The composite sheet layup process involves stacking several layers of a viscoelastic prepreg sheet and curing the laminate to manufacture the component. Demands for automating functional tasks in the composite manufacturing processes have dramatically increased in the past decade. A simulation system representing a digital twin of the composite sheet can aid in the development of such an autonomous system for prepreg sheet layup. While finite element analysis (FEA) is a popular approach for simulating flexible materials, material properties need to be encoded to produce high-fidelity mechanical simulations. We present a methodology to predict material parameters of a thin-shell FEA model based on real-world observations of the deformations of the object. We utilize the model to develop a digital twin of a composite sheet. The method is tested on viscoelastic composite prepreg sheets and fabric materials such as cotton cloth, felt, and canvas. We discuss the implementation and development of a high-speed FEA simulator based on the VegaFEM library. By using our method to identify sheet material parameters, the sheet simulation system is able to predict sheet behavior within 5 cm of average error and have proven its capability for 10 fps real-time sheet simulation.

Recently, the availability of various materials and ongoing research in developing advanced systems for multi-material additive manufacturing (MMAM) have opened doors for innovation in functional products. One major concern of MMAM is the strength at the interface between materials. This article hypothesizes overlapping and interlacing materials to enhance the bonding strength. To test this hypothesis, we need a computer-aided manufacturing (CAM) tool that can process the overlapped material regions. However, existing computational tools lack key multi-material design processing features and have certain limitations in making full use of the material information, which restricts the testing of our hypothesis. Therefore, this research also develops a new MMAM slicing framework that efficiently identifies the boundaries for materials to develop different advanced features. By modifying a ray tracing technology, we develop layered-depth material images (LDMI) to process the material information from computer-aided design (CAD) models for slicing and process planning. Each sample point in the LDMI has associated material and geometric properties that are used to identify the multi-material regions. Based on the material information in each slice, interlocking joint (T-Joint) and interlacing infill are generated in the regions with multiple materials. Tensile tests have been performed to verify the enhancement of mechanical properties by the use of overlapping and interlacing materials.

Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large data set of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely, random failure and physical damage. Frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the failure count, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.

Trochoidal (TR) milling is a popular means for slotting operation. Attributing to its unique circular-shaped path pattern, TR milling avoids the full tool–workpiece engagement, which helps reduce the cutting heat accumulation and hence slow down the tool wear. While traditionally TR milling is only used for machining 2.5D cavities, it has now been extended to machining genuine 3D curved cavities under the realm of five-axis machining. However, since for a typical five-axis machine tool the rotary axes have a much larger moment of inertia than the three linear axes, to reduce both the total machining time and the consumed electric energy (for driving the machine tool), it is desirable to minimize the use of the two rotary axes (particularly the one with the larger moment of inertia) when planning a TR tool path for a given 3D cavity. Nevertheless, due to the newness of five-axis TR machining, there has no published reports on this subject. In this paper, we present a five-axis TR tool path planning algorithm for machining an arbitrary 3D curved cavity, which will consider the kinematical characteristics of the five-axis machine tool and try to minimize the use of the rotary axis with the largest moment of inertia, while tending to all the required constraints such as the threshold on the cutting force. Both computer simulation and physical cutting experiments of the proposed method have been conducted, and the results give a preliminary confirmation on the feasibility and advantages of the proposed method.

Mirror error compensation is usually used to improve the machining precision of thin-walled parts. However, due to the time-varying cutting condition of thin-walled parts, this zero-order method may result in inadequate error compensation. To cope with this problem, an online first-order error compensation method is proposed for thin-walled parts. With this context, first, the time-varying cutting condition of thin-walled parts is defined with its in-process geometric and physical characteristics. Based on it, a first-order machining error compensation model is constructed. Then, before process starting, the theory geometric and physical characteristics of thin-walled parts are, respectively, obtained with CAM software and structure dynamic modification theory. After process performing, the real geometric characteristic of thin-walled parts is measured, and it is used to calculate the dimension error of thin-walled parts. Next, the error compensated value is evaluated to construct an error compensation surface, which is used to modify the tool center points of next process step. Finally, the machining error is compensated by performing the next process step. Two typical experiments, milling of thin-walled parts with plane- and curved-surface, are used to validate the proposed method, and the experiment results shown that this method can significantly improve the error compensation effect for low-stiffness structure. Compared with the mirror compensation, the final thickness error of thin-walled parts is reduced by 71.4% and 56.2%, respectively, for plane- and curved-surface parts.

Temperature history prediction is essential for a better understanding of the relationship between microstructural change and processing conditions for energy beam additive manufacturing fabricated components. Here, a new efficient approach combining a moving heat source analytical model with a melting and solidification model is presented. An innovative method is proposed to compute the “effective computation zone” as a boundary condition, which can save computation time significantly. Notably, the computational efficiency can improve by 10 4 –10 5 compared with finite element models. With this range of improvement efficiency, the temperature predicted based on this method is consistent (around 9% of average deviation) with experimental measurements by the thermocouple. This model can be used as a reference to define the boundary condition for further complex numerical analysis with improved accuracy at a reduction of efficiency as desired. In addition, it can be used as a reference to determine processing conditions that would allow the efficient and effective control of the temperature history within a range for a certain microstructure design.

Achieving defect-free parts is traditionally challenging in laser powder bed fusion (LPBF). The mechanical properties of additively manufactured parts are highly affected by their density; as such, research in defect detection and pore prediction has gained significant interest. The process parameters, the powder characteristics, and the process environment conditions play an important role in defect occurrence. Moreover, the laser scan path affects density, especially at scan path discontinuities. In this work, the complex interaction between the process parameters and the scan path on the occurrence of subsurface pores is investigated. In the data preparation step, a synthetic data set is generated to model the melt pool morphology along the scan path. A secondary data set containing the pore space of the resulting parts is obtained via X-ray computed tomography (CT) and is registered with the synthetic data set. Machine learning models, namely, a Conditional Variational AutoEncoder (CVAE) and a Convolutional Neural Network (CNN), are then trained based on the input features to predict pore occurrence. The performance evaluation of both CNN and CVAE models on synthetic data indicates that the scan path and process parameters can be utilized in predicting pore locations. Quantitative results show that employing offline CT images a priori in training the CVAE, without the need to have CT information in the test phase, leads the CVAE model to superior performance over the CNN.

Additive manufacturing (AM) for metals is a widely researched, continuously enhanced manufacturing process and is implemented across various industries. However, the AM process exhibits variation that affects the geometric quality of the end product. The effect of process variation on geometric quality is rarely considered during the design stages. In this paper, sources that influence the geometric quality in a metal AM process are reviewed from a robust design perspective and further sorted into control factors and noise factors. A framework for geometric robustness analysis of AM products is presented as an outcome. This framework would facilitate development of methods and tools to produce geometry assured AM products. Also, the prospects of variation simulation to support geometric robustness analysis and the challenges associated with it are discussed.

A critical task in multi-pass process planning for the five-axis machining of complicated parts is to determine the intermediate surfaces for rough machining. Traditionally, the intermediate surfaces are simply parallel Z-level planes, and the machining is of the simplest three-axis type. However, for complicated parts, this so-called Z-level method lacks flexibility and causes isolated islands on layers, which require extraneous air movements by the tool. Moreover, the in-process workpiece machined according to the Z-level method suffers from the staircase effect, which often induces unstable dynamic problems on the tool-spindle system. In this paper, we propose a new method of planning a five-axis machining process for a complicated freeform solid part. In our method, the intermediate surfaces are no longer planar but curved, and they are intrinsically influenced by the convex hull of the part. The powerful algebraic tool of geodesic distance field is utilized to generate the desired intermediate surfaces, for which collision-free five-axis machining tool paths are then planned. In addition, we propose a novel idea of alternating between the roughing and finishing machining operations, which helps improve the stiffness of the in-process workpiece. Ample physical cutting experiments are performed, and the experimental results convincingly confirm the advantages of our method.

Ti6Al4V is one of the vital metal alloys used in various industries including aerospace, especially at high-temperature applications, because of having high strength-to-weight ratio, and high melting temperature. Manufacturing these metal parts by the conventional subtractive methods have been challenging due to the difficulty involved with the cutting and machining it. However, additive manufacturing (AM) offers a convenient way for shaping this metal into the desired complex parts. Although different powder bed fusion (PBF) AM processes are time and cost effective, degradation of mechanical properties during high-temperature applications could be a concern for parts produced by them. Therefore, this study focuses on the anisotropic and high-temperature elastic and plastic behaviors of Ti6Al4V parts made using electron beam powder bed fusion (EB-PBF) process. Mechanical properties, like modulus of elasticity, 0.2% yield strength, ultimate tensile strength (UTS), and percent elongation, have been determined at 200 °C, 400 °C, and 600 °C temperatures from the samples produced in different build orientations. Considerable anisotropic behavior and temperature dependency were observed for all the analyzed properties. At 600 °C, various softening mechanisms such dislocation glide, grain boundary slip, and grain growth were anticipated to be activated reducing the flow stress and increasing the elasticity. Fractography analysis on fractured surfaces of the samples reveals various defects, including partially melted or unmelted powder particles near the surface and subsurface areas. Those internal and external defects are analyzed further using X-ray computed tomography (CT) and surface profilometer to show their effect on the anisotropic behaviors.

Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

A novel method to calibrate the work rolls of a precision leveler for heavy plates is proposed. During the leveling process, a sequence of bending steps is imposed on the plate by the work rolls of the leveler to eliminate flatness defects. The respective positions of the rolls define the intensity of the consecutive bends. Any deviation from the desired roll position may result in a suboptimal leveling process. Thus, an exact control of the position of the work rolls is required. Due to backlash, wear, tolerances, and elastic deformation, the roll positions cannot be determined from nominal parameters and dimensions. Therefore, the machine has to be calibrated and properly adjusted to find suitable reference positions for control. During calibration, the structure is prestressed with a suitable test force and the signals of force and displacement sensors are recorded. As a result, reference points for future control inputs are obtained. A major drawback of a frequently used calibration method for levelers is that the work rolls of multi-actuator gap levelers cannot be properly calibrated. In practice, the work rolls are manually adjusted by the maintenance personnel of the machine. In this case, backlash may cause the loaded work rolls to deviate from their intended positions. Thus, a new method to calibrate the position of the individually actuated work rolls is developed and tested.

7xxx-Series aluminum alloys have a wide application prospect in the automotive industry due to its higher strength than other series of aluminum alloy. However, little literature has been reported on the formability and strengthening behavior of 7xxx-series aluminum alloys for the structural components of body in white (BIW). In this paper, the formability and strengthening behavior of 7075-T6 were investigated systematically under hot forming–quenching (HFQ) integrated process. First, compound dies with rod-heated blank holder and water-cooled lower punch and upper die were set up for experiment. For low adhesion friction and high cooling rate, the optimum blanking temperature was determined as 400 °C. Second, forming state, thickness distribution, and temperature field were investigated through experiment and finite element simulations. A-pillar reinforcement panel was successfully manufactured based on the simulation results. The simulation data showed that the maximum thinning rate of the whole part is about 14.25%, and the maximum thickening rate is about 1.95%. Third, the formability and quench sensitivity of u7075-T6 were achieved through time–temperature–transformation (TTT) curves and continuous cooling precipitation (CCP) diagrams combined with the thermo-mechanical coupling simulation. Finally, the strengthening was measured by testing the tensile strength and Vickers hardness at different positions of formed parts after artificial aging process. The testing result showed that the strength of the formed parts after artificial aging at 120 °C for 24 h were 162.7–172.2 HV and 505–528 MPa, respectively.

Zirconia ceramics which are sometimes called “ceramic steel” have gained significant interest because of their excellent properties. However, it is desired to maintain the surface quality while increasing the economics of ceramics grinding process. A hybrid laser/grinding (HLG) process was utilized to grind zirconia ceramics which was irradiated with continuous wave laser before grinding in the hybrid process. The feasibility of hybrid laser/grinding of zirconia ceramics was investigated in terms of grinding force and energy, material removal, and damage formation mechanisms. The results show that laser irradiation can induce lateral cracks, which can help material removal and prevent further crack propagating into the base. The results of grinding tests indicate that grinding force and energy decrease significantly as compared with conventional grinding of ceramics. The combinations of the fractured area, the plowing striations, and seldom debris on the ground surfaces in this work indicate the combined material removal mechanism of both brittle mode and ductile mode.

Gear shaping is, currently, the most prominent method for machining internal gears, which are a major component in planetary gearboxes. However, there are few reported studies on the mechanics of the process. This paper presents a comprehensive model of gear shaping that includes the kinematics, cutter–workpiece engagement (CWE), and cutting forces. To predict the cutting forces, the CWE is calculated at discrete time steps using a tridexel discrete solid modeler. From the CWE in tridexel form, the two-dimensional (2D) chip geometry is reconstructed using Delaunay triangulation (DT) and alpha shape reconstruction. This in turn is used to determine the undeformed chip geometry along the cutting edge. The cutting edge is discretized into nodes with varying cutting force directions (tangential, feed, and radial), inclination angles, and rake angles. If engaged in the cut during a particular time-step, each node contributes an incremental force vector calculated with the oblique cutting force model. Using a three-axis dynamometer on a Liebherr LSE500 gear shaping machine tool, the cutting force prediction algorithm was experimentally verified on a variety of processes and gears, which included an internal spur gear, external spur gear, and external helical gear. The simulated and measured force profiles correlate closely with about 3–10% RMS error.

Barrel-shaped surfaces are widely used in industries, e.g., blades, vases, and tabular parts. Because a part such as an aero-engine blade is typically quite large, the efficiency of its measurement becomes a critical issue. The recently emerged five-axis sweep scanning technology offers to be a powerful means to significantly increase the efficiency of measurement. However, currently it still mostly relies on humans to manually plan a five-axis sweep scanning path, and in most cases, the surface is simply divided into a number of smaller open patches for which the sweep scanning is then individually planned. We present an algorithm for automatically planning the five-axis sweep scanning for an arbitrary barrel-shaped surface in the form of either a compound, a trimmed, or a simple surface. The planning algorithm is novel in that no partitioning of the surface is needed and a single continuous five-axis sweep scanning path will be generated for the entire surface. By eliminating the nonsweeping time spent by the stylus due to its air-moves between multiple patches and also the time-costly approach-retraction operations required for each patch, the proposed algorithm is able to significantly reduce the total inspection time, sometimes more than 50%, as validated in our physical inspection experiments.

Oxygen inhibition has been proved capable of reducing the separation force and enabling successful prints in constrained surface vat photopolymerization (CSVP) based three-dimensional (3D) printing processes. It has also been demonstrated as a key factor that determines the feasibility of the newly developed CSVP-based continuous 3D printing systems, such as the continuous liquid interface production. Despite its well-known importance, it is still largely unknown regarding how to control and enhance the oxygen inhibition in CSVP. To close this knowledge gap, this paper investigates the constrained surface design, which allows for continuous and sufficient air permeation to enhance the oxygen inhibition in CSVP systems. In this paper, a novel constrained surface with air-diffusion-channel is proposed. The influences of the air-diffusion-channel design parameters on the robustness of the constrained surface, the light transmission rate, and light intensity uniformity are studied. The thickness of the oxygen inhibition layer associated with the proposed constrained surface is studied analytically and experimentally. Experimental results show that the proposed air-diffusion-channel design is effective in maintaining and enhancing the oxygen-inhibition effect, and thus can increase the solid cross section size of printable parts.

Hybrid additive manufacturing (hybrid-AM) has described hybrid processes and machines as well as multimaterial, multistructural, and multifunctional printing. The capabilities afforded by hybrid-AM are rewriting the design rules for materials and adding a new dimension in the design for additive manufacturing (AM) paradigm. This work primarily focuses on defining hybrid-AM in relation to hybrid manufacturing (HM) and classifying hybrid-AM processes. Hybrid-AM machines, materials, structures, and function are also discussed. Hybrid-AM processes are defined as the use of AM with one or more secondary processes or energy sources that are fully coupled and synergistically affect part quality, functionality, and/or process performance. Historically, defining HM processes centered on process improvement rather than improvements to part quality or performance; however, the primary goal for the majority of hybrid-AM processes is to improve part quality and part performance rather than improve processing. Hybrid-AM processes are typically a cyclic process chain and are distinguished from postprocessing operations that do not meet the fully coupled criterion. Secondary processes and energy sources include subtractive and transformative manufacturing technologies, such as machining, remelting, peening, rolling, and friction stir processing (FSP). As interest in hybrid-AM grows, new economic and sustainability tools are needed as well as sensing technologies that better facilitate hybrid processing. Hybrid-AM has ushered in the next evolutionary step in AM and has the potential to profoundly change the way goods are manufactured.

This paper formulates the generalized dynamics and stability of thread turning operations with custom multipoint inserts. The closed-loop chip regeneration mechanism is modeled by evaluating the effect of the current vibrations and the vibration marks left from the previous tooth. Using the developed chip discretization method, the dynamic cutting and process damping forces are obtained at each point along the cutting edge by projecting the three-dimensional (3D) vibrations of the tool and workpiece in the direction of local chip thickness. The equation of motion is derived in both physical and modal spaces, and stability is analyzed in frequency domain using Nyquist criterion. An iterative process optimization algorithm has been developed to maximize productivity while respecting machine tool's torque and power limits. Extension of the model to thin-walled workpieces along with the validating experiments on real-scale oil pipes is presented in Part II of this paper.

In most trendsetting industries like the aerospace, automotive and medical industry functionally critical parts are of highest importance. Due to strict legal requirements regarding the securing of the functionality of high-risk parts, both production costs and quality costs contribute significantly to the manufacturing costs. Thus, both types of costs have to be taken into consideration during the stage of technology planning. Due to the high variety of potential interactions between individual component properties as well as between component properties and manufacturing processes, the analysis of the influence of the manufacturing history on an efficient design of inspection processes and inspection strategies is extremely complex. Furthermore, the effects of inspection strategies and quality costs on the planning of manufacturing process sequences cannot be modeled to date. As a consequence, manufacturing and inspection processes are designed separately and thus a high cost reduction potential remains untapped. In this paper, a new approach for an integrative technology and inspection planning is presented and applied to a case study in medical industry. At first, existing approaches with regard to technology and inspection planning are reviewed. After a definition of relevant terms, the case study is introduced. Following, an approach for an integrative technology and inspection planning is presented and applied to the case study. In the presented approach, the complex causalities between technology planning, manufacturing history, and inspection planning are considered to enable a cost-effective production process and inspection sequence design.