Abstract
A product development framework called LeanDfX has been conceived at INEGI, aiming at organizing the product design and development process benefitting from lean thinking and DfX paradigms. The design of the metallic structure for an automated guided vehicle (AGV) focusing on its static, dynamic and fatigue characteristics was a recent opportunity to enhance and further develop the framework through the consideration and integration into the process of several existing tools such as FMEA (failure mode and effect analysis), QFD (quality function deployment) or fuzzy logic. This paper describes the integration of those tools in the LeanDfX framework and an application to the design of an AGV structure. The methodology presented involves systematic consideration of a substantial number of design requirements and more detailed product specification characterization. Such a number might be seen as delaying the development process, but the present case study showed that the inverse was true, thanks to the structured systematic approach and timely elimination of less desirable alternatives.

Abstract
This study focuses on the characterization of the production process of a composite sandwich panel for an aircraft structure. Two curing alternatives were compared, namely hot-press and autoclave. A holistic assessment was conducted applying the Total Efficiency Framework, which combines both process efficiency and environmental performance analyses into a single index score to support manufacturing decision. The study provides inventory data, collected at laboratory scale regarding materials, energy consumption, and process operation for composite panel production, which are seldom available. This foreground data was used to quantify the process efficiency, based on lean design tool, and to estimate the potential environmental impacts, using Life Cycle Assessment methodology to determine the eco-efficiency of the production process. The results suggested that the autoclave curing outperforms the hot-press alternative in terms of efficiency, eco-efficiency, and environmental perfor-mance. Regarding the total efficiency index results for maximum productivity, the results show a difference of 12% between the two alternatives, indicating potential competitive advantages in an industrial setting.

Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Abstract
Over the last years, the numerical simulation of integrated processes has become the major challenge in virtual try-out of sheet metal components, including trimming operations that may occur between forming steps. Detailed simulation of trimming processes is a challenging task, particularly when integrated with other forming operations such as deep drawing or hemming. A simplified approach can be adopted in which elements outside the trim surface are deleted from the finite element (FE) model adjusting the remaining to the surface. Following this approach, the state variables are mapped from the old FE mesh to the new trimmed mesh to continue the simulation. This paper addresses this simplified approach to the trimming process exploring a previously presented algorithm (Finite Elem Anal Des 42: 1053-1060, Baptista et al. 2006), which allows the treatment of hexahedral finite element meshes. Particularly, it focuses on the performance evaluation of the implemented strategies for correcting the FE mesh to the trimming surface, including the treatment of pentahedral-shaped elements. Different correction and treatment strategies are evaluated on different types of meshes, based on numerical simulation results of simple mechanical tests: uniaxial tensile test and simple bending test. Finally, two practical applications are given where the local effect of the trimming algorithm is highlighted.

Abstract
Lean and green (L&G) manufacturing in Industry 4.0 (I4.0) has brought many advantages in manufacturing industries by minimizing waste and maximizing efficiency with integration of renewable energy sources and sustainable materials. Multi-layer Stream Mapping (MSM) is a new framework for the performance assessment of complex manufacturing processes. MSM is used for multi-domain analysis of manufacturing processes to assess resources, and processes, that are used to identify Non-ValueAdded (NVA) procedures or steps that consume unnecessary time and resources, and/or release emissions and waste that can no longer be reused or recycled to be eliminated or replaced to create a Value Added (VA) process flow that avoids waste in a clean, green and environmental friendly manner. This paper presents the implementation of the L&G strategy through MSM in metal working production systems. In metalworking production systems, the variables of operational performance and resources consumption considered are process time, number of operators, consumables, raw material, and energy. These can be suitably used for reduction in water emissions, gas emissions, solid waste and scrap generated in metalworking production systems.

Abstract
Solid-phasetemplate-assisted mechanosynthesis of Fe-N-C,featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine(TPTZ), and NaCl is reported. Efficient and sustainable synthesis of performant metal/nitrogen-dopedcarbon (M-N-C) catalysts for oxygen reduction and evolutionreactions (ORR/OER) is vital for the global switch to green energytechnologies-fuel cells and metal-air batteries. Thisstudy reports a solid-phase template-assisted mechanosynthesis ofFe-N-C, featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine (TPTZ), and NaCl. ANaCl-templated Fe-TPTZ metal-organic material was formed usingfacile liquid-assisted grinding/compression. With NaCl, the Fe-TPTZtemplate-induced stability allows for a rapid, thus, energy-efficientpyrolysis. Among the produced materials, 3D-FeNC-LAG exhibits remarkableperformance in ORR (E (1/2) = 0.85 V and E (onset) = 1.00 V), OER (E ( j=10) = 1.73 V), and in the zinc-airbattery test (power density of 139 mW cm(-2)). Themultilayer stream mapping (MSM) framework is presented as a tool forcreating a sustainability assessment protocol for the catalyst productionprocess. MSM employs time, cost, resource, and energy efficiency astechnoeconomic sustainability metrics to assess the potential upstreamimpact. MSM analysis shows that the 3D-FeNC-LAG synthesis exhibits90% overall process efficiency and 97.67% cost efficiency. The proposedsynthetic protocol requires 2 times less processing time and 3 timesless energy without compromising the catalyst efficiency, superiorto the most advanced methods.