Top 5 Points on Implementation of VR in Product Designing

CADDZOOM

Today’s most ground-breaking manufacturing companies are saving thousands of dollars and reducing construction timing by implementing virtual reality (VR). Whether these organizations are designing or authenticating their concepts, or seeing their application in real-time on the construction floor, immersive imagining is proving its worth. Therefore, we have listed below some points to demonstrate the application of VR, check it out:

1. CAD-oriented product design

VR is mainly useful to product design teams when they are testing and interrelating with the product.

2. Distinguish in details

Interpreting a virtual product design at a high frame rate is significant for interactive skills that need a lot of movement on the portion of the user. Understanding that there is a catch-22 between assigning resources for geometric difficulty and frame rate.

3. Aesthetics of geometry

Product designs vary importantly, and therefore, significance of aesthetics versus functionality needs to be understood well in advance. High-quality, fully detailed forms are important features that help communicate the aesthetic value.

4. Implementing VR system is not a piece of cake

Though the charge of implementing a VR system for product design continues to go down; however, the financial resources required to build, function and continue it is substantial. Getting people on board internally will be as stimulating as convincing decision-makers in management to pay money.

5. Return on Investments must not depress

Once the VR system is upheld at your organization, track how frequently people are using it, what they are using it for, and why they are using it on database. These records can help you create approximations for potential gains.

Precisely VR system not only helps you design team with advance procedures, but also supports product’s lifecycle. More so, it assists the team understand the ins and outs of installing, working, upholding and fixing a VR system design to produce greater ROI.

NOW ANSYS 19.2 WITH FASTEST CFD, AUTONOMOUS VEHICLES AND MANY MORE

CADDZOOM

ANSYS a company which develops, markets, and supports engineering replication software used to forecast how product designs will act in real-world environment. Engineering simulation is their sole focus. Their latest release of ANSYS 19.2 software has updated tools that will support autonomous vehicles designing, additive manufacturing, optics and assembly. Some of the important features of ANSYS 19.2 are:

Automotive System

ANSYS 19.2 introduced the ANSYS VRXPERIENCE, which permits operators to fully and realistically simulate independent vehicles using real-world customizable conditions.

Increased Use of Optics

ANSYS 19.2 contains a capital of general optical imitation tools, such as ANSYS SPEOS which is one solution for imitation of optics and optoelectronics. These new tools permit imitation of lighting, interior and exterior lighting, cameras and LiDARs, and delivers designers with the ability to assess optical performance while reducing expansion time and costs.

Digital Twins Bug

New capabilities of ANSYS 19.2 make it easier to shape, authenticate and organize digital twins more quickly. Now, users can create images of 3D fields of static ROMs (Reduced Order Modeling) and view simulation results, such as speed and flow rate, on the 3D geometry of the twin.

Preservative Manufacturing

ANSYS 19.2 continues to deliver the Additive Suite and Additive Print tools for imagining 3D printing processes. It also offers a beta version of Additive Science. This technology allows operators to simulate the micro structure and advance insight into the properties of the final published part.

Computerized Fluid Dynamics

In the fluids suite, ANSYS 19.2 delivers new features to accelerate CFD simulations for boosting productivity. It works on the basis of Mosaic meshing technology.

Discover Live Updates

This feature gives a boost to its abilities by allowing users to account for the angular speed of rotating components in a fluid simulation. This is especially useful for understanding the result of wheel rotation on a vehicle and detecting real-world trends in virtual designs.

Physical Simulations

The new Material Designer feature can make full models of fiber-filled, woven or lattice resources, and then calculate equivalent belongings for use in larger-scale imitations. In topology optimization, ANSYS 19.2 has extra loading options, manufacturing restraints that are perfect for preservative manufacturing, and a sole lattice optimization capability.

Overall, the features mentioned above shows some additional features incorporated in ANSYS 19.2. Precisely, these additional features would improve the productivity of ANSYS and would enhance the overall user experience.

Engineering Education and Job Satisfaction

CADDZOOM

A couple of years back, a bachelor’s degree in engineering was assumed to be enough to achieve success in the career. But, with more competitiveness in the industries, the employers now seek candidates that hold advanced degrees in engineering. These degrees also helps the professional to move into job roles that are more challenging, highly paid, and with more responsibilities.

Recent research about degrees and job roles stated that:

  • Bachelor’s degree was the most common degree across the technical industries.
  • Master’s degree held the second highest position.
  • An exception was seen in the education sector that held a higher number of doctoral degrees.

Degree Types and Job Roles

Degrees plays a significant role in the employee job responsibilities. A match between the job roles and education is thus important for success. Therefore, people who wish to succeed in their career opt for advanced degrees in their field. It is often observed that:

  • Most of the successful people working in the same field of their education had both a bachelor’s and a master’s degree.
  • These professionals persuaded for a higher level of education to gain a deeper understanding of their fields and advance on their career paths.

Job Satisfaction

It is one of the most motivating factors to go for an advanced degree.

  • Most of the professionals believe that having a masters or a PhD degree helps them excel in their career.
  • It helps to gain an improvedbreadth of knowledge and expertise in their filed.
  • It gives professionals a higher level of job satisfaction and enjoyment of their tasks.
  • Lastly, it offers a high range of salary which gives them a feeling of satisfaction.

Does Digitalization Affect Manufacturing Economics?

CADDZOOM

Digitalization has revolutionized the entire manufacturing sector. The labor-intensive industry has transformed into a technology-driven industry. Technology such as 3-D printing, CNC machining, industrial internet of things (IIoT), and artificial intelligence (AI) have optimized the manufacturing operations and have considerably reduced the production and maintenance cost. The details of this technological impact on manufacturing are listed below:

  1. Economically Viable Mass Customization

Digital manufacturing helps automate front-end engineering associated with customization. Interactive design for manufacturing (DFM-Design for Manufacturing) tools helps to analyze the design components before manufacturing, which significantly reduces manufacturing risks and enhance productivity. Precisely, these tools improve traceability across the manufacturing process making it more flexible and efficient. Concisely, this digitalization decreases the tooling cost and makes customization economically feasible.

  1. Improved Product Design and Manufacturing

Additive manufacturing is enabling manufacturing organizations to create new and improved products at a lower price. Under the additive manufacturing, engineers have design freedom and assembly reduction, which permits them to customize the product design according to the needs and demands of the industry and customers. Overall, the digital revolution is enhancing product quality, quantity, and variety.

  1. Low Maintenance Cost and Downtime

Machine learning and AI(Artificial Intelligence) could effectively reduce the maintenance and downtime cost. It is because planned and anticipated maintenance is more efficient compared to maintenance of unplanned problems. So, digital manufacturing helps to anticipate potential risks which eventually reduces the cost incurred due to unexpected events.

  1. Generating Revenue by Accelerating Production

Digital Manufacturing automates the process of design analysis, toolpathing, and front-end processes. This automation helps in the easy prototyping of product which in turn simplify the supply chain management. With the readily modeled design, manufacturers could enhance the procurement process and subsequently reduce the production time. As a result, the product is launched to the market in time, and high revenues are generated benefitting the manufacturing firm.

Overall, with the digitalization of the manufacturing industry, the labor-intensive work has reduced, and the productivity has increased exponentially. Consequently, the profits have escalated the cost of productivity and maintenance have decreased. All in all, the useful technology innovation in the manufacturing sector have benefitted the overall industry and have paved the way for improved production.

Artificial Intelligence Learns from Patient Data to Refine Cancer Treatment

CADDZOOM

Artificial Intelligence (AI) and Machine Learning techniques in healthcare are reaching new heights through research and innovation every day. AI is used currently across all the treatment stages, right from the disease diagnosis, sample analysis, upto their medication and cure.

To make further studies, a team of researchers from the MIT(Madras Institute of Technology) focused on the brain cancer treatment. They reduced the deadly chemotherapy and radiotherapy dosage for the glioblastoma patients to obtain test results.

The team used a research model based on a technique known as the Reinforced Learning (RL). This method was inspired by behavioral psychology in which the model learns by reading the patient’s behavior automatically to generate the desired outcome. The adapted RL model which studied the treatment of glioblastoma used a combination of drugs such as Temozolomide (TMZ), Procarbazine, Lomustine, and Vincristine (PVC). For the test, the researchers:

  • Took a trial on a set of 50 patients.
  • For every patient, the model conducts about 20,000 trial-and-error tests.
  • The model, either initiates or withholds a dose for a patient.
  • Based on the reaction, it then decides what amount of dosage is sufficient.
  • The treatment cycles of the AI model reduced the effects of the disease to a great extent.

The RL model offers a significant improvement over the traditional “eye-balling” method of administering the patient dosage. The model observes how every patient responds towards the medication given to them and adjusts them accordingly to get the desired results. In this way, AI is learning new ways of medication from the patient data.