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Generative Design for Performance and Weight Reduction(으)로 돌아가기

Autodesk의 Generative Design for Performance and Weight Reduction 학습자 리뷰 및 피드백

4.8
별점
83개의 평가
20개의 리뷰

강좌 소개

There are many considerations and factors that play a part in designing a new product. Cost is usually a big one, but sometimes there are other factors that are the main contributors to a product's direction. With vehicles, specifically motorcycles, we see advanced engineering practices performed on seemingly minor parts. In some instances, making a part as light as possible can have a big effect on performance. In other cases, the strength or stiffness of a part, such as a motor mount, is more critical than its mass. Generative design allows us the ability to solve for both problems at the same time and make informed design decisions without the sacrifice. In this course, we’ll explore how generative design can be applied to motorcycle parts to help reduce mass while also increasing performance. You’ll need a paid subscription to Fusion 360 to complete the assignments in this course. Be sure to review your access or payment options before enrolling: https://www.autodesk.com/products/fusion-360 Want to take your learning to the next level? Complete the Autodesk Generative Design for Manufacturing Specialization, and you’ll unlock an additional Autodesk Credential as further recognition of your success! The Autodesk Credential comes with a digital badge and certificate, which you can add to your resume and share on social media platforms like LinkedIn, Facebook, and Twitter. Sharing your Autodesk Credential can signal to hiring managers that you’ve got the right skills for the job and you’re up on the latest industry trends like generative design. Enroll in the Specialization here: https://www.coursera.org/specializations/autodesk-generative-design-manufacturing...

최상위 리뷰

B
2021년 11월 21일

This is an excellent introductory course for Generative Design. It is properly organized and appropriately presented with a laser focus on Generative Design. I enjoyed every moment of the class.

SR
2020년 5월 8일

Thank you for this amazing course. Special thanks to AUTODESK for providing unlimited cloud credits to students like me.

필터링 기준:

Generative Design for Performance and Weight Reduction의 20개 리뷰 중 1~20

교육 기관: lateef b

2021년 11월 22일

This is an excellent introductory course for Generative Design. It is properly organized and appropriately presented with a laser focus on Generative Design. I enjoyed every moment of the class.

교육 기관: Carlos A N T

2020년 8월 18일

Great course. The examples, activities, and challenges are well chosen and give a complete state of the art about weight reduction using generative design.

교육 기관: Sachin R

2020년 5월 8일

Thank you for this amazing course. Special thanks to AUTODESK for providing unlimited cloud credits to students like me.

교육 기관: sagar k

2020년 6월 12일

Another supirior quality course from coursera and autodesk in generative design series. Happy to complete second one.

교육 기관: Kasuntha M

2020년 11월 20일

Great course to enhance your knowledge with practical applications. Thank you all for the immense support.

교육 기관: Deleted A

2021년 7월 28일

Great course with unique applicable study to industry.

교육 기관: Juan D

2019년 11월 27일

Excellent! course to improve your skills!! Liked it!!

교육 기관: Afif S

2020년 6월 30일

Really a great course for mechanical engineers.

교육 기관: nithin c

2020년 5월 20일

Good Explanation of course by instructor

교육 기관: VIGNESH R

2020년 9월 13일

This course is really helpful for me

교육 기관: Nathan B

2020년 1월 7일

Fun course with great content.

교육 기관: Pritam A p

2020년 7월 7일

Excellent course to cover.

교육 기관: alba

2021년 1월 6일

love the final challenge

교육 기관: Adnan M T

2021년 1월 11일

Thanks Coursera.

교육 기관: acintron

2020년 2월 20일

Excellent class.

교육 기관: SIVAKUMAR.G

2020년 7월 9일

Good

교육 기관: AKARSH B

2020년 10월 19일

Very good course to understand and learn Generative design.

교육 기관: Himanshu B

2020년 8월 10일

Great course

교육 기관: SENTHILKUMAR C

2020년 7월 11일

Nice

교육 기관: Mehmet B S

2020년 8월 29일

poor