Review of digital twin applications in manufacturing

Additive manufacturing has the potential to make a longstanding impact on the manufacturing world and is a core element of the Fourth Industrial Revolution. Additive manufacturing signifies a new disruptive path on how we will produce parts and products. Several studies suggest this technology could foster sustainability into manufacturing systems based on its potential of optimizing material consumption, creating new shapes, customizing designs and shortening production times that, all combined, will greatly transform some of the existing business models. Although it requires reaching a certain level of design maturity to completely insert this technology in an industrial setting, additive manufacturing has the potential to favorably impact the manufacturing sector by reducing costs in production, logistics, inventories, and in the development and industrialization of a new product. The transformation of the industry and the acceleration of the adopting rate of new technologies is driving organizational strategy. Thus, through the lenses of Industry 4.0 and its technological concepts, this paper aims to contribute to the knowledge about the impacts of additive manufacturing technology on sustainable business models. This aim is accomplished through a proposed framework, as well as the models and scales that can be used to determine these impacts. The effects are assessed by taking into account the social, environmental and economic impacts of additive manufacturing on business models and for all these three dimensions a balanced scorecard structure is proposed.

https://www.mdpi.com/2071-1050/12/17/7066/pdf

Impact Assessment of Additive Manufacturing on Sustainable Business Models in Industry 4.0 Context

How to integrate additive manufacturing technologies into manufacturing systems successfully: A perspective from the commercial vehicle industry

Role of Additive Manufacturing in Medical Application COVID-19 Scenario: INDIA Case study.

A hybrid deep learning model of process-build interactions in additive manufacturing

CAD-based design and pre-processing tools for additive manufacturing

Additive Manufacturing is becoming a strategic initiative to enable the transformation of today’s business into a digital enterprise. This technology is increasing the rate of innovation with new lightweight and sustainable designs and dramatically reducing the time from product ideation (design) to realization (manufacturing). Makerspaces are being built all over the world, especially in academic settings and students have 3D printers readily available to design and produce innovative ideas on the spot. The University of Cincinnati (UC) is partnering with Siemens PLM Software to create students who are ready for the digital workforce of the future considering the entire product lifecycle. UC is developing curriculum to teach the fundamentals of Additive Manufacturing using Siemens PLM software as a foundation using a cradle to cradle approach. Students will realize the idea of a ‘digital thread’ that connects a product design to its production environment using fast product/process simulations using a cradle to cradle approach. They will also become aware of simulation tools required for assessing and iterating the performance attributes of the process and the product between the ideation and realization phases to create a ‘digital twin’ of that product. This will help prepare students for Industry 4.0.

Additive Manufacturing Simulation Tools in Education

Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

INTRODUCTION: Financial restrictions limit attainment of hermetically-precise, patient-specific cranial implants (PSCIs) after decompressive hemicraniectomy (DHC) in developing countries. Several authors have reported use of image-segmentation, modeling software, and 3D printers to develop low-cost alternatives for PSCIs. However, technical expertise and time requirements have prevented widespread utilization of this technique. METHODS: A freeware algorithm titled CranialRebuild was constructed with the following capabilities: after upload of DICOM files, the normal side is analyzed in reference to the side of DHC, a Boolean subtraction algorithm is used to obtain a virtual image of the desired implant, a two-piece virtual (.STL) model of a negative of the implant is then generated. Following construction, CranialRebuild was evaluated in four cadaveric specimens. In each specimen, a standard DHC was performed. Post-DHC CT imaging were used to obtain a virtual model of the negative of the implant, which was then printed using low-cost polylactic acid (PLA). Methylmethacrylate cement was used to generate a PSCI from the PLA mold. The PSCIs were re-implanted into cadaveric specimens. Pictures were obtained, and cosmesis was subjectively evaluated using a 5-point Likert scale. RESULTS: Two of the four heads received a grade of 4/5, indicating optimal cosmesis could be obtained following minimal post-processing modification. Two of the heads were graded as 3/5, indicating optimal cosmesis could be obtained following moderate post-processing modification. CONCLUSIONS: The CranialRebuild freeware algorithm can be used to create low-cost, hermetically-precise PSCIs at a fraction of the price of third-party vendors. This technology is expected to greatly improve accessibility of customized cranial implants worldwide.

Omkar Ghalsasi

Papers

CAD-based design and pre-processing tools for additive manufacturing

Additive Manufacturing Simulation Tools in Education

Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

170 Towards Global Availability of Patient-Specific Cranial Implants: Creation and Validation of CranialRebuild Freeware Application

Toward global availability of low-cost, patient-specific cranial implants: creation and validation of automated CranialRebuild freeware application.