Robert G. Landers

Machining Process Monitoring and Control: The State-Of-The-Art

Research in automating the process level of machining operations has been conducted, in both academia and industry, over the past few decades. This work is motivated by a strong belief that research in this area will provide increased productivity, improved part quality, reduced costs, and relaxed machine design constraints. The basis for this belief is twofold. First, machining process automation can be applied to both large batch production environments and small batch jobs. Second, process automation can autonomously tune machine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substantially increase the machine tools performance in terms of part tolerances and surface finish, operation cycle time, etc. Process automation holds the promise of bridging the gap between product design and process planning, while reaching beyond the capability of a human operator. The success of manufacturing process automation hinges primarily on the effectiveness of the process monitoring and control systems. This paper discusses the evolution of machining process monitoring and control technologies and conducts an in-depth review of the state-of-the-art of these technologies over the past decade. The research in each area is highlighted with experimental and simulation examples. Open architecture software platforms that provide the means to implement process monitoring and control systems are also reviewed. The impact, industrial realization, and future trends of machining process monitoring and control technologies are also discussed.

Reconfigurable machine tools

In accordance with the teachings of the present invention, a machine tool assembly is easily reconfigurable to perform single or multiple machining processes on a workpiece so that this machine has exactly the functionality required to perform a given set of machining tasks. The invention allows rapid changes in the machine structure and rapid conversion of the machine by relocating its basic building blocks. The assembly secures a raw workpiece to a table and includes support units that carry at least one single-axis spindle unit. The spindle units are easily attached to one of the support units and are easily movable thereon to perform machining processes from various positions and orientations relative the workpiece. A cutting tool, or other machining tool, is secured to each spindle, which is computer controlled to rotate the tool and stroke it linearly along its axis of rotation. The support units are reconfigurable and can be easily relocated to different positions and orientations about the workpiece.

Applications of a hybrid manufacturing process for fabrication of metallic structures

Purpose – This paper sets out to summarize the current research, development, and integration of a hybrid process to produce high‐temperature metallic materials. It seeks to present the issues and solutions, including the understanding of the direct laser deposition process, and automated process planning.Design/methodology/approach – Research in simulation and modeling, process development, integration, and actual part building for hybrid processing are discussed.Findings – Coupling additive and subtractive processes into a single workstation, the integrated process, or hybrid process, can produce metal parts with machining accuracy and surface finish. Therefore, the hybrid process is potentially a very competitive process to fabricate metallic structures.Originality/value – Rapid prototyping technology has been of interest to various industries that are looking for a process to produce/build a part directly from a CAD model in a short time. Among them, the direct laser deposition process is one of the f...

Model-Based Machining Force Control

Regulating machining forces provides significant economic benefits by increasing operation productivity and improving part quality. Machining force regulation is a challenging problem since the force process varies significantly under normal operating conditions. Since fixed-gain controllers cannot guarantee system performance and stability as the force process varies, a substantial research effort has been invested in the development of adaptive force controllers. However, adaptive controllers can be difficult to develop, analyze, implement, and maintain due to their inherent complexity. Consequently, adaptive machining force controllers have found little application in industry. In this paper, a model-based machining force control approach, which incorporates detailed force process models, is introduced. The proposed design has a simple structure and explicitly accounts for the changes in the force process to maintain system performance and stability. Two model-based machining force controllers are implemented in face milling operations. The stability robustness of the closed-loop system with respect to model parameter uncertainties is analyzed, and the analysis is verified via simulation and experimental studies.

Multiaxis Contour Control—the State of the Art

Contour control is an important task for many motion control applications. This paper reviews the state-of-the-art control methods in academic research for contour tracking, including individual axis tracking control, cross coupled control and its variants, and other contour control methods, such as neural networks and velocity field control. Different methods for contour error estimation are also reviewed. Areas for future multiaxis contour control research are discussed.

Robust Machining Force Control With Process Compensation

Force control is an effective means of improving the quality and productivity of machining operations. Metal cutting force models are difficult to accurately generate and, thus, there is large uncertainty in the model parameters. This has lead to investigations into robust force control techniques; however, the approaches reported in the literature include known process changes (e.g., a change in the depth-of-cut) in the model parameters variations. These changes create substantial variations in the model parameters; thus, only loose performance bounds may be achieved. A novel robust force controller is presented in this paper that explicitly compensates for known process effects and accounts for the force-feed nonlinearity inherent in metal cutting operations. The controller is verified via simulation and experimental studies and the results demonstrate that the proposed controller is able to maintain tighter performance bounds than robust controllers that include known process changes in the model parameter variations.

Predictive Contour Control With Adaptive Feed Rate

Contour control is an important issue in motion system development. In this paper, a contour-control methodology combining predictive control and adaptive feed rate is proposed. To reduce the computational time required for online optimization, a simple unconstrained model predictive controller is first designed to perform biaxial contour-position control. The performance index introduced in this paper allows the designer to manipulate the importance between contour error, axial tracking error, and control usage. The controller is then implemented to track diamond and free-form contours. The results demonstrate that the controller is capable of not only tracking both contours with high precision (contour and axial) for steady motions, but also improving contour precision during transient periods. To further reduce the contour error during transient periods, an adaptive feed-rate scheme is proposed, which utilizes the predicted contour error to adjust the feed rate online. This adaptive feed-rate scheme is experimentally validated using diamond and limacon contours. The results demonstrate that in comparison with constant feed-rate schemes, the proposed adaptive feed-rate scheme is capable of significantly reducing the transient contour error at high feed rates, while maintaining comparable tracking performance at low feed rates.

Melt Pool Temperature Control for Laser Metal Deposition Processes—Part I: Online Temperature Control

Melt pool temperature is of great importance to deposition quality in laser metal deposition processes. To control the melt pool temperature, an empirical process model describing the relationship between the temperature and process parameters (i.e., laser power, powder flow rate, and traverse speed) is established and verified experimentally. A general tracking controller using the internal model principle is then designed. To examine the controller performance, three sets of experiments tracking both constant and time-varying temperature references are conducted. The results show the melt pool temperature controller performs well in tracking both constant and time-varying temperature references even when process parameters vary significantly. However a multilayer deposition experiment illustrates that maintaining a constant melt pool temperature does not necessarily lead to uniform track morphology which is an important criteria for deposition quality. The reason is believed to be that different melt pool morphologies may have the same temperature depending on the dynamic balance of heat input and heat loss.

Layer-to-Layer Height Control for Laser Metal Deposition Process

A laser metal deposition height control methodology is presented in this paper. The height controller utilizes a particle swarm optimization (PSO) algorithm to estimate model parameters between layers using measured temperature and track height profiles. Using the estimated model, the powder flow rate reference profile, which will produce the desired layer height reference, is then generated using iterative learning control (ILC). The model parameter estimation performance using PSO is evaluated using a four-layer single track deposition, and the powder flow rate reference generation performance using ILC is tested using simulation. The results show that PSO and ILC perform well in estimating model parameters and generating powder flow rate references, respectively. The proposed height control methodology is then tested experimentally for tracking a constant height reference with constant traverse speed and constant laser power. The experimental results indicate that the controller performs well in tracking constant height references in comparison with the widely used fixed process parameter strategy. The application of layer-to-layer height control produces more consistent layer height increment and a more precise track height, which saves machining time and increases powder efficiency.

Optimal Tracking Control of Motion Systems

Tracking control of motion systems typically requires accurate nonlinear friction models, especially at low speeds, and integral action. However, building accurate nonlinear friction models is time consuming, friction characteristics dramatically change over time, and special care must be taken to avoid windup in a controller employing integral action. In this paper a new approach is proposed for the optimal tracking control of motion systems with significant disturbances, parameter variations, and unmodeled dynamics. The ‘desired’ control signal that will keep the nominal system on the desired trajectory is calculated based on the known system dynamics and is utilized in a performance index to design an optimal controller. However, in the presence of disturbances, parameter variations, and unmodeled dynamics, the desired control signal must be adjusted. This is accomplished by using neural network based observers to identify these quantities, and update the control signal on-line. This formulation allows for excellent motion tracking without the need for the addition of an integral state. The system stability is analyzed and Lyapunov based weight update rules are applied to the neural networks to guarantee the boundedness of the tracking error, disturbance estimation error, and neural network weight errors. Experiments are conducted on the linear axes of a mini CNC machine for the contour control of two orthogonal axes, and the results demonstrate the excellent performance of the proposed methodology.