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Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often measured in terms of cost, quality, productivity, and material and energy efficiency. With the advent of smart manufacturing technologies-better production equipment, sensing technologies, computational methods, and data analytics applied from the process to enterprise levels-the potential for sustainability performance improvement is tremendous. Sustainable manufacturing seeks the best balance of a variety of performance measures to satisfy and optimize the goals of all stakeholders. Accurate measures of performance are the foundation on which sustainability objectives can be pursued. Historically, operational and information technologies have undergone disparate development, with little convergence across the domains. To focus future research efforts in advanced manufacturing, the authors organized a one-day workshop, sponsored by the U.S. National Science Foundation, at the joint manufacturing research conferences of the American Society of Mechanical Engineers and Society of Manufacturing Engineers. Research needs were identified to help harmonize disparate manufacturing metrics, models, and methods from across conventional manufacturing, nanomanufacturing, and additive/hybrid manufacturing processes and systems. Experts from academia and government labs presented invited lightning talks to discuss their perspectives on current advanced manufacturing research challenges. Workshop participants also provided their perspectives in facilitated brainstorming breakouts and a reflection activity. The aim was to define advanced manufacturing research and educational needs for improving manufacturing process performance through improved sustainability metrics, modeling approaches, and decision support methods. In addition to these workshop outcomes, a review of the recent literature is presented, which identifies research opportunities across several advanced manufacturing domains. Recommendations for future research describe the short-, mid-, and long-term needs of the advanced manufacturing community for enabling smart and sustainable manufacturing.

1. Summary This document presents supporting documentation for a reference implementation of the Unit Manufacturing Process (UMP) information model presented in ASTM E3012, Standard Guide for Characterizing Environmental Aspects of [...]

To understand the past successes and future opportunities for applying Industry 4.0 technologies toward manufacturing sustainability assessment, this state-of-the-art review examines previous literature at the intersection of these domains. Specifically, we focus on the application of Industry 4.0 technologies in the context of the following stages in manufacturing sustainability assessment: (i) planning, infrastructure development, and problem definition, (ii) performance measurement, (iii) results interpretation and decision-making, as well as (iv) intervention, control, and validation strategies. On the basis of the literature review, we present the trends, benefits, research gaps, and required future work for holistically integrating the research domains mentioned above. To accompany this literature review, we performed a meta-analysis of 14,498 articles and compared them to 316 articles compiled from a focused literature search. Based on database-controlled keywords, networks representing keyword co-occurrences were constructed to reveal clusters of related terms and evaluate overall term centrality (i.e., importance). Results from this analysis showed that the two datasets exhibited a similar network structure and also helped reveal Industry 4.0-related opportunities for manufacturing sustainability assessment. They include areas such as automation, robotics, and advanced inspection technologies, which are yet to be exploited in manufacturing sustainability assessment. Further research is needed to investigate whether the incorporation of such areas can (i) facilitate more robust and accessible assessments of manufacturing sustainability and (ii) make manufacturing systems themselves more sustainable. [ABSTRACT FROM AUTHOR]

Product design is one of the most important sectors influencing global sustainability, as almost all the products consumed by people are outputs of the product development process. In particular, early design decisions can have a very significant impact on sustainability. These decisions not only relate to material and manufacturing choices but have a far-reaching effect on the product's entire life cycle, including transportation, distribution, and end-of-life logistics. However, key challenges have to be overcome to enable eco-design methods to be applicable in early design stages. Lack of information models, semantic interoperability, methods to influence eco-design thinking in early stages, measurement science and uncertainty models in eco-decisions, and ability to balance business decisions and eco-design methodology are serious impediments to realizing sustainable products and services. Therefore, integrating downstream life cycle data into eco-design tools is essential to achieving true sustainable product development. Our review gives an overview of related research and positions early eco-design tools and decision support as a key strategy for the future. By merging sustainable thinking into traditional design methods, this review provides a framework for ongoing research, as well as encourages research collaborations among the various communities interested in sustainable product realization. [DOI: 10.1115/1.4002308] Keywords: sustainable design, eco-design, product design, manufacturing, supply chain

The issue of environmental sustainability, which is unprecedented in both magnitude and complexity, presents one of the biggest challenges faced by modern society. Design engineers can make significant contributions by incorporating environmental awareness into product and process development. It is critical that engineers make a paradigm shift in product design from centering on cost and performance to balancing economic, environmental, and societal considerations. Although there have been quite a few designs for environment (or ecodesign) tools developed, so far, these tools have only achieved limited industrial penetration. The present-day methods are either too qualitative to offer concrete solutions and not effective for designers with limited experience or too quantitative, costly, and time consuming. Thus, current ecodesign tools cannot be implemented during the early design phases. This paper develops a novel, semiquantitative ecodesign methodology that is targeted specifically toward the early stages of the design process. The new methodology is a combination of environmental life cycle assessment and visual tools such as quality function deployment, functional-component matrix, and Pugh chart. Since the early design process is function-oriented, a new visual tool called the function impact matrix has been developed to correlate environmental impacts with product function. Redesign of office staplers for reduced carbon footprint has been selected as a case study to demonstrate the use of the proposed approach. Life cycle assessment results confirm that the new stapler design generated using this methodology promotes improved environmental performance. [DOI: 10.1115/1.4001890] Keywords: ecodesign, visual tool, life cycle assessment, function impact matrix

This paper quantifies machining process inventories based on commonly used techniques in various stages of the detailed design process. We investigate variabilities in process inventories between these techniques and their relation to manufacturing process parameters, which are required for generating machine-level instructions. We also benchmark these process inventory modeling techniques against experimental measurements. We then showcase potential issues of using available process models without properly accounting for product and process differences. Our results caution practitioners against basing their environmental analyses on data collected prior to detailed process planning. We conclude that accurate quantification of machining process inventories requires combining accurate simulation-based models with data-driven estimates for model parameters. [ABSTRACT FROM AUTHOR]