Progress in applied research for sustainable machine tools and forming technologies bases upon industrial and environmental requirements for resource efficiency. Relevant technical trends base upon impact studies and applied research projects on the lifecycle resource consumption for manufacturing processes and systems. This paper gives an overview about a unified methodological approach of the evaluation of resource efficiency of machine tools. It answers the scientific question on sustainability: which technological parameters and machine tool characteristics lead to their lowest resource consumption/losses and part manufacturing costs. Therefore, the method allows to consider them as an energy-information model, in which the transformation of any forms and types of energy, material, and information takes place. It is shown that innovative hollow shaft forming technologies become sustainable alternatives to cutting technologies. A smart factory uses digitalization, manufacturing data management, and self-learning methods for resource efficiency. Sustainable production requires robust and error-free machining processes. Therefore, a collision prevention system protects machining centers and work pieces from collisions in real time will be presented. The gathered information about the product and its properties as well as manufacturing data builds a digital twin and enables a prediction of the resource consumption in smart factories.

可持续机床和成型技术的应用研究进展基于工业和环境对资源效率的要求。相关技术趋势基于对制造过程和系统的生命周期资源消耗的影响研究和应用研究项目。本文概述了一种用于机床资源效率评估的统一方法论方法。它回答了关于可持续性的科学问题：哪些技术参数和机床特性导致其最低的资源消耗/损耗和零件制造成本。因此，该方法允许将它们视为能量信息模型，在其中进行能量，材料和信息的任何形式和类型的转换。结果表明，创新的空心轴成形技术已成为切削技术的可持续替代方案。智能工厂使用数字化，制造数据管理和自学习方法来提高资源效率。可持续生产需要强大且无差错的加工过程。因此，将提出一种防碰撞系统，该系统可以保护加工中心和工件免受实时碰撞。所收集的有关产品及其属性以及制造数据的信息将构建一个数字孪生模型，并能够预测智能工厂中的资源消耗。可持续生产需要强大且无差错的加工过程。因此，将提出一种防碰撞系统，该系统可以保护加工中心和工件免受实时碰撞。所收集的有关产品及其属性以及制造数据的信息将构建一个数字孪生模型，并能够预测智能工厂中的资源消耗。可持续生产需要强大且无差错的加工过程。因此，将提出一种防碰撞系统，该系统可以保护加工中心和工件免受实时碰撞。所收集的有关产品及其属性以及制造数据的信息将构建一个数字孪生模型，并能够预测智能工厂中的资源消耗。