Using 3D printing at Pre and Post-Production Processes Saves Millions of Dollars For The Manufacturers
Manufacturing is an ongoing business throughout the world. The goal of any production company is to save time and produce more revenue. Car manufacturing companies, for example, extend a lot of labor usage to produce even one car.
Manufacturing is an ongoing business throughout the world. The goal of any production company is to save time and produce more revenue. Car manufacturing companies, for example, extend a lot of labor usage to produce even one car. There are many different ways to attempt some control and efficiency over the production cost, however. German technology has come up with some new ways of cutting back on production time and costs.
By analyzing and predicting the necessary needs before starting the production of a vehicle, the proper plans can be put in place. Changes can also be initiated in the original plan if needed. All of this can be done before any manual labor takes place, therefore, saving time and energy. There will be no need for the “practice” round in the shop. The technology will take care of the prep work.
Other digital help will come in the form of planning for customer satisfaction. The quality of products can be determined and improved, as well as better pricing options. Digital decision making with algorithm based programs can analyze quickly and efficiently. The development of products in the workplace will change forever.
The new 3D printing availability will also allow for replacement parts to be produced as needed. This can help save money by eliminating the need to order parts way ahead of time and often replacing them before they need to be changed. This will help machines to work at their best capacity for as long as possible before they are maintenance. Necessary repairs can also be made quickly.
Story Via; Harvard Business Review
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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