Jeremy L. Rickli

A Review of Engineering Research in Sustainable Manufacturing

Karl R. Haapala 1 School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331 e-mail: Karl.Haapala@oregonstate.edu Fu Zhao School of Mechanical Engineering, Division of Environmental and Ecological Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907 e-mail: fzhao@purdue.edu Jaime Camelio Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 235 Durham Hall, Blacksburg, VA 24061 e-mail: jcamelio@vt.edu John W. Sutherland Division of Environmental and Ecological Engineering, Purdue University, 322 Potter Engineering Center, West Lafayette, IN 47907 e-mail: jwsuther@purdue.edu Steven J. Skerlos Department of Mechanical Engineering, University of Michigan, 2250 GG Brown Building, Ann Arbor, MI 48105 e-mail: skerlos@umich.edu David A. Dornfeld Department of Mechanical Engineering, University of California, 6143 Etcheverry Hall, Berkeley, CA 94720 e-mail: dornfeld@berkeley.edu I. S. Jawahir Department of Mechanical Engineering, University of Kentucky, 414C UK Center for Manufacturing, Lexington, KY 40506 e-mail: jawahir@engr.uky.edu A Review of Engineering Research in Sustainable Manufacturing Sustainable manufacturing requires simultaneous consideration of economic, environmen- tal, and social implications associated with the production and delivery of goods. Funda- mentally, sustainable manufacturing relies on descriptive metrics, advanced decision- making, and public policy for implementation, evaluation, and feedback. In this paper, recent research into concepts, methods, and tools for sustainable manufacturing is explored. At the manufacturing process level, engineering research has addressed issues related to planning, development, analysis, and improvement of processes. At a manufac- turing systems level, engineering research has addressed challenges relating to facility operation, production planning and scheduling, and supply chain design. Though economi- cally vital, manufacturing processes and systems have retained the negative image of being inefficient, polluting, and dangerous. Industrial and academic researchers are re- imagining manufacturing as a source of innovation to meet society’s future needs by under- taking strategic activities focused on sustainable processes and systems. Despite recent developments in decision making and process- and systems-level research, many chal- lenges and opportunities remain. Several of these challenges relevant to manufacturing process and system research, development, implementation, and education are highlighted. [DOI: 10.1115/1.4024040] Andres F. Clarens Department of Civil and Environmental Engineering, University of Virginia, D220 Thornton Hall, Charlottesville, VA 22904 e-mail: aclarens@virginia.edu Jeremy L. Rickli Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 217 Durham Hall, Blacksburg, VA 24061 e-mail: jlrickli@vt.edu Corresponding author. Contributed by the Manufacturing Engineering Division of ASME for publication in the J OURNAL OF M ANUFACTURING S CIENCE AND E NGINEERING . Manuscript received July 11, 2012; final manuscript received March 4, 2013; published online July 17, 2013. Editor: Y. Lawrence Yao. Manufacturing and Sustainability The concept of sustainability emerged from a series of meetings and reports in the 1970s and 1980s, and was largely motivated by environmental incidents and disasters as well as fears about Journal of Manufacturing Science and Engineering C 2013 by ASME Copyright V AUGUST 2013, Vol. 135 / 041013-1 Downloaded From: http://manufacturingscience.asmedigitalcollection.asme.org/ on 07/09/2014 Terms of Use: http://asme.org/terms

Partial disassembly sequencing considering acquired end-of-life product age distributions

Uncertainty management is a priority in remanufacturing operations due to uncertain end-of-life (EoL) product quality, quantity and return timing. Ignoring EoL product uncertainty can result in inefficient remanufacturing operations. In this work, an approach is developed that addresses the impact of EoL product quality uncertainty on partial disassembly sequences. Disassembly is performed on nearly all EoL products, yet it is vulnerable to uncertain EoL product quality, defined in this work as the remaining value of an EoL product compared to original equipment manufacturer standards. The developed approach converges to an optimal or near-optimal partial disassembly sequence provided that information regarding acquired EoL product age distributions is known and correlates to EoL product quality. A mathematical framework is introduced to evaluate disassembly sequences based on profit standard deviation and profit probability as well as the traditionally used expected profit. The approach is tested on an example case study to investigate the impact of uncertain quality on the optimal or near-optimal disassembly sequence, expected profit, profit standard deviation and profit probability.

A descriptive framework for additive remanufacturing systems

Additive manufacturing, which is envisioned to transform manufacturing by offering extraordinary levels of customisation and agility, has also been shown to be transformative to remanufacturing. High value end–of–life cores are able to be restored to original equipment manufacturer specifications for a fraction of the price of new production using advanced condition assessment and material deposition technologies. However, this capability is only realised if advanced technologies work in harmony to assess, reprocess, and inspect end–of–life cores. In this paper, we describe a framework for remanufacturing systems that aims to take advantage of additive manufacturing processes to remanufacture end–of–life cores. The presented framework is divided into three critical stages; condition assessment and digitisation, material deposition, and reprocessing and final inspection. Technology advancements and current challenges are discussed for each stage and a case study is presented to further illustrate the three stages of the additive remanufacturing system framework.

Fault detection and prognosis of assembly locating systems using piezoelectric transducers

Fixture faults have been identified as a principal root cause of defective products in assembly lines; however, there exists a lack of fast and accurate monitoring tools to detect fixture fault damage. Locating fixture damage causes a decrease in product quality and production throughput due to the extensive work required to detect and diagnosis a faulty fixture. In this paper, a unique algorithm is proposed for fixture fault monitoring based on the use of autoregressive models and previously developed piezoelectric impedance fixture sensors. The monitoring method allows for the detection of changes within a system without the need for healthy references. The new method also has the capability to quantify deterioration with respect to a calibrated value. Deterioration prognosis can then be facilitated for structural integrity predictions and maintenance purposes based on the quantified deterioration and forecasting algorithms. The proposed robust methodology is proven to be effective on an experimental setup for monitoring damage in locating fixtures. Fixture wear and failure are successfully detected by the methodology, and fixture structural integrity prognosis is initiated.

Quality assessment of used-products under uncertain age and usage conditions

Quality of a used-product is often highly uncertain, impacts pricing decisions, and is influenced by many factors like age and usage. However, analysis of usage and its uncertain nature is not well understood. In this paper, joint effects of age and usage on quality of used/end-of-life products are analysed. To conduct this research, Product State Transformation Diagram is proposed and used to analyse quality of used-products under uncertain age and usage conditions. Subsequently, governing multivariate stochastic partial differential equation is derived and paired with operational conditions to develop Quality Degradation Model (QDM). Then, QDM is numerically analysed and compared with common age-based model. Results indicate that age-based models overestimate quality. Using one-factor-at-time approach, sensitivity of QDM to its parameters and inputs is analysed. In a real-life case study, QDM is applied to evaluate prices of used-cars. To validate QDM and to generate insights towards its accuracy, a pool of popular statistical and machine learning models are trained and compared with QDM. Results show that performance of QDM is comparable to known models like SVM, LR, ANN-MLP for this application.

Partial disassembly sequence optimization of end-of-life products for value recovery

End-of-life product recovery operations require performance improvement to be viable in an industrial environment. A genetic algorithm (GA) is proposed to optimize end-of-life partial disassembly decisions based on disassembly costs, revenues, and environmental impacts. Facilitating disassembly optimization with costs, revenues, and environmental impacts is necessary to enhance sustainable manufacturing through value recovery. End-of-life products may not warrant disassembly past a unique disassembly stage due to limited recovered component market demand and minimal material recovery value. Remanufacturing is introduced into disassembly sequence optimization in the proposed GA as an alternative to recycling, reuse, and disposal. The proposed GA’s performance is first verified through optimizing partial disassembly sequences considering costs and environmental impacts independently. Extension to a multiobjective case concerning costs, revenues, and impacts is achieved by specifying a new set of multi-objective crossover probabilities from independent crossover probabilities.

Monitoring and Diagnosis of Assembly Fixture Faults Using Modified Multivariate Control Charts and Surface Scanning Content

Recent advances in process monitoring technology have introduced an influx of exceptionally large data sets containing information on manufacturing process health. Recorded data sets are comprised of numerous parameters for which multivariate statistical process control (MSPC) methodologies are required. Current multivariate control charts are ideal for monitoring data sets with a minimal amount of parameters, however, new monitoring devices such as surface scanning cameras increase the number of parameters by two orders of magnitude in some cases. This paper proposes a modified form of the original multivariate Hotelling T2 chart possessing the capability to monitor manufacturing processes containing a large number of parameters and a fault diagnosis procedure incorporating least squares analysis in conjunction with univariate control charts. A case study considering surface scanning of compliant sheet metal components and comparisons to processes utilizing Optical CMM’s is presented as verification of the proposed assembly fixture fault diagnosis methodology and modified Hotelling T2 multivariate control chart.Copyright