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Review Article

J. Manuf. Sci. Eng. 2017;140(3):030801-030801-19. doi:10.1115/1.4037763.

Product cost estimation (PCE) still draws the attention of researchers and practitioners, even though it has been extensively discussed in the literature for more than 20 years. This is due to its central impact on the company's performance. Nowadays, the adoption of cost estimation methods seems to be limited, despite the multitude of examples and applications available. A possible reason is the multitude of approaches and techniques proposed in the literature, which, instead of representing a guide for enabling possible implementations, actually create confusion and ambiguity on their appropriateness for a particular application. Hence, this paper aims to provide a systematic review of the recent literature in the field of PCE, and intensively investigates the aspects that can enable a more conscious decision on the type of technique to be adopted. This results in the identification of five different perspectives, which can be taken simultaneously into account. By combining the different viewpoints, a new multilayer framework is derived, with a specific focus on the whole product life cycle. The proposed framework can be used as a decision-making tool by both researchers and practitioners. In fact, the former group can benefit from the new structure, as a way to identify new areas of possible research opportunities. The latter group is provided an operative guide for the application in industrial contexts.

Commentary by Dr. Valentin Fuster

Research Papers

J. Manuf. Sci. Eng. 2017;140(3):031001-031001-9. doi:10.1115/1.4038509.

While additive manufacturing allows more complex shapes than conventional manufacturing processes, there is a clear benefit in leveraging both new and old processes in the definition of metal parts. For example, one could create complex part shapes where the main “body” is defined by extrusion and machining, while small protruding features are defined by additive manufacturing. This paper looks at how optimization and geometric reasoning can be combined to identify cutting planes within complex three-dimensional (3D) shapes. These cutting planes are used to divide realistic mechanical parts into subparts that can be joined together through additive manufacturing or linear friction welding (LFW). The optimization method presents possible manufacturing alternatives to an engineering designer where optimality is defined as a minimization of cost. The paper presents and compares several cutting planes identification methods and describes how the optimization finds the optimal results for several example parts.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031002-031002-9. doi:10.1115/1.4038369.

The stamping of fiber metal laminates (FMLs) at thermoforming temperature of the thermoplastic matrix is investigated. The studied FML types consist of a unidirectional carbon fiber-reinforced core that is attached to metal cover layers either made of a steel or magnesium alloy. An analytical model is established in order to predict the process forces during forming, which are the blankholder force required to make the metal covers yield plastically, the punch force, and the corresponding load distribution on the individual layers (outer layer, core layer, and inner layer). The global forces are primarily verified through experimental force measurements, while numerical simulations are mainly performed to assess the resulting load distribution with the help of strain distributions in the cover layers. The results show that the introduced model can be applied successfully if the stamp-forming process is dominated by friction-induced tensional loading rather than by bending.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031003-031003-10. doi:10.1115/1.4038184.

The problem of fault diagnosis for dimensional integrity in multistation assembly systems is addressed in this paper. Fault diagnosis under this context is to identify the process errors which significantly contribute to the large product dimensional variation based on sensor data. The main challenges to be resolved in this paper include (1) the number of measurements is less than the process errors, which is typical in practice, but results in an ill-posed estimation problem, and (2) there exists spatial correlation among the dimensional variation of process errors, which has not been addressed yet by existing literature. A spatially correlated Bayesian learning (SCBL) algorithm to address these challenges is developed. The SCBL algorithm is based on the relevance vector machine (RVM) by exploiting the spatial correlation of dimensional variation from various process errors, which occurs in some circumstances of assembled parts and is well defined in GD&T standards. The proposed algorithm relies on a parametrized prior including the spatial correlation, and eventually leads sparsity in fault diagnosis; hence, the issues with ill-posedness and structured process errors will be addressed. A number of simulation studies are performed to illustrate the superiority of SCBL algorithm over state-of-the-art algorithms in sparse estimation problems when spatial correlation exists among the nonzero elements. A real autobody assembly process is also used to demonstrate the effectiveness of proposed SCBL algorithm.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031004-031004-10. doi:10.1115/1.4038511.

Double-sided incremental forming (DSIF) is a dieless sheet metal forming process that uses two generic tools to form a part of arbitrary geometry from a clamped sheet via the accumulation of small localized deformations. In DSIF, there is a need for an automatic toolpath generation method to separate geometric features coupled with a strategy to form these features in the correct sequence such that they can be accurately formed. Traditional CNC machining toolpaths are not suitable for DSIF because these toolpaths are designed for material removal processes, which do not have to account for the motion of the virgin material during the process. This paper presents a novel and simple way to represent geometric features in a hierarchical tree structure during z-height-based slicing along with algorithms to generate different forming strategies using this tree structure. The proposed approach is demonstrated through physical experiments by forming a complex part with multiple features.

Topics: Algorithms , Geometry
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031005-031005-14. doi:10.1115/1.4038513.

Tailor welded blank (TWB) has many advantages over a traditional blank for manufacturing automobile sheet metal components, such as significant flexibility in product design, higher structural stiffness, and crash behavior. However, lower formability and weld line movement are some of the problems associated with forming of TWBs. Hydroforming is a potential technique to enhance formability. In this work, the effect of thickness ratio on maximum dome height and weld line movement in hydraulic bulging of laser welded interstitial-free (IF) steel blanks of different thickness combinations has been predicted using finite element (FE) simulations. The results are also validated with hydraulic bulging experiments on TWBs. It has been found that with increase in thickness ratio, the maximum bulge height decreased and weld line displacement toward thicker side increased. These results have been used to relocate the weld line toward the thinner side in the initial blanks and achieve a more uniform bulge profile of the dome. The peak pressure to achieve maximum safe dome height and percentage thinning has also been found out. The results showed huge improvement in uniformity of bulge profile with little reduction in dome height.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031006-031006-9. doi:10.1115/1.4038207.

In the context of computer numeric control (CNC)-based sheet metal laser cutting, the problem of heat transfer simulation is relevant for the optimization of CNC programs. Current physically based simulation tools use numeric or analytic algorithms which provide accurate but slow solutions due to the underlying mathematical description of the model. This paper presents: (1) an analytic solution to the laser heating problem of rectangular sheet metal for curved laser trajectories and convective cooling, (2) a graphics processing unit (GPU) implementation of the analytic solution for fast simulation of the problem, and (3) an integration within an interactive environment for the simulation of sheet metal CNC laser cutting. This analytic approach sacrifices the material removal effect of the laser cut in the favor of an approximated real-time temperature map on the sheet metal. The articulation of thermal, geometric, and graphic feedback in virtual manufacturing environments enables interactive redefinition of the CNC programs for better product quality, lower safety risks, material waste, and energy usage among others. The error with respect to finite element analysis (FEA) in temperature prediction descends as low as 3.5%.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031007-031007-9. doi:10.1115/1.4038512.

Among the sheet hydroforming processes, hydrodynamic deep drawing (HDD) process has been used to form complex shapes and can produce parts with high drawing ratio. Studies showed that radial pressure created on the edge of the sheet can decrease the drawing force and increase drawing ratio. Thus, increasing of radial pressure to an amount greater than chamber pressure, and independent control of these pressures, is the basic idea in this study. In this research, the effect of radial and chamber pressures on formability of St13 and pure copper sheets in the process of hydrodynamic deep drawing assisted by radial pressure (HDDRP) with inward flowing liquid is investigated. Giving that a significant portion of the maximum thinning of the formed part occurs in the beginning of the process, the pressure supply system used in the experimental tests was designed in a way, which provides simultaneous control of the radial and chamber pressures throughout the process. Thickness distribution, forming force, and tensile stresses are the parameters that were evaluated in this study. Results indicated that using a higher radial pressure than the chamber pressure and controlling their values in the initial stages of the process enhances the thickness distribution of the formed part in all regions. A comparison between the thickness distribution and maximum forming force of the formed parts by the HDDRP and HDDRP with inward flowing liquid methods showed that by applying the later method, parts with more uniform thickness distribution and less maximum thinning and forming force can be achieved.

Topics: Pressure , Simulation , Copper
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031008-031008-12. doi:10.1115/1.4038442.

Severe plastic deformation (SPD) processing such as equal channel angular extrusion (ECAE) has been pioneered to produce ultrafine grained (UFG) metals for improved mechanical and physical properties. However, understanding the machining of SPD-processed metals is still limited. This study aims to investigate the differences in chip morphology when machining ECAE-processed UFG and coarse-grained (CG) titanium (Ti) and understand the chip formation mechanism using metallographic analysis, digital imaging correlation (DIC), and nano-indentation. The chip morphology is classified as aperiodic saw-tooth, continuous, or periodic saw-tooth, and changes with the cutting speed. The chip formation mechanism of the ECAE-processed Ti transitions from cyclic shear localization within the low cutting speed regime (such as 0.1 m/s or higher) to uniform shear localization within the moderately high cutting speed regime (such as from 0.5 to 1.0 m/s) and to cyclic shear localization (1.0 m/s). The shear band spacing increases with the cutting speed and is always lower than that of the CG counterpart. If the shear strain rate distribution contains a shift in the chip flow direction, the chip morphology appears saw-tooth, and cyclic shear localization is the chip formation mechanism. If no such shift occurs, the chip formation is considered continuous, and uniform shear localization is the chip formation mechanism. Hardness measurements show that cyclic shear localization is the chip formation mechanism when localized hardness peaks occur, whereas uniform shear localization is operative when the hardness is relatively constant.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031009-031009-15. doi:10.1115/1.4038293.

Significant advancements in the field of additive manufacturing (AM) have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small fusible openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes lead to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel artificial neural network (ANN)-based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part's geometric accuracy.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031010-031010-10. doi:10.1115/1.4038597.

In recent years, many titanium alloys have emerged, each of them associated with a range of different heat treatments. Thus, several microstructures have been studied to varying degrees. For example, the Ti64 titanium alloy, mostly known for its α + β structure, can display a different state: the structure, inducing nonstandard mechanical behavior. This work presents chip formation in this specific microstructure where a strong heterogeneity is observed and where the shear band formation is a function of the relationship between the shear direction and the microstructure orientation. From these reasons, major differences are found in the chip morphology, within the same cutting condition, in comparison to the bimodal structure where a single chip morphology is obtained for each cutting condition. A section of this paper is devoted to the presentation of the β microstructure where different configurations can be seen within the same chip. Next, the influence of cutting conditions on the chip formation is studied. To highlight the specific chip formation process, a temperature model has been developed and combined with cutting force analysis to understand clearly the specificity of the chip formation for this structure. Finally, the discussion explains the different chip formation scenarios according to the workpiece microstructure to be cut.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;140(3):031011-031011-12. doi:10.1115/1.4038598.

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(3):031012-031012-9. doi:10.1115/1.4038571.

The relative velocity between workpiece and media has a strong effect on the material removal rate in vibratory finishing. Due to this fact, a measurement system in the form of a camera-integrated workpiece is presented in this paper, which is capable of measuring the relative velocity between the workpiece and the media in an unguided vibratory finishing process. The unique feature of this measurement system is the completely wireless construction, so that the results are not influenced by wires for the data transfers and the electrical power supply of the light-emitting diodes of the camera system. Furthermore, the influence of the media size and adjustments of the imbalance engine like rotational speed, mass distribution between the upper and the lower imbalance weights, and offset angle between the imbalance weights were investigated. The evaluation of the results has shown that the media size and the rotational speed of the imbalance engine are the major influence factors on the relative velocity between workpiece and media.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(3):031013-031013-10. doi:10.1115/1.4038294.

Recent changes in legislation along with environmental initiatives to drive sustainability and reduce carbon emissions have sprouted the development of energy models to characterize manufacturing processes. In the case of injection molding, much work has been performed in coupling sensors with control statistical systems to promptly identify process' instabilities, such as pressure drops or fluctuations in the filling point. Latest energy models for injection molding make use of injection pressure and temperature parameters that are a function of the machine, mold geometry, and process characteristics. The latest state-of-the-art way to measure energy consumption is through the use of energy loggers, which provide power data at the end of the production cycles. Although seemingly correlated, little has been published on the extrapolation of cavity signals for their use in energy calculations. In this study, the advantages and disadvantages of using cavity sensors in injection molding are explored; a novel approach to the use of cavity sensors' pressure and temperature data is proposed by exploring their input in an energy model for the estimation of specific energy consumption (SEC). The model was validated against power data obtained via an energy logger; the averaged energy reported by the model indicated a range of 60–67% accuracy.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(3):031014-031014-13. doi:10.1115/1.4037891.

Metal-based powder-bed-fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conventional subtractive manufacturing processes, and is flexible to produce parts with complex geometries. Quality and repeatability of AM parts remain a challenging issue that persistently hampers wide applications of AM technology. Rapid advancements in sensing technology, especially imaging sensing systems, provide an opportunity to overcome such challenges. However, little has been done to fully utilize the image profiles acquired in the AM process and study the fractal patterns for the purpose of process monitoring, quality assessment, and control. This paper presents a new multifractal methodology for the characterization and detection of defects in PBF-AM parts. Both simulation and real-world case studies show that the proposed approach effectively detects and characterizes various defect patterns in AM images and has strong potential for quality control of AM processes.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(3):031015-031015-13. doi:10.1115/1.4038515.

Designing products for recyclability is driven by environmental and economic goals. Several design for assembly (DFA) rules and parameters can be used to gauge the recyclability index of product designs. These indices can be used for comparative analysis of the recyclability of different products. This assists the designer in making design choices related to the product's end of life. However, many of the existing recyclability indices are only available after design and manufacturing decisions are made. If such design decisions could be made earlier in the design process, when the design space is less bound, recyclability could be considered earlier. A case study is performed to determine if DFA parameters could be utilized to determine product recyclability. The parameters were obtained from existing DFA time estimate tables. The results of the study indicated that the recyclability of the product, as defined by established recyclability metrics, could be predicted through DFA measures. A negative correlation was realized between recyclability and insertion time. Components that required greater time to mate during assembly adversely affected the recyclability of the product. Conversely, handing time was found to have no predictive capability on product recyclability. These findings are used to develop a recyclability index that utilizes the DFA measures, allowing designers and engineers to determine recyclability earlier in the design process.

Topics: Manufacturing , Design
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(3):031016-031016-10. doi:10.1115/1.4038576.

Friction stir blind riveting (FSBR) process offers the ability to create highly efficient joints for lightweight metal alloys. During the process, a distinctive gradient microstructure can be generated for the work material near the rivet hole surface due to high-gradient plastic deformation and friction. In this work, discontinuous dynamic recrystallization (dDRX) is found to be the major recrystallization mechanism of aluminum alloy 6111 undergoing FSBR. A cellular automaton (CA) model is developed for the first time to simulate the evolution of microstructure of workpiece material during the dynamic FSBR process by incorporating main microstructure evolution mechanisms, including dislocation dynamics during severe plastic deformation, dynamic recovery, dDRX, and subsequent grain growth. Complex thermomechanical loading conditions during FSBR are obtained using a mesh-free Lagrangian particle-based smooth particle hydrodynamics (SPH) method, and are applied in the CA model to predict the microstructure evolution near the rivet hole. The simulation results in grain structure agree well with the experiments, which indicates that the important characteristics of microstructure evolution during the FSBR process are well captured by the CA model. This study presents a novel numerical approach to model and simulate microstructure evolution undergoing severe plastic deformation processes.

Commentary by Dr. Valentin Fuster

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