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Surface enhanced Raman spectroscopy (SERS) is a surface-sensitive detection technique that dramatically increases the scattering signals of the analytes compared to traditional Raman spectroscopy. Rapid and low-cost fabrication of SERS substrates with easily tunable features remains a challenge, although many SERS substrates with high enhancement factors (EF) were investigated. Here, we report a novel and rapid approach of fabricating SERS substrates using ultrasonic vibration assisted nanomachining. Grids patterns with easily tuned dimensions were fabricated on PMMA surfaces. SERS substrates were fabricated after coating an 80 nm gold layer on the patterned silicon after reactive ion etching (RIE) using the patterned PMMA as the mask. Probing molecules of R6G with the area density of 2.8 × 10-12 M/mm2 can be detected, achieving an EF of 3.11 × 103. Results show a pattern with higher grids density tends to achieve a higher EF.

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.

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 chapter will cover the challenges and opportunities in regenerative medicine manufacturing. We will start with a brief background on the importance of advancing manufacturing in this space to enable technologies to be viable in the commercial marketplace. We will then review highlighted manufacturing challenges and corresponding opportunities in these challenge spaces to provide a context as to where the gaps are and how much needs to be done to mature processes in these spaces. We will then provide a number of perspectives on future manufacturing systems that we believe will be developed in the next decade. We conclude by providing a global landscape of various initiatives and societies that are seeking to advance manufacturing in the regenerative medicine space.

Summary The Regenerative Medicine Foundation Annual Conference held on May 6 and 7, 2014, had a vision of assisting with translating tissue engineering and regenerative medicine (TERM)-based technologies closer to the clinic. This vision was achieved by assembling leaders in the field to cover critical areas. Some of these critical areas included regulatory pathways for regenerative medicine therapies, strategic partnerships, coordination of resources, developing standards for the field, government support, priorities for industry, biobanking, and new technologies. The final day of this conference featured focused sessions on manufacturing, during which expert speakers were invited from industry, government, and academia. The speakers identified and accessed roadblocks plaguing the field where improvements in advanced manufacturing offered many solutions. The manufacturing sessions included (a) product development toward commercialization in regenerative medicine, (b) process challenges to scale up manufacturing in regenerative medicine, and (c) infrastructure needs for manufacturing in regenerative medicine. Subsequent to this, industry was invited to participate in a survey to further elucidate the challenges to translation and scale-up. This perspective article will cover the lessons learned from these manufacturing sessions and early results from the survey. We also outline a road map for developing the manufacturing infrastructure, resources, standards, capabilities, education, training, and workforce development to realize the promise of TERM.

Nanofabrication technology is very important for many emerging engineering and scientific applications. Among different nanofabrication technologies, vibration-assisted nano-machining provides a low cost easy-to-setup approach to produce structures with nano-scale resolution. It is critical to understand the mechanism for the nano-machining process and predict the cutting force, so as to provide guidelines to achieve higher productivity and reduce tip wear. In this article, a machining force model for tip-based nano-machining process is developed and validated. We analyze the instantaneous engagement area between cutting tool (AFM tip) and workpiece (PMMA film) at the given tip position for the vibration-assisted nano-machining process. A discrete voxel method is adopted to calculate the material removal rate at each moment, and an empirical machining force model is developed by correlating the cutting force with material removal rate. The model was verified by experiments over a large range of machining conditions, and the coefficients and parameters in the force model was obtained using Mean Square Error (MSE) method by comparing the predicted machining force from the force model and measured machining force from experiments. The results show good fit between predicted machining force and measured machining force.Copyright © 2015 by ASME

Remote thermocouple sensing (RTS) is a technique that utilizes analytical and experimental evidence to create models for temperature sensing, thus integrating the advantages of both approaches. The remote solution is an inverse problem of predicting the source based on some remote location temperatures. An accurate forward solution should be available that determines the relation between the source and sink, for estimation of the inverse relationship. Extensive simulation has to be performed on the forward solution, thus establishing a need for fast and accurate computation. A mathematical formulation for RTS is done using an elliptic partial differential equation. Boundary conditions are formulated to simulate the actual machining situation. Some of these are derived by placing an insulator between the tool and tool holder. For inverse solution, three thermocouples are used for enhancing the accuracy of source prediction. Issues such as steady-state machining, and location of thermocouples are discussed. Overall, a basic framework for RTS in machining is presented.

This paper describes a method for layout design of a Cellular Manufacturing System (CMS) that would allow simultaneously, the grouping of machines unique to a part family into cells and those shared by several cells to be located together in functional sections. Using an illustrative example, this integration of the flexibility of a functional layout, the reduced handling gained from cell formation and allowance of limited intercell flows among adjacent cells is described. Thereby, the traditional strategy of simultaneous formation of part families and distribution of machines into independent cells which creates machine distribution and unbalanced utilization problems is avoided. This is justified by an analysis of the complex interactions between the critical subproblems in cell formation—machine grouping, part family formation, distribution and utilization of shared machines, intracell layout, intercell (or shop) layout and material handling. This approach represents a new direction in cell formation where, by allowing the handling function to limit the extent of machine duplication between adjacent cells, a new graph theoretic structure for simultaneous machine grouping and layout design was developed and validated.

This paper describes a relational database system for semi-generative process planning for sheet metal parts that emulates expert system capabilities. The system integrates a feature-based relational database for the parts, a forward chaining rule-based strategy for machine selection, both global and feature-specific execution of the rules and a graph theoretic cost optimization model for optimal process plan selection. This system, which is currently being developed for a sheet metal fabrication company, suggests that, using the experience of shopfloor personnel, an efficient integration of feature-based process planning and expert system strategies can be accomplished.