The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (T_m) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, T_m, and C, in relation to cutting speed, v_c, depth of cut, a_p, and feed per revolution, f_r. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v_c, a_p, and f_r. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, T_m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v_c = 250 m/min, cutting depth a_p = 1.0 mm, and feed per revolution f_r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm³ (T_m). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and T_m was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum T_m with a slight increase (under 5%) in milling costs.

Friction stir welding is a solid-state welding process, which is successfully applied to aluminum alloys to replace fusion welding processes. In the present work, the influence of varying the feed rate at constant rotational speed on the mechanical and microstructural properties of friction stir welded AA5754 was investigated. FSW caused dynamic recrystallization leading to microstructural changes in different zones. Electron backscattered diffraction confirmed such changes via misorientation angle and subgrain size distributions. Energy dispersive spectroscopy determined the elements present in the matrix quantitatively and their distribution through mapping. Tensile and hardness tests showed slight enhancement compared to base metal. The strain hardening exponent was used to investigate the effect of varying the feed rate on the behavior of the welded samples.