According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Cultivating Communities of Practice: A Guide to Managing Knowledge

https://re.public.polimi.it/bitstream/11311/1087435/2/Urbinati%2c%20et%20al.%2c%202017.pdf

Towards a new taxonomy of circular economy business models

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

BACKGROUNDMetallic components are the cornerstone of modern industries such as aviation, aerospace, automobile manufacturing, and energy production. The stringent requirements for high-performance metallic components impede the optimization of materials selection and manufacturing. Laser-based additive manufacturing (AM) is a key strategic technology for technological innovation and industrial sustainability. As the number of applications increases, so do the scientific and technological challenges. Because laser AM has domain-by-domain (e.g., point-by-point, line-by-line, and layer-by-layer) localized forming characteristics, the requisite for printing process and performance control encompasses more than six orders of magnitude, from the microstructure (nanometer- to micrometer-scale) to macroscale structure and performance of components (millimeter- to meter-scale). The traditional route of laser-metal AM follows a typical “series mode” from design to build, resulting in a cumbersome trial-and-error methodology that creates challenges for obtaining high-performance goals.ADVANCESWe propose a holistic concept of material-structure-performance integrated additive manufacturing (MSPI-AM) to cope with the extensive challenges of AM. We define MSPI-AM as a one-step AM production of an integral metallic component by integrating multimaterial layout and innovative structures, with an aim to proactively achieve the designed high performance and multifunctionality. Driven by the performance or function to be realized, the MSPI-AM methodology enables the design of multiple materials, new structures, and corresponding printing processes in parallel and emphasizes their mutual compatibility, providing a systematic solution to the existing challenges for laser-metal AM. MSPI-AM is defined by two methodological ideas: “the right materials printed in the right positions” and “unique structures printed for unique functions.” The increasingly creative methods for engineering both micro- and macrostructures within single printed components have led to the use of AM to produce more complicated structures with multimaterials. It is now feasible to design and print multimaterial components with spatially varying microstructures and properties (e.g., nanocomposites, in situ composites, and gradient materials), further enabling the integration of functional structures with electronics within the volume of a laser-printed monolithic part. These complicated structures (e.g., integral topology optimization structures, biomimetic structures learned from nature, and multiscale hierarchical lattice or cellular structures) have led to breakthroughs in both mechanical performance and physical/chemical functionality. Proactive realization of high performance and multifunctionality requires cross-scale coordination mechanisms (i.e., from the nano/microscale to the macroscale).OUTLOOKOur MSPI-AM continues to develop into a practical methodology that contributes to the high performance and multifunctionality goals of AM. Many opportunities exist to enhance MSPI-AM. MSPI-AM relies on a more digitized material and structure development and printing, which could be accomplished by considering different paradigms for AM materials discovery with the Materials Genome Initiative, standardization of formats for digitizing materials and structures to accelerate data aggregation, and a systematic printability database to enhance autonomous decision-making of printers. MSPI-oriented AM becomes more intelligent in processes and production, with the integration of intelligent detection, sensing and monitoring, big-data statistics and analytics, machine learning, and digital twins. MSPI-AM further calls for more hybrid approaches to yield the final high-performance/multifunctional achievements, with more versatile materials selection and more comprehensive integration of virtual manufacturing and real production to navigate more complex printing. We hope that MSPI-AM can become a key strategy for the sustainable development of AM technologies. Download high-res image Open in new tab Download PowerpointMaterial-structure-performance integrated additive manufacturing (MSPI-AM).Versatile designed materials and innovative structures are simultaneously printed within an integral metallic component to yield high performance and multifunctionality, integrating in parallel the core elements of material, structure, process, and performance and a large number of related coupling elements and future potential elements to enhance the multifunctionality of printed components and the maturity and sustainability of laser AM technologies.

Material-structure-performance integrated laser-metal additive manufacturing.

Emerging trends in manufacturing such as light weighting, increased performance and functionality increases the use of multi-material, hybrid structures and thus the need for joining of dissimilar materials. The properties of the different materials are jointly utilised to achieve product performance. The joining processes can, on the other hand be challenging due to the same different properties. This paper reviews and summarizes state of the art research in joining dissimilar materials. Current and emerging joining technologies are reviewed according to the mechanisms of joint formation, i.e.; mechanical, chemical, thermal, or hybrid processes. Methods for process selection are described and future challenges for research on joining dissimilar materials are summarized.

/pdf/joining-of-dissimilar-materials-5d5ghi3lwg.pdf

Joining of dissimilar materials

Remanufacturing is one of the critical elements moving our economy toward one that is circular. Today, remanufacturing is attracting growing attention worldwide. Meanwhile, despite its potential in terms of its effects on both environment and economy, remanufacturing has not yet been sufficiently exploited. This indicates that there exist both drivers for and barriers to an increase in remanufacturing in economies. Although there are both technological and non-technological requirements for remanufacturing, R&D is unavoidable for its promotion. This article outlines trends, drivers, and barriers for remanufacturing, and presents reviews of studies on selected topics in remanufacturing. The selected R&D topics in this article are product design for remanufacturing, additive manufacturing for remanufacturing, operations management in remanufacturing, and business models for remanufacturing.

Trends and research challenges in remanufacturing

Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

Kristian Martinsen

Papers

Joining of dissimilar materials

Trends and research challenges in remanufacturing

Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

Integration of digital learning in industry 4.0

Monitoring and Control for Thermoplastics Injection Molding A Review