## 1 Introduction
[PERSON] et al. [14] analyzed the issues faced by legacy factories (brownfield) as they transition into I4.0 ecosystems, and proposed a general system architecture that can help bridge the gaps. This architecture was designed around characteristics such as flexibility, scalability, security, and real-time communication. [PERSON] et al. [15] presented a system architecture to interconnect equipment at the shop floor with the Cloud. Their main concern was interoperability of the system, and work focused on communication protocols and open-source software for implementation. This work argues that intelligence is a characteristic that can benefit from I4.0. Computer Numerical Control (CNC) allows for the automatic operation of many machine functions. However, autonomous operation, which allows the machine to adapt to changes in the environment, is a goal that lies beyond the reach of CNC. Monitoring and modeling of the process have long been recognized as capacities that are needed for autonomous control and operation of machine tools (see [PERSON] [16] and [PERSON] [17]). More recently, [PERSON] et al. [18] pointed out that Intelligent process monitoring and data from measurements of finished products are two features that are needed to achieve autonomy. [PERSON] et al. [19] discussed the use of machine tools for in-line metrology to obtain part data right after manufacture. However, machine tools are meant to produce parts. Measuring is not a function that adds value, and other options exist. [PERSON] et al. ([20; 21]) identified trendsin metrology for manufacturing: Metrology at smaller scales (micro and nano), holistic measurement in which multiple sensors are used to measure parts fully, and quick low-cost measurement techniques for high volumes in the production line, or in-line measurement. More specifically, [PERSON] [22] proposed that in-line inspection stations are vital for part quality monitoring in modern Reconfigurable Manufacturing Systems (RMS). From the previous discussion, communication between machines, improved intelligence, and data from the process and the product are necessary ingredients to achieve the goal of Smart Manufacturing. New machine architectures must incorporate features to achieve this goal. While world class, well-established machine tool builders routinely include state of the art concepts in their designs, they tend to maintain control of the information that is available to their customers. Furthermore, there is a growing trend in which regional manufacturing subsidiaries of global companies as well Tier 1 suppliers invest in the development of advanced manufacturing technologies (AMT), facilitating the digitization of existing factories [23]. Local developers of AMT compete on the basis of cost and development time for application-specific machines. This work proposes that Connectivity, defined as the process of transferring data from an edge device (sensor or control) to another device or the Cloud, is the machine design feature that enables the implementation of I4.0, affecting the intelligence of the machine and how hardware capabilities are utilized. Connectivity allows intelligence to be decentralized and reconfigured to facilitate analysis and decision-making. Furthermore, Connectivity is the means to facilitate the construction of the digital thread of a product [24]. Through a specific case, this research shows that speed and safety of data transfer, as well as synchronization with the decision-making process, are factors that need to be accounted for during design stage. Proper design results in significant improvements over previous generations of machines, specifically, for the purposes of process monitoring and forecasting of the quality of the part and the health of the machine. This article describes how Connectivity can be incorporated into the design of machine tools and applies these ideas for the design of an in-line measuring machine.