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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.

Knowledge graph networks powering web search and chatbot agents have shown immense popularity. This paper discusses the first steps towards building a knowledge graph for manufacturing services discoverability. Due to the lack of a unified widely adopted schema for structured data in the manufacturing services domain as well as the limitations of existing relational database schemas to represent manufacturing service definitions, there does not exist a unified schema that connects manufacturing resources service descriptions and actual manufacturing service business entities. This gap severely limits the automated discoverability of manufacturing service business organizations. This paper designs a knowledge graph covering over 8,000+ manufacturers, the manufacturing services they provide and corresponding linkage with manufacturing service definitions available from Wikidata. In addition, this work also proposes extensions to Schema.org to assist small business manufacturers to contain embedded search engine optimization (SEO) tags for search and discovery through web search engines. Such vocabulary extensions are critical to the rapid identification and real-time capability assessment particularly when the service providers themselves are responsible for updating tags. A wider scale enhancement of manufacturing specific vocabulary extensions to schema.org can tremendously benefit small and medium scale manufacturers. This paper concludes with the additional work that must be done for a comprehensive addition to manufacturing service graph that spans the entire manufacturing knowledge base.

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.

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.

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.

Uncertainty in manufacturing networks has created barriers to closing the gap between design enterprises and the American industrial base. Uncertainty arises from the lack of transparent access to manufacturer capabilities, the inability to auto-discover service providers who are best capable for a given job request, and the dependence on human word-of-mouth trust network relationships that exist in the manufacturing supply chain. This uncertainty slows down the pace of product development lifecycles from a viewpoint of inefficient forms of supplier assessment, vetting, selection, and compliance, leading to a trust tax tacked onto the final price of products. In times of global crisis such as the coronavirus disease pandemic, this uncertainty also leads to inefficient forms of gathering information on manufacturing capability, available capacity, and registered licenses and assessing compliance. This technical note outlines solution pathways that can help ease the search and discovery process of connecting clients and manufacturing service providers through digitally enabled technologies.

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.

This paper summarizes preliminary rat studies aimed at identifying the effectiveness of using electrically stimulated silver as a bactericidal agent for indwelling residual hardware devices (RHD). A variety of bactericidal indwelling devices were designed, fabricated and surgically inserted into the medullary cavity of live rats. The rats were inoculated with Staphylococcus aureus to try and induce osteomyelitis. A total of 37 surgeries were performed by implanting the rats with both control and potentially bactericidal devices. As surgical procedures and devices were improved, it appeared that the implants produced antibiotic effects in the animals. All of the control animals and all of the animals where the device failed tested positive for Staphylococcus aureus growth. Of the rats with operational bactericidal devices (those that survived the surgery and incubation period), half tested negative for Staphylococcus aureus. The device designs are discussed in this paper along with the test procedures, operating practices and results. A statistical analysis of the results, which shows a very high confidence level in the effectiveness of electrically stimulated silver as a bactericidal agent/antibiotic, is also presented.

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.

Existing approaches to cell formation concentrate on machine grouping and part family formation. Since their input data usually do not consider flow directions and volumes, they neglect layout and handling strategies that can simplify the machine sharing problem. Hence, these approaches fail to relate the intracell and intercell layout decisions to the machine grouping and sharing decisions. This paper introduces a new approach for cell formation which integrates machine grouping and layout design, neglecting part family formation. The concepts of a hybrid cellular layout and virtual manufacturing cells are related. It is shown that a combination of overlapping GT cells, functional layout and handling reduces the need for machine duplication among cells. This approach questions the traditional emphasis on machine duplication to create independent cells that is suggested by the standard machine-part matrix clustering methods. The steps in the method are demonstrated by using two illustrative examples obtai...

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.

Machining introduces a large amount of plastic deformation in the workpiece material and chip. This plastic strain is nonuniform, and therefore residual stresses are induced in the workpiece surface and subsurface throughout, and slightly below, the depth of plastic deformation. Thus, residual stresses are often an undesirable but unavoidable byproduct of machining. It is known that the mechanism of plastic deformation in metal is dislocation movement.

The focused ion beam (FIB) technique is a versatile tool for nanoscale manipulation, deposition and etching. However, degradation mechanisms which lead to residual stresses in materials exposed to high-energy ion beams are not well understood. In this study, we examine the evolution of residual stresses in 100 nm thick freestanding aluminum films subjected to typical ion beam exposures within a commercial FIB tool. Experimental results show that the magnitude of the residual stresses increase with cumulative ion beam exposure and that upper limits are attainable. Further investigation demonstrates that a decrease in ion beam current at constant acceleration-voltage augments the upper limits, which manifests itself in greater residual stresses. The stress gradients in thin films develop from surface modifications in the form of amorphous top layers, which are modeled as bilayer approximations. Experimental observations and analysis indicate that ion beam exposure effects on the mechanical properties of nanoscale thin films and nanostructures cannot be ignored.