A New Computationally Efficient Model for Tempering in Multi-Tracks Laser Hardening

The bottleneck in laser hardening principally occurs when large surfaces have to be treated because this process situation leads to multi-tracks laser scanning in order to treat all the component surface. Unfortunately, multi-tracks laser trajectories generate an unwanted tempering effect that depends on the overlapping of two close trajectories. To reduce the softening effects, a simulator capable to optimize the process parameters such as laser power and speed, number and types of trajectories, could sensibly increase the applicability of the process. In this paper an original model for the tempering is presented. By introducing a tempering time factor for the martensitic transformation, the hardness reduction can be predicted according to any laser process parameters, material and geometry. Experimental comparisons will be presented to prove the accuracy of the model.

Fabrication and Characterization of Bulk Nanocrystalline Layer in Steel and Aluminum Alloys Formed by Shot Peening

The formation of bulk nanocrystalline (NC) layers in AISI 1075 pearlitic and martensitic steels and aluminum alloys 6061-T6 and 7075 using air blast shot peening was studied. The cross-sectional microstructure of the samples showed a gradual reduction of the grain size near the surface. The NC layers were characterized using optical and scanning electron microscopy and nanohardness measurements. 2D surface topography of the top surface was also carried out. The roughness of the peened surfaces depends on sample hardness. The hardened AISI 1075 martensitic steel had lower surface roughness value. NC layers of 5 to 15 μm thickness were observed in the steels whereas the aluminum alloys 6061 and 7075 yielded NC layers up to 20 to 25 μm thick. The measured nanohardness in the NC layers confirmed the higher hardness of the NC layer compared with the bulk material.

Design of Compact 6-DOF Precision Aligner for Micro-Assembly

This work will develop a 6 Degrees Of Freedom (DOF) precision aligner with a small size and a high resolution for the assembly of the micro-scale parts used in mobile electronics. The arrangement of actuators in the aligner is symmetrical, based on kinematic design. A VCM (Voice Coil Motor) actuator that is small and reliable will be applied to this aligner system. This paper presents the combination of two modules to form the mechanism for the 6-DOF precision aligner. The first is a stage that can control XY θZ motion, while the second can control Z motion, i.e. Z tilt motion. According to its specifications, it is expected to satisfy precision requirements. Several tests will be carried out to confirm the specifications with PID control.

Optimization of Laser Hardening Processes for Industrial Parts With Complex Geometry via Predictive Modeling

A predictive laser hardening model for industrial parts with complex geometric features has been developed and used for optimization of hardening processes. A transient three-dimensional thermal model is combined with a three-dimensional kinetic model for steel phase transformation and solved in order to predict the temperature history and solid phase history of the workpiece while considering latent heat of phase transformation. Further, back-tempering is also added to the model to determine the phase transformation during multitrack laser hardening. The integrated model is designed to accurately predict temperature, phase distributions and hardness inside complex geometric domains. The laser hardening parameters for two industrial workpieces are optimized for two different industrial laser systems using this model. Experimental results confirm the validity of predicted results.

Feasibility Study of the Effectiveness of Cryogenically Treated Ceramic Inserts for Pocket Milling Maneuvers

Machining is a popular and versatile manufacturing process that is widely used in today’s industry when producing metallic parts; however, limited tool life can make this an expensive and time consuming fabrication technique. Consequently, methods that decrease the rate of tool wear and, thus, increase tool longevity are a vital component when improving the efficiency of machining processes. To this end, cryogenically treating cutting tools (especially high-speed steel tooling) is becoming more commonplace since research has shown that the treated tooling exhibits significantly higher wear resistance. At this point, however, the effect of cryogenic treatments on ceramic tooling has not been established. Considering this, the research herein presents a feasibility study on the effectiveness of using cryogenic treatments to enhance the wear resistance of WG-300 whisker-reinforced ceramic cutting inserts. To begin, the effect of the cryogenic treatment on the insert’s hardness is examined. Subsequently, tool wear tests are conducted at various cutting speeds. Through this study, it is shown that cryogenically treating the ceramic inserts decreases the rate of tool wear at each of the cutting speeds that were tested. However, the degree of wear resistance introduced by cryogenically treating the inserts proved to be highly dependent on the cutting speed, with slower speeds exhibiting greater improvements. Thus, based on this initial study, the cryogenic treatment of ceramic tooling appears to produce beneficial results, potentially increasing the overall efficiency of machining processes.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Sustainability and Manufacturing Philosophy: From Mass Production to Intelligent Energy Field Manufacturing

In the downturn of economy, it is meaningful to reflect on the evolution of engineering philosophies. This paper gives a historical review of major manufacturing philosophies and methodologies, such as mass production, lean manufacturing, systematic innovation, digital manufacturing, and sustainable development. The general strategy of Intelligent EFM and the from nature to nature philosophy of engineering are established to help achieve both prosperity and sustainability.

Robotic Manufacturing of Near-Net Shaped Short-Fiber Reinforced Composites With Functional Properties

Fiber reinforced composite materials are now frequently being used over conventional materials for their ability to achieve tailored properties and performance characteristics. With the recent advancements in manufacturing techniques, short-fiber composites are coming into prominence in this sector, with their cost advantage and their capability for large throughput. Randomness of fiber orientation is inherent to short fiber composite manufacturing processes. In order to effectively manipulate the mechanical properties of a short-fiber reinforced composite, it is imperative to adequately control the orientation of the fibers during the deposition stage. A process is currently developed to acquire geometrical data of the target object and to utilize it to create a short-fiber reinforced component with controlled fiber orientation. The topological data acquisition of the object is made possible using non-contact 3D imaging techniques. The geometric data is then transferred to a commercial CAD package for the added capability to manipulate the geometry as may be required for specific applications. Subsequently, geometric data constitutes the basis of path planning for the tooling processes. In our process, a novel rapidly re-configurable tooling and molding technology is employed by which a 6-axis robotic arm is used to sculpt a pin-device vacuum surface. After the tooling is completed, the robotic arm will use a deposition nozzle to orient a steady stream of initially random short-fiber from a feeder into a unidirectional output, onto the tool surface. By controlling the position and orientation of the deposition nozzle, it is possible to control the orientation and density of fiber in each section of the near-net shaped composite pre-form. The fiber pre-form is then impregnated with a suitable matrix medium and cured to create the required component. The outlined process is thus capable of manufacturing a near-net shaped short-fiber reinforced component with highly specific mechanical properties. One of the many applications envisaged using this process is the manufacture of custom form-fitting braces, masks and guards for use in healthcare products. A patient intervention can have his or her features acquired using stereo-imaging and have corrective measures incorporated into the device prior to manufacturing. By controlling the orientation and density of the fiber at different portions of the device, it is possible to provide adequate support at specific areas or to restrict movement in specific directions while providing compliance to movement in the others.

High-Definition Metrology Based Spatial Variation Pattern Analysis for Machining Process Monitoring and Diagnosis

Non-contact high-definition measurement technology, such as laser holographic interferometry, makes it feasible to quickly inspect dimensional variation at micron level, providing up to 2 million data points over a surface area of up to 300×300 mm2. Such high-definition metrology (HDM) data contain rich spatial variation information but it is challenging to utilize this information for process monitoring and control. The spatial distribution of the data is in high-dimensional form and may show nonlinear patterns. Conventional statistical process monitoring and diagnostic schemes based on simple test statistics and linear statistical process models are incapable of capturing the complex surface characteristics as reflected by large amounts of spatial data. This paper develops a framework for efficient monitoring of spatial variation in HDM data using principal curves and quality control charts. Since large scale surface variation patterns (caused by fixturing and part bending) may camouflage those in the smaller scale (generally associated with tooling conditions), it is essential to separate the patterns in these scales and monitor them individually. At each scale, process monitoring is implemented in a sequential manner by monitoring the overall spatial features followed by localized variation identification if an out-of-control condition is detected. To examine the overall spatial characteristics, a principal-component-analysis (PCA) filtered principal curve regression is proposed in conjunction with multivariate control charts whereby nonlinear patterns of spatial data are extracted and monitored. When the overall monitoring indicates a problem, the identification of a surface variation change can be achieved through localized monitoring over each surface region based on variogram pattern analysis and control charts. The location of surface region change provides clues for variation source diagnosis. The proposed method is illustrated using simulated HDM data.

Integrated Production and Preventive Maintenance Decision Making Using Option-Based Cost Model

This paper presents an analytical, option-based cost model for an integrated production and preventive maintenance decision making with stochastic demand. The determination of preventive maintenance times and their schedule during a production period is converted to an option problem through maximizing the profit of the production per unit time. The optimal number of preventive maintenance actions is obtained and some further discussions on how the cost parameters affect the optimal results are also derived. The resulting option-based model is found to add flexibility to the production system and thus reduce the risk of shortage when the production system is faced with stochastic demand. A comparisons between the basic model (without option) and the option-based preventive maintenance model has shown that the option model is a more flexible under demand uncertainty and results in at least as much profit as the basic one.