Society's consumption of natural resources and the impact of industrial activities on the environment have gained increasing attention over the last several decades. This paper provides a historical perspective on the origins of the environmental movement and its connection to industrial systems. Then, recent research related to product design, process improvement and change, green manufacturing planning, and the circular economy are described. With respect to product design, topics such as material selection and component light-weighting are considered. For process-related research, efforts such as operation changes and equipment design for reduced energy consumption are discussed. For manufacturing planning, new developments in process planning and production scheduling are highlighted that consider environmental performance. The concept of circular economy is examined critically, with particular emphasis placed on closing materials loops via recycling and remanufacturing. The paper concludes with a discussion of challenges and opportunities to achieve the goal of industrial sustainability.

Promoting excellence in sustainable manufacturing has emerged as a strategic mission in academia and industry. In particular, universities must prepare the next generation of engineers to contribute to the task of sustaining and improving manufacturing by providing appropriate types of sustainability education and training. However, engineering curricula are challenged in delivering educational training for assessing technical solutions from the three domains that define sustainability: economic, environmental, and social. In the research presented here, an educational framework is developed with an aim to improve student understanding of sustainable product design (PD) and manufacturing. The framework is founded on the analyze, design, develop, implement, and evaluate (ADDIE) model for instructional design. The developed framework is demonstrated using an example of a sustainable PD activity. This instructional design case study illustrates how engineering students would be able to investigate the impacts of raw materials, unit manufacturing processes, manufacturing locations, and design changes on product sustainability performance by integrating PD information and manufacturing analysis methods during the PD phase.

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Interest in assessing the sustainability performance of manufacturing processes and systems during product design is increasing. Prior work has investigated approaches for quantifying and reducing impacts across the product life cycle. Energy consumption and carbon footprint are frequently adopted and investigated environmental performance metrics. However, challenges persist in concurrent consideration of environmental, economic, and social impacts resulting from manufacturing processes and supply chain networks. Companies are striving to manage their manufacturing networks to improve environmental and social performance, in addition to economic performance. In particular, social responsibility has gained visibility as a conduit to competitive advantage. Thus, a framework is presented for improving environmental and social performance through simultaneous consideration of manufacturing processes and supply chain activities. The framework builds upon the unit manufacturing process modeling method and is demonstrated for production of bicycle pedal components. For the case examined, it is found that unit manufacturing processes account for 63–97% of supply chain carbon footprint when air freight transport is not used. When air freight transport is used for heavier components, transportation-related energy consumption accounts for 78–90% of supply chain carbon footprint. Similarly, from a social responsibility perspective, transportation-related activities account for 73–99% of supply chain injuries/illnesses, and days away from work when air freight transport is used. Manufacturing activities dominate the impacts on worker health when air freight transport is not used, leading to 59–99% of supply chain injuries/illnesses, and days away from work. These results reiterate that simultaneous consideration of environmental and social impacts of manufacturing and supply chain activities is needed to inform decision making in sustainable product manufacturing.

The emergence of additive manufacturing (AM) has potential for dramatic changes in labor productivity and economic welfare. With the growth of AM, understanding of the sustainability performance of relevant technologies is required. Toward that goal, an environmental impact assessment (EIA) approach is undertaken to evaluate an AM process. A novel fast mask image projection stereolithography (MIP-SL) process is investigated for the production of six functional test parts. The materials, energy, and wastes are documented for parts fabricated using this process. The EIA is completed for human health, ecosystem diversity, and resource costs using the ReCiPe 2008 impact assessment method. It is noted that process energy, in the form of electricity, is the key contributor for all three damage types. The results are used to depict the underlying relationship between energy consumed and the environmental impact of the process. Thus, to facilitate prediction of process energy utilization, a mathematical model relating shape complexity and dimensional size of the part with respect to part build time and washing time is developed. The effectiveness of this model is validated using data from real-time process energy monitoring. This work quantifies the elemental influence of design features on AM process energy consumption and environmental impacts. While focused on the environmental performance of the fast MIP-SL process, the developed approach can be extended to evaluate other AM processes and can encompass a triple bottom line analysis approach for sustainable design by predicting environmental, economic, and social performance of products.