Search results

This review will describe the development of the Bionic Handling Assistant as well as the additive manufacturing (AM) process of robot grippers and its possibilities.

AM offers the chance to use the additive processes to produce highly flexible automation parts and systems as the Bionic Handling Assistant in small and medium quantities that can utilize a lot of design advantages provided by the process.

A lot of products of today and especially tomorrow could be produced by rapid manufacturing. New categories of products, such as the Bionic Handling Assistant, will occur.

In the paper, aspects of a visionary scenario for future productions are shown and demonstrated on the Bionic Handling Assistant.

Rapid manufacturing processes provide designers, mechanical and process engineers with a lot of chances and opportunities. It is necessary to show them, how their work and their processes will change due to these new technologies.

Rapid manufacturing offers the chance to use the additive manufacturing processes to produce not just prototypes but advanced functional parts in small and medium quantities that can utilise a lot of design advantages that are provided by the process. We consult, as a German applied research institute, independent companies, helping them to introduce new product development and manufacturing processes, rethink the design of their products and to be aware of the advantages of upcoming manufacturing technologies.

Finds that a lot of products of today and especially of tomorrow could be produced by the new rapid manufacturing processes today and at competitive costs, if their design was adapted carefully using the new possibilities. New categories of products will come up too.

The acceptance of rapid manufacturing as a new production technology is still limited by the available manufacturing systems. The number of released materials is still small and the accuracy of the parts and the building speed is still not exactly rapid. The mindset of the decision makers and of the R&D departments has to be radically changed. There is a lot of development going on, so the situation will change…

Aspects of a visionary scenario for future productions and products are shown and some examples are demonstrated based on a customised robot gripper.

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.