0


Guest Editorial

J. Manuf. Sci. Eng. 2016;138(10):100301-100301-2. doi:10.1115/1.4034550.
FREE TO VIEW
Commentary by Dr. Valentin Fuster

Review Article

J. Manuf. Sci. Eng. 2016;138(10):100801-100801-16. doi:10.1115/1.4034277.

The studies about sustainable manufacturing engineering (SME) contain an increasing body of knowledge, motivated by the rising interest in the processes lifecycle sustainability. Its continuous improvement and optimization (including sustainability criteria) has become an emerging necessity. For this reason, new clean technologies and proposals of work methods are required; they have to integrate the ecological and social dimensions at an operational level in the manufacturing processes, maintaining the economic and technical feasibility attained up to this moment. However, a unified framework does not exist to orientate the lines of research in optimization when applied to sustainability. In this sense, the article reviews studies from scientific literature about sustainable machining developed in the last 15 years. The review has been carried out from the triple bottom-line (TBL) perspective, defined by the three general sustainability dimensions (economy, ecology, and equity). It contributes to the literature and current machining engineering knowledge, with its involvement in mitigating the metabolic rift. The results from the review have allowed to characterize the investigation effort, with regard to the optimization of the sustainable machining processes; even though numerous studies exist which optimize machining operations (with the aim to find the trade-off between different environmental and equity factors), in general, the technical and economic feasibilities are still the priority. The patterns defined through the analysis of the publications have established the current development trend; furthermore, as a consequence of the review results, we propose an outline of articulated lines of investigation with the aim to mitigate the metabolic rift through triple bottom-line, necessary so that machining engineering assumes the goal of finding the balance to achieve integral sustainability.

Commentary by Dr. Valentin Fuster

Research Papers

J. Manuf. Sci. Eng. 2016;138(10):101001-101001-12. doi:10.1115/1.4033661.

As environmental performance becomes increasingly important, the sintering process is receiving more attention since it consumes large amounts of energy. This paper proposes a data-driven model for sintering energy consumption, which considers both model accuracy and time efficiency. The proposed model begins with removing data anomalies using a local outlier factor (LOF) algorithm and an attribute selection module using the RReliefF method. Then, to accurately predict sintering energy consumption, an integrated predictive model is employed that uses bagging-enhanced extreme learning machine (ELM) and support vector regression (SVR) machine, combined with an entropy weight method. A case study is used to demonstrate the effectiveness of the proposed model using actual production data for a year. Results show that the proposed model outperforms other models and is computationally efficient. Optimal parameters of the LOF (1.3) and number of attributes (30) were identified. It was found that coke powder has the most significant impact on the solid energy consumption (SEC), while cooling water flow rate provides the most significant impact on the gas energy consumption (GEC) within each recorded attribute variation. Parametric analysis further revealed the relationships between energy consumption and the significant attributes mentioned above. It is suggested that the proposed model could effectively reduce the energy consumption by attaining more efficient attribute settings.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101002-101002-12. doi:10.1115/1.4033689.
OPEN ACCESS

A key element for achieving sustainable manufacturing systems is efficient and effective resource use. This potentially can be achieved by encouraging symbiotic thinking among multiple manufacturers and industrial actors and establish resource flow structures that are analogous to material flows in natural ecosystems. In this paper, ecological principles used by ecologists for understanding food web (FW) structures are discussed which can provide new insight for improving closed-loop manufacturing networks. Quantitative ecological metrics for measuring the performance of natural ecosystems are employed. Specifically, cyclicity, which is used by ecologists to measure the presence and strength of the internal cycling of materials and energy in a system, is discussed. To test applicability, groupings of symbiotic eco-industrial parks (EIP) were made in terms of the level of internal cycling in the network structure (high, medium, basic, and none) based on the metric cyclicity. None of the industrial systems analyzed matched the average values and amounts of cycling seen in biological ecosystems. Having detritus actors, i.e., active recyclers, is a key element for achieving more complex cycling behavior. Higher cyclicity values also correspond to higher amounts of indirect cycling and pathway proliferation rate, i.e., the rate that the number of paths increases as path length increases. In FWs, when significant cycling is present, indirect flows dominate direct flows. The application of these principles has the potential for novel insights in the context of closed-loop manufacturing systems and sustainable manufacturing.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101003-101003-8. doi:10.1115/1.4034438.

With the emergence of the concept of industrial ecology (IE) and the first discovery of its practice in an existent park in Kalundborg, the interest from the scientific community as well as from the public and private stakeholders has increased significantly. For more than a decade, a handful of national programs and private initiatives have been initiated worldwide to implement industrial ecology into existent or newly built industrial parks. To date, more than hundreds of eco-industrial parks (EIPs) have been established. However, the relationship between the context and the origin of EIP initiatives with its methodology of development and management is still not clearly defined. Therefore, the aim of this article is to contribute to filling this knowledge gap. The return of experiences of 19 EIPs worldwide, based on bibliographical and empirical research through literature review and field interviews, allows the definition of a trend in the creation and the management of EIPs according to the context of implementation. This investigation exposes the exclusive relationships between trigger factors to develop an EIP either economic, environmental, or a mix according to the bottom-up, top-down, or mixed approach of creation, respectively. Moreover, it highlights the association dependence between the natures of the approach with the coordination structure and consequently the influence of the social context and the presence of a certain gap of cohabitation of the two extreme systems, i.e., public and private.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101004-101004-10. doi:10.1115/1.4033756.

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.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101005-101005-11. doi:10.1115/1.4034302.

Manufacturing plants energy consumption accounts for a large share in world energy usage. Energy consumption modeling and analyses are widely studied to understand how and where the energy is used inside of the plants. However, a systematic energy modeling approach is seldom studied to describe the holistic energy in the plants. Especially using layers of models to share information and guide the next step modeling is rarely studied. In this paper, a manufacturing system temporal and organizational framework was used to guide the systematic energy modeling approach. Various levels of models were established and tested in an automotive manufacturing plant to illustrate how the approach can be implemented. A detail paint spray booth air unit was described to demonstrate how to investigate the most sensitive variables in affecting energy consumption. While considering the current plant metering status, the proposed approach is advanced in information sharing and improvement suggestion determination.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101006-101006-10. doi:10.1115/1.4034303.

This paper offers an extension of axiomatic design theory to ensure that leaders, managers, and engineers can sustain manufacturing systems throughout the product lifecycle. The paper has three objectives: to provide a methodology for designing and implementing manufacturing systems to be sustainable in the context of the enterprise, to define the use of performance metrics and investment criteria that sustain manufacturing, and to provide a systems engineering approach that enables continuous improvement (CI) and adaptability to change. The systems engineering methodology developed in this paper seeks to replace the use of the word “lean” to describe the result of manufacturing system design. Current research indicates that within three years of launch, ninety percent of “lean implementations” fail. This paper provides a methodology that leaders, managers, and engineers may use to sustain their manufacturing system design and implementation.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101007-101007-11. doi:10.1115/1.4034159.

The profitability of electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in the quantity, quality, and timing of returns originating from consumers' behavior. The cloud-based remanufacturing concept, data collection, and information tracking technologies seem promising solutions toward the proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an agent based simulation (ABS) framework to model the overall product take-back and recovery system based on the product identity data available through cloud-based remanufacturing infrastructure. Sociodemographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process, and product life cycle information have all been considered to capture the optimum buy-back price (bbp) proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101008-101008-7. doi:10.1115/1.4033922.

Sustainability assessments are dependent on accurate measures for energy, material, and other resources used by the processes involved in the life cycle of a product. Manufacturing accounts for about 1/5 of the energy consumption in the U.S. Minimizing energy and material consumption in this field has the promise of dramatically reducing our energy dependence. To this end, ASTM International [1] has formed both a committee on Sustainability (E60) and a Subcommittee on Sustainable Manufacturing (E60.13). This paper describes ASTM’s new guide for characterizing the environmental aspects of manufacturing processes [2]. The guide defines a generic representation to support structured processes. Representations of multiple unit manufacturing processes (UMPs) can be linked together to support system-level analyses, such as simulation and evaluation of a series of manufacturing processes used in the manufacture and assembly of parts. The result is the ability to more accurately assess and improve the sustainability of production processes. Simulation is commonly used in manufacturing industries to assess individual process performance at a system level and to understand behaviors and interactions between processes. This paper explores the use of the concepts outlined in the standard with three use cases based on an industrial example in the pulp and paper industry. The intent of the use cases is to show the utility of the standard as a guideline for composing data to characterize manufacturing processes. The data, besides being useful for descriptive purposes, is used in a simulation model to assess sustainability of a manufacturing system.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101009-101009-18. doi:10.1115/1.4034481.

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.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101010-101010-9. doi:10.1115/1.4034475.

This paper studies the adverse environmental impacts of atomic layer deposition (ALD) nanotechnology on manufacturing of Al2O3 nanoscale thin films. Numerical simulations with detailed ALD surface reaction mechanism developed based on density functional theory (DFT) and atomic-level calculations are performed to investigate the effects of four process parameters including process temperature, pulse time, purge time, and carrier gas flow rate on ALD film deposition rate, process emissions, and wastes. Full-cycle ALD simulations reveal that the depositions of nano thin films in ALD are in essence the chemisorption of the gaseous species and the conversion of surface species. Methane emissions are positively proportional to the film deposition process. The studies show that process temperature fundamentally affects the ALD chemical process by changing the energy states of the surface species. Pulse time is directly related to the precursor dosage. Purge time influences the ALD process by changing the gas–surface interaction time, and a higher carrier gas flow rate can alter the ALD flow field by accelerating the convective heat and mass transfer in ALD process.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101011-101011-12. doi:10.1115/1.4033995.

This paper studies the effect of various lamellar-type solid lubricants (graphite and hBN) that can be mixed into a lubricant to potentially improve the machinability of minimum quantity lubrication (MQL) machining. To examine this, the solid lubricants are classified into particles and platelets based on their aspect ratios as well as their respective sizes. In particular, the particles are classified into microparticles and nanoparticles based on their dimensions (average radius), while the platelets were classified, based on their average thickness, into two types: the “microplatelets” if the thickness is typically up to few tens of microns and the “nanoplatelets” if the thickness is well below a tenth of a micron (even down to few nanometers). Our previous work has shown that the mixture of an extremely small amount (about 0.1 wt. %) of the graphitic nanoplatelets and vegetable oil immensely enhanced the machinability of MQL machining. In this paper, many lubricants, each mixed with a particular variety of nano- or micro-platelets or one type of nanoparticles, were studied to reveal the effect of each solid lubricant on MQL machining. Prior to the MQL machining experiment, the tribological test was conducted to show that the nanoplatelets are overall more effective than the microplatelets and nanoparticles in minimizing wear despite of no significant difference in friction compared to pure vegetable oil. Consequently, the MQL ball-milling experiment was conducted with AISI 1045 steel yielding a similar trend. Surprisingly, the oil mixtures with the microplatelets increased flank wear, even compared to the pure oil lubricant when the tools with the smooth surface were used. Thus, the nanoscale thickness of these platelets is a critical requirement for the solid lubricants in enhancing the MQL machining process. However, maintaining the nanoscale thickness is not critical with the tools with the rough surfaces in enhancing the MQL process. Therefore, it is concluded that finding an optimum solid lubricant depends on not only the characteristics (material as well as morphology) of solid lubricants but also the characteristic of tool surface.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2016;138(10):101012-101012-8. doi:10.1115/1.4033603.

Strict environmental regulations and increasing public awareness toward environmental issues force many companies to establish dedicated facilities for product recovery. All product recovery options require some level of disassembly. That is why, the cost-effective management of product recovery operations highly depends on the effective planning of disassembly operations. There are two crucial issues common to most disassembly systems. The first issue is disassembly sequencing which involves the determination of an optimal or near optimal disassembly sequence. The second issue is disassembly-to-order (DTO) problem which involves the determination of the number of end of life (EOL) products to process to fulfill the demand for specified numbers of components and materials. Although disassembly sequencing decisions directly affects the various costs associated with a disassembly-to-order problem, these two issues are treated separately in the literature. In this study, a genetic algorithm (GA) based simulation optimization approach was proposed for the simultaneous determination of disassembly sequence and disassembly-to-order decisions. The applicability of the proposed approach was illustrated by providing a numerical example and the best values of GA parameters were identified by carrying out a Taguchi experimental design.

Commentary by Dr. Valentin Fuster

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In