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Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Developments in additive manufacturing technology are serving to expand the potential applications. Critical developments are required in the supporting areas of measurement and in process inspection to achieve this. CM247LC is a nickel superalloy that is of interest for use in aerospace and civil power plants. However, it is difficult to process via selective laser melting (SLM) as it suffers from cracking during rapid cooling and solidification. This limits the viability of CM247LC parts created using SLM. To quantify part integrity, spatially resolved acoustic spectroscopy (SRAS) has been identified as a viable non-destructive evaluation technique. In this study, a combination of optical microscopy and SRAS was used to identify and classify the surface defects present in SLM-produced parts. By analysing the datasets and scan trajectories, it is possible to correlate morphological information with process parameters. Image processing was used to quantify porosity and cracking for bulk density measurement. Analysis of surface acoustic wave data showed that an error in manufacture in the form of an overscan occurred. Comparing areas affected by overscan with a bulk material, a change in defect density from 1.17% in the bulk material to 5.32% in the overscan regions was observed, highlighting the need to reduce overscan areas in manufacture.

Due to its economical production process polysilicon, or multicrystalline silicon, is widely used to produce solar cell wafers. However, the conversion efficiencies are often lower than equivalent monocrystalline or thin film cells, with the structure and orientation of the silicon grains strongly linked to the efficiency. We present a non-destructive laser ultrasonic inspection technique, capable of characterising large (52 x 76 mm2) photocell's microstructure – measurement times, sample surface preparation and system upgrades for silicon scanning are discussed. This system, known as spatially resolved acoustic spectroscopy (SRAS) could be used to optimise the polysilicon wafer production process and potentially improve efficiency.

Measuring the grain structure of aerospace materials is very important to understand their mechanical properties and in-service performance. Spatially resolved acoustic spectroscopy is an acoustic technique utilizing surface acoustic waves to map the grain structure of a material. When combined with measurements in multiple acoustic propagation directions, the grain orientation can be obtained by fitting the velocity surface to a model. The new instrument presented here can take thousands of acoustic velocity measurements per second. The spatial and velocity resolution can be adjusted by simple modification to the system; this is discussed in detail by comparison of theoretical expectations with experimental data.

Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Developments in additive manufacturing technology are serving to expand the potential applications. Critical developments are required in the supporting areas of measurement and in process inspection to achieve this. CM247LC is a nickel superalloy that is of interest for use in aerospace and civil power plants. However, it is difficult to process via selective laser melting (SLM) as it suffers from cracking during rapid cooling and solidification. This limits the viability of CM247LC parts created using SLM. To quantify part integrity, spatially resolved acoustic spectroscopy (SRAS) has been identified as a viable non-destructive evaluation technique. In this study, a combination of optical microscopy and SRAS was used to identify and classify the surface defects present in SLM-produced parts. By analysing the datasets and scan trajectories, it is possible to correlate morphological information with process parameters. Image processing was used to quantify porosity and cracking for bulk density measurement. Analysis of surface acoustic wave data showed that an error in manufacture in the form of an overscan occurred. Comparing areas affected by overscan with a bulk material, a change in defect density from 1.17% in the bulk material to 5.32% in the overscan regions was observed, highlighting the need to reduce overscan areas in manufacture.

Due to its economical production process polysilicon, or multicrystalline silicon, is widely used to produce solar cell wafers. However, the conversion efficiencies are often lower than equivalent monocrystalline or thin film cells, with the structure and orientation of the silicon grains strongly linked to the efficiency. We present a non-destructive laser ultrasonic inspection technique, capable of characterising large (52 x 76 mm2) photocell's microstructure – measurement times, sample surface preparation and system upgrades for silicon scanning are discussed. This system, known as spatially resolved acoustic spectroscopy (SRAS) could be used to optimise the polysilicon wafer production process and potentially improve efficiency.

Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Measuring the grain structure of aerospace materials is very important to understand their mechanical properties and in-service performance. Spatially resolved acoustic spectroscopy is an acoustic technique utilizing surface acoustic waves to map the grain structure of a material. When combined with measurements in multiple acoustic propagation directions, the grain orientation can be obtained by fitting the velocity surface to a model. The new instrument presented here can take thousands of acoustic velocity measurements per second. The spatial and velocity resolution can be adjusted by simple modification to the system; this is discussed in detail by comparison of theoretical expectations with experimental data.

Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Developments in additive manufacturing technology are serving to expand the potential applications. Critical developments are required in the supporting areas of measurement and in process inspection to achieve this. CM247LC is a nickel superalloy that is of interest for use in aerospace and civil power plants. However, it is difficult to process via selective laser melting (SLM) as it suffers from cracking during rapid cooling and solidification. This limits the viability of CM247LC parts created using SLM. To quantify part integrity, spatially resolved acoustic spectroscopy (SRAS) has been identified as a viable non-destructive evaluation technique. In this study, a combination of optical microscopy and SRAS was used to identify and classify the surface defects present in SLM-produced parts. By analysing the datasets and scan trajectories, it is possible to correlate morphological information with process parameters. Image processing was used to quantify porosity and cracking for bulk density measurement. Analysis of surface acoustic wave data showed that an error in manufacture in the form of an overscan occurred. Comparing areas affected by overscan with a bulk material, a change in defect density from 1.17% in the bulk material to 5.32% in the overscan regions was observed, highlighting the need to reduce overscan areas in manufacture.

Due to its economical production process polysilicon, or multicrystalline silicon, is widely used to produce solar cell wafers. However, the conversion efficiencies are often lower than equivalent monocrystalline or thin film cells, with the structure and orientation of the silicon grains strongly linked to the efficiency. We present a non-destructive laser ultrasonic inspection technique, capable of characterising large (52 x 76 mm2) photocell's microstructure – measurement times, sample surface preparation and system upgrades for silicon scanning are discussed. This system, known as spatially resolved acoustic spectroscopy (SRAS) could be used to optimise the polysilicon wafer production process and potentially improve efficiency.

Measuring the grain structure of aerospace materials is very important to understand their mechanical properties and in-service performance. Spatially resolved acoustic spectroscopy is an acoustic technique utilizing surface acoustic waves to map the grain structure of a material. When combined with measurements in multiple acoustic propagation directions, the grain orientation can be obtained by fitting the velocity surface to a model. The new instrument presented here can take thousands of acoustic velocity measurements per second. The spatial and velocity resolution can be adjusted by simple modification to the system; this is discussed in detail by comparison of theoretical expectations with experimental data.

Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Developments in additive manufacturing technology are serving to expand the potential applications. Critical developments are required in the supporting areas of measurement and in process inspection to achieve this. CM247LC is a nickel superalloy that is of interest for use in aerospace and civil power plants. However, it is difficult to process via selective laser melting (SLM) as it suffers from cracking during rapid cooling and solidification. This limits the viability of CM247LC parts created using SLM. To quantify part integrity, spatially resolved acoustic spectroscopy (SRAS) has been identified as a viable non-destructive evaluation technique. In this study, a combination of optical microscopy and SRAS was used to identify and classify the surface defects present in SLM-produced parts. By analysing the datasets and scan trajectories, it is possible to correlate morphological information with process parameters. Image processing was used to quantify porosity and cracking for bulk density measurement. Analysis of surface acoustic wave data showed that an error in manufacture in the form of an overscan occurred. Comparing areas affected by overscan with a bulk material, a change in defect density from 1.17% in the bulk material to 5.32% in the overscan regions was observed, highlighting the need to reduce overscan areas in manufacture.

Due to its economical production process polysilicon, or multicrystalline silicon, is widely used to produce solar cell wafers. However, the conversion efficiencies are often lower than equivalent monocrystalline or thin film cells, with the structure and orientation of the silicon grains strongly linked to the efficiency. We present a non-destructive laser ultrasonic inspection technique, capable of characterising large (52 x 76 mm2) photocell's microstructure – measurement times, sample surface preparation and system upgrades for silicon scanning are discussed. This system, known as spatially resolved acoustic spectroscopy (SRAS) could be used to optimise the polysilicon wafer production process and potentially improve efficiency.

Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.

A major factor limiting the adoption of powder bed fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacture are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Developments in additive manufacturing technology are serving to expand the potential applications. Critical developments are required in the supporting areas of measurement and in process inspection to achieve this. CM247LC is a nickel superalloy that is of interest for use in aerospace and civil power plants. However, it is difficult to process via selective laser melting (SLM) as it suffers from cracking during rapid cooling and solidification. This limits the viability of CM247LC parts created using SLM. To quantify part integrity, spatially resolved acoustic spectroscopy (SRAS) has been identified as a viable non-destructive evaluation technique. In this study, a combination of optical microscopy and SRAS was used to identify and classify the surface defects present in SLM-produced parts. By analysing the datasets and scan trajectories, it is possible to correlate morphological information with process parameters. Image processing was used to quantify porosity and cracking for bulk density measurement. Analysis of surface acoustic wave data showed that an error in manufacture in the form of an overscan occurred. Comparing areas affected by overscan with a bulk material, a change in defect density from 1.17% in the bulk material to 5.32% in the overscan regions was observed, highlighting the need to reduce overscan areas in manufacture.