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The number of ‘things’ ranging from simple devices to complex machines on the factory floor connected at the enterprise level and to the broader internet is growing exponentially. This connection also leads to a tremendous amount of data generated leading to ‘Data’ now considered one of the core assets in the broader manufacturing industry. However, the availability of this asset is hardly made use of by Small and Medium scale manufacturing enterprises (SME) - the ‘Mittelstand’ of America. How can certain types of data be shared by SME companies, yet have the ability to retain ownership and control over their own data? How does SME leverage computing on these diverse forms of data for the benefit of its clients and itself? In this paper, we propose a decentralized data distribution architecture to democratize the potential availability of large amounts of data generated by the manufacturing industry using the Fog Computing paradigm. The architecture leverages an Industry scalable middleware extension of Cloud manufacturing that securely filters and transmits data from IoT enabled manufacturing machines on the shop floor to potential users over the cloud. This work also demonstrates a data-centric approach which allows peer-to-peer data sharing laterally within the fog layer to serve cloud users. We demonstrate the feasibility of the Fog middleware infrastructure through case studies that involves various types of manufacturing data.

Nanoimprint lithography is a fabrication method by applying nanomolds on resists to form inversed patterns. It can be utilized to fabricate high-resolution nanopatterns in a low cost and rapid fashion on both flat and curved surfaces. The fidelity of fabricated master nanopatterns and elastomer nanomolds are essential to the quality of the nanoimprinted features. Although two-dimensional nanoimprint lithography has been widely investigated, there are limited researches about three-dimensional master nanopatterns and nanomolds. Even less of them discussed the fidelity of entire procedures throughout the initial master fabrication to the final nanoimprinting. In this paper, we demonstrated the effectiveness of our approach, atomic force microscope (AFM) based ultrasonic vibration assisted nanomachining, in fabricating three-dimensional master nanopatterns with complex contours and continuously height changes. Nanomolds for nanoimprint lithography were fabricated subsequently based on the master nanopatterns with a positive draft, which is naturally generated from the AFM tip. We investigated the fidelity of the nanomachining results and the residual effect of reusing mater patterns. Besides, we demonstrated the 3D nanoimprinting capability of the solvent-assisted microcontact molding process, discovering the possibility of tuning the layer-thickness of imprinted 3D nanopatterns.

The affordability and increased capability of additive manufacturing machines has spawned prototyping service bureaus throughout the world. This poses a challenge to designers who are looking to obtain quality 3D printed parts at best available prices within fastest turnaround times. Customers relying on a sole source for 3D printed parts may have limited options in obtaining the best deals. From a service supplier point of view, filling excess capacity will require significant marketing budgets to reach and retain customers. In this paper, we present a novel mechanism design approach for improving the accessibility of prototyping services providers by leveraging their excess capacity. In our proposed mechanism, consumers name their own price and the mechanism will find service bureaus who are willing to make the part under the stated price. The mechanism runs similar to a reverse auction where consumers bid and the platform finds a service supplier which is able to match the stated bid price. The incentive for suppliers to participate in such a platform is the opportunity to market their excess capacity to a deal conscious consumer at a lower price without cannibalizing their existing sales channels. Qualified suppliers do not directly compete with each other for any given order since they are chosen using a two stage selection process by the service platform. This algorithm ensures that every supplier has a fair chance of selling its services on the platform regardless of price. We implement the proposed mechanism design approach in a simulated service marketplace and empirically evaluate the marketplace behavior by studying the impact of various model factors such as the supplier threshold price, the size and variety of suppliers in the marketplace.

This paper develops and characterizes a three-dimensional (3-D) nanofabrication process using ultrasonic vibration assisted nanomachining based on an atomic force microscope (AFM). The superiorities of height control over force control in the process are explained and are demonstrated by the fabrication results. Three factors impacting actual feature depths are investigated, including the ultrasonic z -vibrational amplitude, the assigned base feature depth, and the machining speed. 3-D nanostructures with continuous height variations were successfully fabricated on polymethyl methacrylate (PMMA) films with the feature height manipulated through controlling the absolute height of the cantilever tip in AFM. By selecting machining parameters based on characterizations, feature dimensions can be controlled as desired values within small variances. The capability of transferring 3-D nanostructures from PMMA films to silicon substrates is further explored in this paper. After selecting recipes of the reactive ion etching process, 3-D nanostructures are successfully transferred to silicon substrates with controllable selectivity. The reported ultrasonic vibration assisted nanomachining process in height control provides a robust approach of fabricating 3-D nanostructures.

Future factories will feature strong integration of physical machines and cyber-enabled software, working seamlessly to improve manufacturing production efficiency. In these digitally enabled and network connected factories, each physical machine on the shop floor can have its ‘virtual twin’ available in cyberspace. This ‘virtual twin’ is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace. This results in the virtualization of a machine resource to external factory manufacturing systems. This paper describes how streaming data can be stored in a scalable and flexible document schema based database such as MongoDB, a data store that makes up the virtual twin system. We present an architecture, which allows third-party integration of software apps to interface with the virtual manufacturing machines. We evaluate our database schema against query statements and provide examples of how third-party apps can interface with manufacturing machines using the VMM middleware. Finally, we discuss an operating system architecture for VMMs across the manufacturing cyberspace, which necessitates command and control of various virtualized manufacturing machines, opening new possibilities in cyber-physical systems in manufacturing.

Automated Tool condition monitoring is critical in intelligent manufacturing to improve both productivity and sustainability of manufacturing operations. Estimation of tool wear in real-time for critical machining operations can improve part quality and reduce scrap rates. This paper proposes a probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process. By applying PL, the method helps to avoid developing a complex closed form formulation for a specific tool wear model. It increases the robustness of the algorithm and reduces the time complexity of computation. The application of the PL approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead tool wear state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of the cutting tool inserts.

Nanofabrication technology is very important for many emerging engineering and scientific applications. Among different nanofabrication technologies, vibration-assisted nanomachining provides a low-cost easy-to-setup approach for producing structures with nano-scale resolution. It is very important to understand the mechanism for this nanomachining process and predict the involved machining force, so as to provide guidelines to achieve higher productivity and reduce tip wear. In this work, a machining force model for the tip-based nanomachining process was developed and validated. We analyzed the instantaneous engagement between the cutting tool (AFM tip) and the workpiece (PMMA film) during each tip rotation cycle for the vibration-assisted nanomachining process. A discrete voxel method was adopted to calculate the material removal rate at each moment, and an empirical machining force model is developed by correlating the machining force with material removal rate, which is a function of the input parameters of the nanomachining process. The machining force model was verified by experiments over a large range of machining conditions, and the coefficients in the force model were obtained by minimizing the Mean Square Error (MSE) method by comparing the predicted machining force from the model and measured machining force from the experiments. The results show a good fit between the predicted machining force and the measured machining force.

The digital factory of the future will be driven by the integration of physical smart machine tools and cyber-enabled software, working seamlessly to increase manufacturing intelligence, flexibility, agility and production efficiency. The objective of this study is develop and demonstrate a middleware software architecture to interface physical machines on a shop floor with client manufacturing applications. We have connected both legacy and modern ‘smart’ machines to a highly scalable database capable of storing streaming time-series data generated by on-board sensors and machine controllers. Three client applications were developed to demonstrate the mechanism through which third-party apps can be written without direct physical communications with machines on the shop-floor. The first, is an application that resides within the Digital Manufacturing Commons (DMC) which demonstrates the ability to query data from any physical machine on the floor; the 2 nd application demonstrates a python app which compares digital product data with machine generated data; and the 3 rd application demonstrates building a LabView app built to interface with the middleware service. This proposed architecture enables an ecosystem of smart manufacturing applications to be built and deployed on the shop-floor through open-sourced software and hardware devices thereby reducing cost of manufacturing software development.

This paper presents sensor data integration and information fusion to build “digital-twins” virtual machine tools for cyber-physical manufacturing. Virtual machine tools are useful for simulating machine tools’ capabilities in a safe and cost-effective way, but it is challenging to accurately emulate the behavior of the physical tools. When a physical machine tool breaks down or malfunctions, engineers can always go back to check the digital traces of the “digital-twins” virtual machine for diagnosis and prognosis. This paper presents an integration of manufacturing data and sensory data into developing “digital-twins” virtual machine tools to improve their accountability and capabilities for cyber-physical manufacturing. The sensory data are used to extract the machining characteristics profiles of a digital-twins machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. In this paper, techniques are discussed for deploying sensors to capture machine-specific features, and analytical techniques of data and information fusion are presented for modeling and developing “digital-twins” virtual machine tools. Example of developing the digital-twins of a 3-axis vertical milling machine is presented to demonstrate the concept of modeling and building a digital-twins virtual machine tool for cyber-physical manufacturing. The presented technique can be used as a building block for cyber-physic manufacturing development.

This paper presents a novel AFM-based 3D nanofabrication process using ultrasonic vibration assisted nanomachining. A set of three dimensional nanostructures on polymethyl methacrylate (PMMA) samples are fabricated with the assistance of high frequency in-plane circular xy-vibration and ultrasonic tip-sample z-vibration. Two methods for fabricating 3D nanostructures were investigated in this study, which are layer-by-layer nanomachining and one pass nanomachining with the depth controlled by setpoint force. Critical parameters in the process are identified, including setpoint force, overlap percentage, amplitude of z vibration and machining speed. By regulating these process parameters, multi-level 3D nanostructures were fabricated by multi-layer machining in vector mode and raster scan mode. Using different setpoint forces for regulating feature depths, other nanostructures, such as convex and concave circles, were fabricated in raster scan mode from gray-scale bitmap pattern images. Under each mode, 3D nanostructure over microscale area can be fabricated in just a few minutes with sub-10 nm resolution in z direction.