1 Introduction
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How much activity is involved in the field of process monitoring?
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What methods and technologies are used for the process monitoring of which quality characteristics?
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What are the research gaps?
2 Material extrusion
Defect | Cause | Outcome | References |
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Bubbles and bulges | Moisture bound in the material evaporates explosively during processing | Compromised mechanical properties, impaired surface quality | |
Incorrect bead deposition position | Faults in the kinematic structure, printing of unsupported overhangs | Geometric deviations | |
Overfill | Incorrect process parameters, errors in motion control | Increased bead width, bump formation | |
Scars | Nozzle grinds over the previously printed layer | Impaired surface quality | [50] |
Stringing | Printing temperature too high, incorrect filament retraction settings | Material oozes out of the nozzle of the moving extruder, even though no extrusion is intended | |
Underfill | Faults in the kinematic structure, clogged nozzle, incorrect process parameters | Voids, reduced bead width, stopped material extrusion, compromised mechanical properties | |
Warpage and shrinkage | Temperature gradients in the part | Delamination, cracking, part deformation |
3 Materials and methods
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one of the sub-categories of MEX is treated;
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central aim is in-situ process monitoring for quality assurance (assessing the status of 3D printer components or parts in production);
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contribution is original research (peer-reviewed), dissertation or active patent.
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process monitoring is included but not for the purpose of quality assurance (e.g., sensor system to validate a simulation of the MEX process);
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not in English or German;
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older than 2013.
4 How much activity is involved in the field of process monitoring?
5 What methods and technologies are used for the process monitoring of which quality characteristics?
5.1 Sensor technology groups and inspected elements
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extrusion head (EH), including the extrusion nozzle and feedstock delivery mechanism;
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feeding system (FS), for feedstock transport to the extrusion head;
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build chamber (BC), including the housing and frame;
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build platform (BP); and
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axis system (AS), including the motors.
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entire part (P);
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layers (L), equivalent to the build surfaces in the majority of cases; and
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sidewalls of part (S).
5.2 2D vision
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[93] | Camera | EH | Convolutional neural network | Offset nozzle height | F3 | D2 |
[94] | Camera | AS | Comparison with G-code | Area of layer | F3 | D2 |
[95] | Camera | AS | Comparison with ideal process | Voids | F3 | D2 |
[96] | Camera | P | Cascade classifiers, comparison with simulated reference image | Geometric deviations | F3 | D1 |
[97] | Camera | P | Principal component analysis and support vector machine, convolutional neural network | Defective part | F3 | D2 |
[98] | Camera | P | Deep learning | Defective process | F3 | D2 |
[99] | Camera | L | Image visualization | Layer surface | F2 | D1 |
[100] | Camera | L | Contour detection | Geometric deviations | F2 | D2 |
[101] | Camera | L | Visualizing in mixed reality | Not applicable (n.a.) | F2 | D2 |
Camera | L | Comparison with reference | Infill structure, part position | F3 | D1 | |
[89] | Camera | L | Comparison with reference | Geometric deviations | F3 | D1 |
Camera | L | Naive Bayes classifier, decision trees, random forest, k-nearest neighbors, anomaly detection, cyber-physical alert correlation | Infill structure voids | F3 | D2 | |
[107] | Camera | L | Comparison with STL file | Geometric deviations | F3 | D2 |
[108] | Camera | L | Random forest | Infill structure voids | F3 | D2 |
[58] | Camera | L | Data fusion, measurements | Bead thickness/intersections/ alignment, geometry | F3 | D2 |
[65] | Camera | L | Comparison with G-code | Voids, bead shape | F3 | D2 |
Camera | L | Statistical process control | Layer contour, overfill, underfill | F3 | D2 | |
[111] | Camera | L | Comparison with tolerance range | Geometric deviations | F4 | P |
[112] | Camera | L | Convolutional neural network | Overfill, underfill | F4 | D2 |
[113] | Camera | S | Differential imaging, blob detection | Detachment, geometric deviations, stopped material flow | F3 | D2 |
Camera | S | Image mining | Part quality | F3 | D2 | |
Camera | S | Neural network | Blobs, voids, thick beads, crack, misalignment | F3 | D2 | |
[92] | Camera | S | Comparison with ideal, deep reinforcement learning | Geometric deviations | F4 | D2 |
[117] | 1/multiple cameras | S | Comparison with ideal | Geometric deviations | F4 | P |
[118] | 1/multiple cameras | L, S | Comparison with CAD model | Parts geometry/position | F3 | P |
5 cameras | S | Comparison with reference | Extrusion stop, material color | F3 | D2 | |
[127] | Camera, illumination | P | Comparison with CAD model | Geometric deviations | F3 | D2 |
[128] | Camera, illumination | P | Comparison with reference | Warping, detachment, extrusion stop | F3 | D2 |
[129] | Camera, illumination | L | Comparison with STL file | Geometric deviations | F3 | D1 |
[130] | Camera, illumination | L | Texture analysis | Layer surface irregularities, geometric deviations | F3 | D1 |
Camera, illumination | L | Statistical process control | Layer contour | F3 | D2 | |
[134] | Camera, illumination | L | Comparison with ideal part, support vector machine | Defective parts | F3 | D2 |
[59] | Camera, illumination | S | Fourier analysis | Layer height | F3 | D1 |
[75] | Camera, illumination | S | Comparison with STL file | Geometric deviations | F3 | D2 |
[135] | Camera, illumination | S | Comparison with reference | Layer shifting | F3 | D2 |
Camera, illumination | S | Measurements, comparison with theoretical model | Voids, shape contour | F3 | D2 | |
[136] | 1/multiple cameras, illumination | P | Comparison with G-code | Detachment, extrusion stop, geometric deviations | F3 | P |
2/3 cameras, illumination | EH, L | Various measurements | Bead structures, deposition area characteristics | F3 | D2 | |
[137] | Multiple cameras, illumination | P | Comparison with CAD model, hidden Markov models, Bayesian inference, neural network | Outer surface of part | F4 | P |
[138] | Line scan camera, illumination | L | n.a | Defective process | F3 | P |
Camera, flatbed scanner | S | Texture analysis for feature extraction | Surface quality | F3 | D1 | |
[156] | Flatbed scanner | L | Distortion adjustment | Layer contour | F1 | D1 |
Digital microscope | EH | Measurements, filament feed speed control | Feeding gear slippage, material flow rate | F4 | D2 | |
[159] | Digital microscope | L | Image visualization | Voids, bead shape | F1 | D2 |
2 digital microscopes, illumination | L | Texture analysis, k-nearest neighbors, naive Bayes classifier, linear discriminant analysis, support vector machine, PID controller | Overfill, underfill | F4 | D2 | |
[163] | Optical sensor | FS | n.a | Material flow rate | F4 | P |
5.3 Temperature monitoring
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[164] | Thermal camera | EH | Temperature control methods | Polymer melt temperature | F4 | D2 |
[165] | Thermal camera | L | Spatial and time-domain data processing | Layer temperature | F2 | D2 |
Thermal camera | L | Sensing with limited sensor data | Layer temperature | F2 | D2 | |
Thermal camera | L | Rules of knowledge, support vector machine | Nozzle clogging, warping, underfill, geometric deviations | F3 | D2 | |
[60] | Thermal camera | L | Process temperature data, control layer start time | Short layer build times | F4 | D2 |
[171] | Thermal camera | S | Spatial and time domain data processing | Surface temperature, bond shape between beads | F2 | D2 |
[61] | Thermal camera | S | Spatial domain data processing | Temperature profiles | F2 | D2 |
[172] | Thermal camera | S | Correct temperature measurements | Surface temperature | F2 | D2 |
Thermal camera | S | Analytical prediction model | Temperature of weld interface, part tensile strength | F3 | D2 | |
[175] | Infrared | EH | n.a | Irregular material flow | F4 | P |
2 thermistors | EH | Feed-forward control | Temperature of nozzle/heater block | F4 | D2 | |
[178] | 3 thermistors | EH, BC, BP | PID controller | Local temperatures | F4 | D1 |
[179] | 3 thermocouples | L | Time domain data processing | Local layer temperature | F2 | D2 |
[180] | Infrared, thermocouple, thermistor | EH, BP, L | Neural network, support vector machine, linear regression, PID controller | Distortion | F4 | D2 |
5.4 Vibration monitoring
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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Accelerometer | EH | Analytical model, frequency and time domain analysis | Nozzle clogging | F2 | D2 | |
[183] | Accelerometer | AS | Logistic regression, support vector machine, random forest | Warping, extrusion stop | F3 | D2 |
[184] | Accelerometer | n.a | Frequency and time domain analysis, comparison with ideal working status | Various defects | F3 | D1 |
[162] | 2 accelerometers | EH, BP | Statistical process control | Voids | F3 | D2 |
[185] | 2 accelerometers | EH, BP | Support vector machine, neural network | Filament jam, warpage, material leakage | F3 | D2 |
[87] | 5 accelerometers | BC, AS | Neural network | Mechanical failure, axle failure | F3 | D2 |
5.5 3D vision
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[186] | Camera, structured light | L | Extracting sub-region features, comparison with CAD model | Holes, bumps, curling | F3 | D2 |
[187] | 2 cameras, structured light | L | Deep learning | Process shifts | F3 | D2 |
2 cameras, structured light | L | Comparison with G-code | Geometric deviations | F3 | D2 | |
2 cameras, structured light | S | Texture analysis | Surface quality | F3 | D1 | |
[192] | Camera, illumination | L | Comparison with reference, artificial intelligence control | Various defects | F4 | P |
[193] | 2 cameras, illumination | L | Comparison with G-code | Geometric deviations, holes, blobs | F3 | D2 |
3 × 2 cameras, illumination | S | Comparison with STL file | Geometric deviations | F3 | D2 | |
[194] | 3D camera | P | Comparison with reference | Geometric deviations | F4 | P |
[195] | Laser triangulation | L | Comparison with CAD model, measurement of defects | Underfill, overfill | F3 | D2 |
[196] | Laser triangulation | L | Visualizing sensor data | Bead shape | F4 | D1 |
[62] | Laser triangulation | L | Comparison with G-code | Underfill, overfill | F4 | D2 |
Laser triangulation | L | Comparison with nominal layer height, re-slicing | Layer height, bead width | F4 | D2 | |
[64] | Laser triangulation | L | Comparison with reference, generating modified path | Spatial bead position | F4 | D2 |
[197] | 2 laser triangulation | L | 2D comparison with G-code | Geometric deviations, voids | F3 | D2 |
[198] | n.a | L | Comparison with reference | Geometric deviations | F3 | P |
5.6 Acoustic emission monitoring
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[199] | Acoustic emission | EH | Feature-based time domain analysis | Filament breakage | F2 | D2 |
[200] | Acoustic emission | EH | Frequency domain analysis | Extruder state | F2 | D2 |
[201] | Acoustic emission | EH | Clustering by fast search and finding of density peaks | Extruder state | F3 | D2 |
Acoustic emission | EH | Hidden semi-Markov model, support vector machine | Extruder state | F3 | D2 | |
Acoustic emission | BP | Hidden semi-Markov model, support vector machine, acoustic emission hits | Curling, detachment | F3 | D2 | |
Acoustic emission | BP | k-means clustering, neural network | First layer defects | F3 | D2 | |
[207] | Audio recorder | EH, AS | Gradient boosting regression, logistic regression classifier | Geometric deviations | F3 | D2 |
[208] | Microphone | EH, AS | Audio classifier for comparison with ideal process | Infill pattern, fill density | F3 | D2 |
[209] | Microphone | EH, BC, AS | Neural network | Nozzle offset height, fan activity, 3D printer activity, door opening/closing, axes movements | F3 | D2 |
[210] | Smartphone | EH, AS | Comparison with ideal process | Malicious modified G-code | F3 | D2 |
5.7 Electrical quantities monitoring
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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Current | EH | Graphical frequency and time domain analysis | Extrusion pressure, foreign objects, deformation | F2 | D2 | |
[214] | Current | EH | Analytical model | Nozzle clogging conditions | F3 | D2 |
[215] | Current | EH, AS | Similarity measure with defect-free reference | Sabotage attacks in G-code | F3 | D2 |
Capacitive | P | n.a | Number of layers, holes | F1 | D1 | |
[108] | Power | EH, AS | Random forest | Infill structure voids, extrusion temperature | F3 | D2 |
5.8 Force and pressure monitoring
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[71] | Load cell | EH | n.a | Piston force | F1 | D2 |
[218] | Load cell | FS | Digital-twin, threshold for defect detection | Filament amount in storage | F3 | D2 |
[219] | Force | EH | n.a | Contact force against the nozzle | F3 | P |
[69] | Force/torque | EH | Visualization, threshold for defect detection | Fiber pullout/shearing | F3 | D2 |
[220] | Pressure | EH | n.a | Pressure in the liquefier, material flow rate | F4 | P |
5.9 Other sensor technologies
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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Fiber Bragg grating | P | Analysis of wavelength changes | Strain | F2 | D2 | |
Fiber Bragg grating | P | Analysis of wavelength changes | Strain | F2 | D2 | |
[226] | Fiber Bragg grating | P | Analysis of wavelength changes | Strain | F2 | D2 |
[227] | 1/2 fiber Bragg grating | P | n.a | Strain, temperature | F1 | D2 |
[228] | Optical backscatter reflectometry | P | Analysis of frequency shifts | Strain, voids | F2 | D2 |
[229] | Ultrasonic | P | n.a | Infill structure | F1 | D1 |
1/2 ultrasonic | P | n.a | Fiber-scale print errors, bonding strength, orientation of beads | F1 | D2 | |
4 ultrasonic | P | Comparison with ideal part, control feedback | Delamination, geometry | F4 | D1 | |
[234] | Ultrasonic, laser Doppler vibrometer | L | Data visualization | Foreign objects, holes | F1 | D2 |
Optical encoder | FS | Calculation of filament movement | Filament blockage/speed, lack of filament | F3 | D2 | |
[237] | Linear encoder | AS | Proportional-integral control | Position of axes | F4 | D2 |
[238] | Laser displacement | L | Comparison with CAD model | Geometric deviations | F2 | D2 |
[239] | Interferometry | BP | Calculation of surface curvature | Deformations | F2 | D2 |
[240] | Vibroacoustic | BP | Discrete wavelet transform | First layer adhesion | F2 | D2 |
[93] | 2 strain gauges | BP | Threshold analysis | Warping | F3 | D2 |
[208] | Gyroscopic | AS | Real-time visualization | Infill pattern, fill density | F2 | D2 |
[241] | Coordinate measuring machine | P | Comparison with reference, adjust process | Geometric deviations | F4 | P |
[242] | Split ring resonator probe | P | Generate 3D map of part | Relative dielectric permittivity, dimensions | F2 | D2 |
[243] | Velocimetry | EH, FS | Controller | Extrudate flow rate, filament feed rate | F4 | P |
[244] | Magnetic | FS, BC, AS | n.a | Door access, motor step losses, build platform level, material transport | F1 | D2 |
[245] | n.a | L | Re-slicing | Various defects | F4 | P |
5.10 Sensor fusion technologies
References | Sensors | Ele | Data handling | Quality characteristics | Fun | Dev |
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[247] | Pressure, thermocouple | EH | Rheological modeling of polymer melt | Polymer melt pressure/temperature | F2 | D2 |
Angle, gyroscopic, accelerometer, magnetic | EH | Machine learning approaches | Joint bearing abrasion, driving belt fault | F3 | D2 | |
2 digital microscopes, strain gauge | EH | Analytical model, measurements, filament feed speed control | Feeding gear slippage, material flow, pressure drop over liquefier | F4 | D2 | |
[248] | Acoustic emission, strain | BC | Feature extraction, filtering | Driving belt fault | F2 | D2 |
Fiber Bragg grating, thermocouples | P | Analysis of wavelength changes and temperature profiles | Strain, local layer temperature | F2 | D2 | |
Laser profilometer, thermal camera | L | Comparison with reference, control layer start time and layer height | Underfill, overfill, low layer times | F4 | D2 | |
[252] | 3D printer data | EH, FS | Digital twin with formal logic | Extruder temperature, energy efficiency | F3 | D2 |
[253] | Encoder, sensor module | EH, AS | Control module | Filament feed rate, axes position | F4 | P |
[68] | Touch probe, electrical touch plate | EH, L | Feedback loop architecture | Nozzle height, layer height | F4 | D2 |
4 microphones, 3 accelerometers, 3 magnetic, current | BC, AS | Regression model, classifiers, comparison with digital twin | Geometric deviations, flowrate | F3 | D2 | |
[256] | 2D vision, 3D vision, other | BC, P | Comparison with reference | Anomalies in material, part, environment | F3 | P |
[257] | 2D vision, 3D vision | L, S | Comparison with reference, change machine code | Various defects | F4 | P |
Optical encoder, thermometer, thermistor, humidity, array of photodiodes | EH, FS, BC | Collection and storage of data streams | Nozzle/ambient temperature, humidity, filament diameter/speed | F2 | D2 | |
Load cell, 4 thermocouples, 3D printer data, encoder | EH, FS, BC | Analytical models | Polymer melt characteristics, filament flow rate, interlayer contact characteristics | F3 | D2 | |
[263] | 2 accelerometers, 2 temperature | EH, BC, BP | k-nearest neighbors, decision tree, support vector machine, naive Bayes classifier, random forest, k-means clustering, expectation maximization | Interferences | F3 | D2 |
[264] | Thermal camera, accelerometer, acoustic emission | EH, BC, AS | Bayesian networks | 3D printer condition | F3 | D1 |
[265] | Strain gauge, tension, 4 accelerometers | EH, BP, AS | Digital twin, comparison with predicted data | Not specified in detail | F3 | D1 |
[266] | 3 accelerometers, acoustic emission | EH, BP, AS | Support vector machine | Loosened bolt, shifting of layers | F3 | D2 |
[267] | 2 thermocouples, 2 accelerometers, infrared | EH, BP, L | Ensemble method with multiple machine learning algorithms | Surface roughness | F3 | D2 |
[268] | 2 cameras, encoders | EH, AS, P | Comparison with G-code and 3D model | Axes position, geometric deviations, extrusion stop | F3 | D1 |
[269] | 2 optical encoders, 4 thermocouples, camera | EH, AS, L | Fusion of sensor data | Axes position, filament flow, extruder temperature, layer defects | F2 | D2 |
[270] | 2 accelerometers, magnetic, camera, acoustic emission | EH, AS, S | Kalman filter, Canny filter, random forest | Infill geometry, printing speed, layer height, fan speed | F3 | D2 |
Infrared, thermocouple, accelerometer | BC, BP, L | Neural network | Tensile strength | F3 | D2 | |
2 laser triangulation, accelerometers | BC, AS, L | Comparison with reference, predictive modeling with random forest, decision tree, and neural network | Overfill, underfill, detachments | F4 | D1 | |
[274] | Vibration, magnetic, temperature, dust, humidity | EH, FS, BC, BP | Low-pass filter with fast Fourier transform | Machine and part state | F4 | D1 |
[275] | 3 accelerometers, acoustic emission, 3 thermocouples, thermal camera | EH, BC, BP, L | Support vector machine | Bed leveling | F3 | D2 |
6 thermocouples, 2 accelerometers, infrared | EH, BC, BP, L | Dirichlet process mixture model and evidence theory, sparse estimation, quantitative and qualitative models | Insufficient extrusion, dimensional accuracy, surface roughness | F3 | D2 | |
[279] | n.a | P | Comparison with reference | Build perimeter/height/volume | F3 | P |
[70] | n.a | P | Comparison with reference, adjust G-code | Bead characteristics | F4 | P |
6 What are the research gaps?
6.1 Key topics for sensor technology and data processing
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RT1: new in-situ imaging modalities
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RT2: real-time process measurement at required spatial and temporal resolution
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RT3: in-situ control and model integration
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RT4: big data analytics
6.2 Rarely examined quality characteristics
6.3 Variety and complexity of monitored parts
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complexity of geometries (simple or complex),
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number of different geometries,
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materials used, and
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number of different materials used.