveraged during instances in which troops are attempt to communicate back to HQ using secure, efficient and timely logistics system.
Most design engineers approach disciplines addressed one at a time before moving to the next one, and multiple iterations are performed through the design process in order to converge into a single solution. Each loop is a serial process that must be done in order, and control of each design variable must be carefully executed. The tech modules are highly coupled so that the dynamic process of integration is stable and converges on a solution.
But we have promoted an approach where discipline-specific designs are done in parallel across a broad design space. This process is designed to improve the flexibility of the design by delaying key decisions until the design space is fully understood, and the parallel approach also makes the process well fit for machine leaning application.
The direct relationship between the Sectional Construction Drawing, Planning and Sequence documents, and the Master Construction Schedule provides Job Site workforce with new tools to improve the logistics of a very complex process.
Capability of a Zone Logistics/Sectional Construction Drawing based plan must be progressed from the standpoint not only of cost but of overall schedule with far greater confidence that a conventional system structure component standup system drawing approach.
Zone logistics techniques are the focus for providing all the necessary requirements for constructing an interim product. Design products are brought into the conversation since the timely delivery of design products, that is, drawings, is particularly significant.
Key techniques employed by the Constraint Site Visit Executive Simulation are direct interaction, automatic constraint recognition, constraint satisfaction and constrained motion. Several optimisation techniques have been implemented to achieve real-time interaction with large industrial models.
Constraint-based approaches for virtual assembly simulations must be combined with physics-based investigations where geometric constraints are created or deleted within the virtual space at runtime. In addition, solutions are provided to low clearance assembly by utilising representation of complex models for accurate collision/physics results.
Constraint Site Visit Executive Simulation must also be able to validate recognised and applied constraints. The validation is the process that determines whether a constraint is still valid or is broken. A constraint is broken if the involved surfaces attempt to move apart beyond a defined threshold.
The goal of a lot of scientific and engineering activities has long been regarded the discovery of structural configuration. The design tasks in engineering sometimes need to combine the predefined components in order to obtain a desired configuration in a realistic time.
A predfined component is described by a set of properties, by a set of ports for connecting it to other components and by structural constraints . The configuration tasks select and arrange combinations of predefined components that satisfy all the requirements.
Configuration can be defined as special case of design activity feature product assembled from fixed set of pre-defined components connected in pre-defined ways. Selecting and arranging combinations of parts satisfying specifications is core function of configuration task.
Configuration comprises selection/instance parameters and composition of components out of pre–defined set of types so goal specification and set of constraints characterise domains
Configurable products are important in domains where standardised components are combined into customised products. A configuration task takes as input a model which describes the components that can be included in the product and a set of constraints that define how components can be combined, and requirements that specify properties of the product to be configured.
Output is a description of a product to be manufactured, a configuration. It consists of a set of components as well as a specification of how they interact to form the working product. The configuration has to satisfy the constraints in the model and the requirements.
In some configuration tasks optional components may be added or some components may require the existence of another component. This type of task leads to a constraint problem in which the set of variables that must be assigned a value may change in response to choices made in the course of problem solving. The solutions to such a problem differ in the sets of variables that are assigned values.
Constraint problems derived from design and configurations tasks often use components/structured values as domains of constrained variables. Most existing methods are forced into unnecessary search because they assign complete components to variables.
Partial choice is introduced as a way to assign a part of a component. The basic idea is to work with descriptions of classes of solutions as opposed to the actual solutions to reduce search and in the best case eliminate search. A distinction is made between a partial commitment ie, a partial choice that would not be retracted and a partial guess.
One technique utilised to implement partial choice problem solving organises choices into family classifications. Use of family organisation not only helps in paring down the search space but also provides a compact dispatch communication structure for describing solutions and representing constraints.
A product configurator has been an effective application tool in successful implementation of mass customisation strategy. It enables manufacturers to automatically generate product configuration information tailored to individual customer requirements.
But current product configurator techniques are not adequate to solve an engineering product configuration problem, because the constraints in such a problem are often expressed by mathematical formulae and computable procedures. This type of constraint posts challenges on constraint modelling and solving within the constraint satisfaction paradigm.
Constraints made up of computational for procedures are not naturally supported with pre-defined constraint semantics in a constraint model. It is also difficult to achieve search efficiency for constraints over continuous variables.
Here we present an innovative approach to modelling and solving an engineering product configuration problem based on the constraint satisfaction paradigm. It aims at developing methodology for a generic configurator that is able to solve an engineering product configuration problem with complex constraints.
The engineering design process can be considered to be constraint oriented. It involves the identification, negotiation and resolution of constantly changing set of constraints. Key characteristic of engineering design is that such problems are rarely as simple as satisfying a single objective with all the design variation continuous and unbounded.
As design factors develop, the designer can miss or overlook some of these constraints. To overcome this, there is a supportive approach which allows the designer to annotate the initial configuration design drawn models with the design constraints.
These constraints are then maintained with the model as it evolves, this presents the opportunity to refine the constraints when the design activity requires. The approach has been created to support manufacturing machinery design and is demonstrated with an industrial case study.
Constraints are imposed conditions, rules or limiting factors. Geometric and numeric constraints occur in engineering and computer-aided design, with applications in a number of mechanical design areas, including architectural drafting and robotics.
There is a clear difference between a geometric constraint and a numeric constraint. Simply put, a geometric constraint relates to other parts of a geometric figure, whereas a numeric constraint is a set number not relative to other parts of a design. Both geometric and numeric constraints define the dimensions of objects in computer-aided design modeling systems.
Geometric constraints define specific points on geometric objects and determine their orientations to other objects. Some examples of geometric constraints include parallelism, perpendicularity, concentricity and symmetry. Parallelism occurs when two or more lines or axes of curves are equidistant from each other. Perpendicularity is a constraint in which lines or axes of curves intersect at right angles. Concentricity arises when two or more arcs, circles or ellipses share the same center point. Symmetry occurs when selected lines or curves become symmetrically constrained around a selected line. configuration design drawn method called "geometric constraint solving" involves finding the configurations of lines, points, circles, and other geometric figures that are constrained to have established relationships to each other.
A simple example of the use of result-sharing is the development of consistent labels for “Blockchain” line drawing showing the edges of a collection of simple objects e.g., cubes, wedges, and pyramids in a scene.
Each image is represented as a graph with nodes that correspond to the vertices of the objects in the image and arcs that correspond to the edges that connect the vertices. The goal is to establish a correspondence between nodes and arcs in the graph and actual objects.
Your ability to deal with complex changing structures means that computers can now be applied to direct systems such as networks of trading partners that formerly required extensive manual attention. Increased directive complexity also extends the scope of operational applied approach problems.
Using the auto configuration design drawing application, you can constrain two geometric objects by performing certain commands. For example, you can select a location on a figure and then select a location on another figure that will move toward the first until those selected points coincide.
Until you remove the constraint, these objects will continue to have this relationship. If you use a command on a constrained object, it affects the other objects that depend on the constraint. For example, when you constrain two objects to be symmetrical, rotating the line of symmetry will rotate the constrained objects as well. The "Auto Constrain" feature in auto configuration design drawing applies a set of constraints automatically, depending on your choice of objects.
Configuration design drawing is design program that uses constraints. "Constraint Manager," feature which automates the process of working with different types of constraints. It allows you to manipulate the spatial relationship between objects and develop predictive outcomes during the design modification process.
Using constraints properly is an effective, time-saving technique as you execute your design. There are comprehensive tutorials available, both online and offline, providing step-by-step instructions on how to use configuration design drawing "Constraint Manager."
“Constraint Manager” Simulation identifies new possible constraints and validates existing ones. The application specifies a list of objects to be searched for new constraints and possibly the surfaces to be tested for new constraints. If the application can determine collisions between surfaces, it can send those colliding surfaces to “Constraint Manager” Simulation. This speeds up the recognition process because it cuts the number of surfaces to be tested.
As the technology has advanced capability of applications has improved dramatically, allowing designers and engineers to manipulate objects on a screen in 3D and make infinite modifications that in turn are quickly translated into code automatically. This has greatly sped up the process of machining, allowing operators of even limited experience the ability to successfully create acceptable finished parts.
Technically “Constraint Manager” doesn’t need to know code. If the configuration design drawing has already created a cutting program, it feeds that information to the machining centre. Applications have already determined the “speed and feed”, the tool path and all the other variables needed to make the part. The operator can simply press the start button and watch the part being made, but there are some problems to this approach
Configuration design drawing programmes do not always produce the optimal tool path for the fastest and most efficient cutting of a part, especially for complex geometries. This is because, as mentioned above, it is learning point-by-point and step-by-step, not taking the entire picture into account.
Only a “Constraint Manager” with real-world experience is capable of determining the ideal function of the machine tool to meet the expectations of customer design intent. configuration design drawings are also optimised for maximum safety and machine tool life, which translates as slow. Sometimes very slow.
Also, configuration design drawning applications can sometimes make errors, or create a cutting programme that may need to be tweaked. If an operator doesn’t know how to modify individual lines of code they would then need to programme the job again from scratch, wasting valuable time, but the experienced operator can fine tune the program, one line of code at a time, to create the most efficient program to create the highest quality part with the lowest cycle time.
Let’s look at how we optimise machine programming to make parts faster while improving quality and consistency. code is the generic industry term for the computer language that most assembly machines use to control their movements and how they make parts.
Code is created as the output from advanced configuration design drawing aided design/computer aided manufacturing applications. Since there are many different brands of design applications available the type of code they generate will also be different. However, most major brands have translation ability that allows them to be compatible with the vast majority of commercially available machines.
Each line of code tells the machine to perform one discrete action, including position, speed, rotation, etc. Shapes are made by stringing together point-by-point sets of instructions. Even simple parts can require hundreds or thousands of lines of code, and ultimately they must all work perfectly together to achieve the desired result.
Because design code works as a series of points and line-by-line instructions, it is possible to make the same part using a variety of paths and instructions, and the resulting parts are not always the same.
We specialise in rapid tooling, rapid prototyping and fast turnaround low-volume production, we deal with a steady influx of new designs every day. That means we must be experts at a number of techniques, and we also must create new cutting programmes all the time. Our operators are proficient in using code and they’re mentored by master machinists with decades of manual and experience.
We know how to get the most out of our advanced equipment, eliminating downtime while working to tight tolerances that are repeatable, time after time. Contact one of our customer service engineers to find out how our team can optimise the making of your next project.
1. Explain forward checking on basis of simple example
2. Develop feature model for your choice of product domain
3. Reasoning over variables states
4. Only active variables part of solution
5. Active constraints determine variable activity status
6. Represent of variables/constraints not components
7. Not applicable if components depend on user preference
8. "On the Fly" generation of components required
9. Must improve maintenance of configuration models
10. Automate translation into representation execution